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2025-01-02T23:09:43.423000 |
VideoRefer Suite: Advancing Spatial-Temporal Object Understanding with Video LLM
| 2 |
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] |
2501.00599
|
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] | 2024-12-31T18:56:46 |
VideoRefer Suite: Advancing Spatial-Temporal Object Understanding with
Video LLM
|
Video Large Language Models (Video LLMs) have recently exhibited remarkable
capabilities in general video understanding. However, they mainly focus on
holistic comprehension and struggle with capturing fine-grained spatial and
temporal details. Besides, the lack of high-quality object-level video
instruction data and a comprehensive benchmark further hinders their
advancements. To tackle these challenges, we introduce the VideoRefer Suite to
empower Video LLM for finer-level spatial-temporal video understanding, i.e.,
enabling perception and reasoning on any objects throughout the video.
Specially, we thoroughly develop VideoRefer Suite across three essential
aspects: dataset, model, and benchmark. Firstly, we introduce a multi-agent
data engine to meticulously curate a large-scale, high-quality object-level
video instruction dataset, termed VideoRefer-700K. Next, we present the
VideoRefer model, which equips a versatile spatial-temporal object encoder to
capture precise regional and sequential representations. Finally, we
meticulously create a VideoRefer-Bench to comprehensively assess the
spatial-temporal understanding capability of a Video LLM, evaluating it across
various aspects. Extensive experiments and analyses demonstrate that our
VideoRefer model not only achieves promising performance on video referring
benchmarks but also facilitates general video understanding capabilities.
| 41 |
677761283c2cb54a3ac79251
| null | null |
|
2025-01-02T22:59:12.319000 |
Dynamic Scaling of Unit Tests for Code Reward Modeling
| 2 |
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2501.01054
|
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"name": "Jie Tang",
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] | 2025-01-02T04:33:31 |
Dynamic Scaling of Unit Tests for Code Reward Modeling
|
Current large language models (LLMs) often struggle to produce accurate
responses on the first attempt for complex reasoning tasks like code
generation. Prior research tackles this challenge by generating multiple
candidate solutions and validating them with LLM-generated unit tests. The
execution results of unit tests serve as reward signals to identify correct
solutions. As LLMs always confidently make mistakes, these unit tests are not
reliable, thereby diminishing the quality of reward signals. Motivated by the
observation that scaling the number of solutions improves LLM performance, we
explore the impact of scaling unit tests to enhance reward signal quality. Our
pioneer experiment reveals a positive correlation between the number of unit
tests and reward signal quality, with greater benefits observed in more
challenging problems. Based on these insights, we propose CodeRM-8B, a
lightweight yet effective unit test generator that enables efficient and
high-quality unit test scaling. Additionally, we implement a dynamic scaling
mechanism that adapts the number of unit tests based on problem difficulty,
further improving efficiency. Experimental results show that our approach
significantly improves performance across various models on three benchmarks
(e.g., with gains of 18.43% for Llama3-8B and 3.42% for GPT-4o-mini on
HumanEval Plus).
| 17 |
67774afb5c6bccc41be7ba62
| null | null |
|
2025-01-02T22:41:28.381000 |
VideoAnydoor: High-fidelity Video Object Insertion with Precise Motion Control
| 3 |
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| true | null |
2501.01427
|
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] | 2025-01-02T18:59:54 |
VideoAnydoor: High-fidelity Video Object Insertion with Precise Motion
Control
|
Despite significant advancements in video generation, inserting a given
object into videos remains a challenging task. The difficulty lies in
preserving the appearance details of the reference object and accurately
modeling coherent motions at the same time. In this paper, we propose
VideoAnydoor, a zero-shot video object insertion framework with high-fidelity
detail preservation and precise motion control. Starting from a text-to-video
model, we utilize an ID extractor to inject the global identity and leverage a
box sequence to control the overall motion. To preserve the detailed appearance
and meanwhile support fine-grained motion control, we design a pixel warper. It
takes the reference image with arbitrary key-points and the corresponding
key-point trajectories as inputs. It warps the pixel details according to the
trajectories and fuses the warped features with the diffusion U-Net, thus
improving detail preservation and supporting users in manipulating the motion
trajectories. In addition, we propose a training strategy involving both videos
and static images with a reweight reconstruction loss to enhance insertion
quality. VideoAnydoor demonstrates significant superiority over existing
methods and naturally supports various downstream applications (e.g., talking
head generation, video virtual try-on, multi-region editing) without
task-specific fine-tuning.
| 51 |
6777523c1ab3b3341103325a
| null | null |
|
2025-01-02T22:28:45.077000 |
A3: Android Agent Arena for Mobile GUI Agents
| 3 |
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2501.01149
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] | 2025-01-02T09:03:56 |
A3: Android Agent Arena for Mobile GUI Agents
|
AI agents have become increasingly prevalent in recent years, driven by
significant advancements in the field of large language models (LLMs). Mobile
GUI agents, a subset of AI agents, are designed to autonomously perform tasks
on mobile devices. While numerous studies have introduced agents, datasets, and
benchmarks to advance mobile GUI agent research, many existing datasets focus
on static frame evaluations and fail to provide a comprehensive platform for
assessing performance on real-world, in-the-wild tasks. To address this gap, we
present Android Agent Arena (A3), a novel evaluation platform. Unlike existing
in-the-wild systems, A3 offers: (1) meaningful and practical tasks, such as
real-time online information retrieval and operational instructions; (2) a
larger, more flexible action space, enabling compatibility with agents trained
on any dataset; and (3) automated business-level LLM-based evaluation process.
A3 includes 21 widely used general third-party apps and 201 tasks
representative of common user scenarios, providing a robust foundation for
evaluating mobile GUI agents in real-world situations and a new autonomous
evaluation process for less human labor and coding expertise. The project is
available at https://yuxiangchai.github.io/Android-Agent-Arena/.
| 22 |
677758817fcef9e7b225cf0a
| null | null |
|
2025-01-02T22:27:32.841000 |
MLLM-as-a-Judge for Image Safety without Human Labeling
| 2 |
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| true | null |
2501.00192
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] | 2024-12-31T00:06:04 |
MLLM-as-a-Judge for Image Safety without Human Labeling
|
Image content safety has become a significant challenge with the rise of
visual media on online platforms. Meanwhile, in the age of AI-generated content
(AIGC), many image generation models are capable of producing harmful content,
such as images containing sexual or violent material. Thus, it becomes crucial
to identify such unsafe images based on established safety rules. Pre-trained
Multimodal Large Language Models (MLLMs) offer potential in this regard, given
their strong pattern recognition abilities. Existing approaches typically
fine-tune MLLMs with human-labeled datasets, which however brings a series of
drawbacks. First, relying on human annotators to label data following intricate
and detailed guidelines is both expensive and labor-intensive. Furthermore,
users of safety judgment systems may need to frequently update safety rules,
making fine-tuning on human-based annotation more challenging. This raises the
research question: Can we detect unsafe images by querying MLLMs in a zero-shot
setting using a predefined safety constitution (a set of safety rules)? Our
research showed that simply querying pre-trained MLLMs does not yield
satisfactory results. This lack of effectiveness stems from factors such as the
subjectivity of safety rules, the complexity of lengthy constitutions, and the
inherent biases in the models. To address these challenges, we propose a
MLLM-based method includes objectifying safety rules, assessing the relevance
between rules and images, making quick judgments based on debiased token
probabilities with logically complete yet simplified precondition chains for
safety rules, and conducting more in-depth reasoning with cascaded
chain-of-thought processes if necessary. Experiment results demonstrate that
our method is highly effective for zero-shot image safety judgment tasks.
| 25 |
6777591238f9a731d4f5762f
| null | null |
|
2025-01-02T22:04:45.023000 |
Reconstruction vs. Generation: Taming Optimization Dilemma in Latent Diffusion Models
| 2 |
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2501.01423
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] | 2025-01-02T18:59:40 |
Reconstruction vs. Generation: Taming Optimization Dilemma in Latent
Diffusion Models
|
Latent diffusion models with Transformer architectures excel at generating
high-fidelity images. However, recent studies reveal an optimization dilemma in
this two-stage design: while increasing the per-token feature dimension in
visual tokenizers improves reconstruction quality, it requires substantially
larger diffusion models and more training iterations to achieve comparable
generation performance. Consequently, existing systems often settle for
sub-optimal solutions, either producing visual artifacts due to information
loss within tokenizers or failing to converge fully due to expensive
computation costs. We argue that this dilemma stems from the inherent
difficulty in learning unconstrained high-dimensional latent spaces. To address
this, we propose aligning the latent space with pre-trained vision foundation
models when training the visual tokenizers. Our proposed VA-VAE (Vision
foundation model Aligned Variational AutoEncoder) significantly expands the
reconstruction-generation frontier of latent diffusion models, enabling faster
convergence of Diffusion Transformers (DiT) in high-dimensional latent spaces.
To exploit the full potential of VA-VAE, we build an enhanced DiT baseline with
improved training strategies and architecture designs, termed LightningDiT. The
integrated system achieves state-of-the-art (SOTA) performance on ImageNet
256x256 generation with an FID score of 1.35 while demonstrating remarkable
training efficiency by reaching an FID score of 2.11 in just 64
epochs--representing an over 21 times convergence speedup compared to the
original DiT. Models and codes are available at:
https://github.com/hustvl/LightningDiT.
| 37 |
677753a38376dfe003a3fc2b
| null | null |
|
2025-01-02T21:58:41.673000 |
ProgCo: Program Helps Self-Correction of Large Language Models
| 2 |
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2501.01264
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},
{
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"hidden": false,
"name": "Bo Zheng",
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}
] | 2025-01-02T13:59:20 |
ProgCo: Program Helps Self-Correction of Large Language Models
|
Self-Correction aims to enable large language models (LLMs) to self-verify
and self-refine their initial responses without external feedback. However,
LLMs often fail to effectively self-verify and generate correct feedback,
further misleading refinement and leading to the failure of self-correction,
especially in complex reasoning tasks. In this paper, we propose Program-driven
Self-Correction (ProgCo). First, program-driven verification (ProgVe) achieves
complex verification logic and extensive validation through self-generated,
self-executing verification pseudo-programs. Then, program-driven refinement
(ProgRe) receives feedback from ProgVe, conducts dual reflection and refinement
on both responses and verification programs to mitigate misleading of incorrect
feedback in complex reasoning tasks. Experiments on three instruction-following
and mathematical benchmarks indicate that ProgCo achieves effective
self-correction, and can be further enhance performance when combined with real
program tools.
| 25 |
677751f33308d0c478a26b14
| null | null |
|
2025-01-02T20:53:29.611000 |
Are Vision-Language Models Truly Understanding Multi-vision Sensor?
| 2 |
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2412.20750
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"_id": "67742e073adba6b02dd84e41",
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},
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},
{
"_id": "67742e073adba6b02dd84e43",
"hidden": false,
"name": "Yong Man Ro",
"status": null,
"statusLastChangedAt": null,
"user": null
}
] | 2024-12-30T06:44:25 |
Are Vision-Language Models Truly Understanding Multi-vision Sensor?
|
Large-scale Vision-Language Models (VLMs) have advanced by aligning vision
inputs with text, significantly improving performance in computer vision tasks.
Moreover, for VLMs to be effectively utilized in real-world applications, an
understanding of diverse multi-vision sensor data, such as thermal, depth, and
X-ray information, is essential. However, we find that current VLMs process
multi-vision sensor images without deep understanding of sensor information,
disregarding each sensor's unique physical properties. This limitation
restricts their capacity to interpret and respond to complex questions
requiring multi-vision sensor reasoning. To address this, we propose a novel
Multi-vision Sensor Perception and Reasoning (MS-PR) benchmark, assessing VLMs
on their capacity for sensor-specific reasoning. Moreover, we introduce Diverse
Negative Attributes (DNA) optimization to enable VLMs to perform deep reasoning
on multi-vision sensor tasks, helping to bridge the core information gap
between images and sensor data. Extensive experimental results validate that
the proposed DNA method can significantly improve the multi-vision sensor
reasoning for VLMs.
| 20 |
67742e083adba6b02dd84e8b
| null | null |
|
2025-01-02T18:48:09.282000 |
VMix: Improving Text-to-Image Diffusion Model with Cross-Attention Mixing Control
| 2 |
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2412.20800
|
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"hidden": false,
"name": "Fei Ding",
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},
{
"_id": "677365f3ed53dd3b007d7a99",
"hidden": false,
"name": "Mengqi Huang",
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},
{
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"hidden": false,
"name": "Wei Liu",
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},
{
"_id": "677365f3ed53dd3b007d7a9b",
"hidden": false,
"name": "Qian He",
"status": null,
"statusLastChangedAt": null,
"user": null
}
] | 2024-12-30T08:47:25 |
VMix: Improving Text-to-Image Diffusion Model with Cross-Attention
Mixing Control
|
While diffusion models show extraordinary talents in text-to-image
generation, they may still fail to generate highly aesthetic images. More
specifically, there is still a gap between the generated images and the
real-world aesthetic images in finer-grained dimensions including color,
lighting, composition, etc. In this paper, we propose Cross-Attention Value
Mixing Control (VMix) Adapter, a plug-and-play aesthetics adapter, to upgrade
the quality of generated images while maintaining generality across visual
concepts by (1) disentangling the input text prompt into the content
description and aesthetic description by the initialization of aesthetic
embedding, and (2) integrating aesthetic conditions into the denoising process
through value-mixed cross-attention, with the network connected by
zero-initialized linear layers. Our key insight is to enhance the aesthetic
presentation of existing diffusion models by designing a superior condition
control method, all while preserving the image-text alignment. Through our
meticulous design, VMix is flexible enough to be applied to community models
for better visual performance without retraining. To validate the effectiveness
of our method, we conducted extensive experiments, showing that VMix
outperforms other state-of-the-art methods and is compatible with other
community modules (e.g., LoRA, ControlNet, and IPAdapter) for image generation.
The project page is https://vmix-diffusion.github.io/VMix/.
| 10 |
677365f6ed53dd3b007d7b13
| null | null |
|
2025-01-02T11:26:22.995000 |
HUNYUANPROVER: A Scalable Data Synthesis Framework and Guided Tree Search for Automated Theorem Proving
| 2 |
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| false | null |
2412.20735
|
[
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"name": "Yang Li",
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},
{
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"name": "Dong Du",
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},
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},
{
"_id": "6776bdde4f9262d263382cf6",
"hidden": false,
"name": "Chen Li",
"status": null,
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},
{
"_id": "6776bdde4f9262d263382cf7",
"hidden": false,
"name": "Weikang Wang",
"status": null,
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},
{
"_id": "6776bdde4f9262d263382cf8",
"hidden": false,
"name": "Tao Yang",
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},
{
"_id": "6776bdde4f9262d263382cf9",
"hidden": false,
"name": "Haitao Mi",
"status": null,
"statusLastChangedAt": null,
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}
] | 2024-12-30T06:18:33 |
HUNYUANPROVER: A Scalable Data Synthesis Framework and Guided Tree
Search for Automated Theorem Proving
|
We introduce HunyuanProver, an language model finetuned from the Hunyuan 7B
for interactive automatic theorem proving with LEAN4. To alleviate the data
sparsity issue, we design a scalable framework to iterative synthesize data
with low cost. Besides, guided tree search algorithms are designed to enable
effective ``system 2 thinking`` of the prover. HunyuanProver achieves
state-of-the-art (SOTA) performances on major benchmarks. Specifically, it
achieves a pass of 68.4% on the miniF2F-test compared to 65.9%, the current
SOTA results. It proves 4 IMO statements (imo_1960_p2, imo_1962_p2},
imo_1964_p2 and imo_1983_p6) in miniF2F-test. To benefit the community, we will
open-source a dataset of 30k synthesized instances, where each instance
contains the original question in natural language, the converted statement by
autoformalization, and the proof by HunyuanProver.
| 11 |
6776bddf4f9262d263382d20
| null | null |
|
2025-01-02T02:22:25.585000 |
OS-Genesis: Automating GUI Agent Trajectory Construction via Reverse Task Synthesis
| 3 |
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] |
2412.19723
|
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{
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{
"_id": "67720d79b3163a95a653babb",
"hidden": false,
"name": "Junxian He",
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{
"_id": "67720d79b3163a95a653babc",
"hidden": false,
"name": "Yu Qiao",
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},
{
"_id": "67720d79b3163a95a653babd",
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"name": "Zhiyong Wu",
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}
] | 2024-12-27T16:21:58 |
OS-Genesis: Automating GUI Agent Trajectory Construction via Reverse
Task Synthesis
|
Graphical User Interface (GUI) agents powered by Vision-Language Models
(VLMs) have demonstrated human-like computer control capability. Despite their
utility in advancing digital automation, a critical bottleneck persists:
collecting high-quality trajectory data for training. Common practices for
collecting such data rely on human supervision or synthetic data generation
through executing pre-defined tasks, which are either resource-intensive or
unable to guarantee data quality. Moreover, these methods suffer from limited
data diversity and significant gaps between synthetic data and real-world
environments. To address these challenges, we propose OS-Genesis, a novel GUI
data synthesis pipeline that reverses the conventional trajectory collection
process. Instead of relying on pre-defined tasks, OS-Genesis enables agents
first to perceive environments and perform step-wise interactions, then
retrospectively derive high-quality tasks to enable trajectory-level
exploration. A trajectory reward model is then employed to ensure the quality
of the generated trajectories. We demonstrate that training GUI agents with
OS-Genesis significantly improves their performance on highly challenging
online benchmarks. In-depth analysis further validates OS-Genesis's efficiency
and its superior data quality and diversity compared to existing synthesis
methods. Our codes, data, and checkpoints are available at
https://qiushisun.github.io/OS-Genesis-Home/{OS-Genesis Homepage}.
| 82 |
67720d7bb3163a95a653bb18
|
https://qiushisun.github.io/OS-Genesis-Home/
|
https://github.com/OS-Copilot/OS-Genesis
|
|
2025-01-02T00:31:04.921000 |
Xmodel-2 Technical Report
| 4 |
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"isPro": false,
"name": "valeriaWong",
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}
| true | null |
2412.19638
|
[
{
"_id": "677223b635722632fcc63ff5",
"hidden": false,
"name": "Wang Qun",
"status": "claimed_verified",
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},
{
"_id": "677223b635722632fcc63ff6",
"hidden": false,
"name": "Liu Yang",
"status": null,
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},
{
"_id": "677223b635722632fcc63ff7",
"hidden": false,
"name": "Lin Qingquan",
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},
{
"_id": "677223b635722632fcc63ff8",
"hidden": false,
"name": "Qu Zhijiu",
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},
{
"_id": "677223b635722632fcc63ff9",
"hidden": false,
"name": "Jiang Ling",
"status": null,
"statusLastChangedAt": null,
"user": null
}
] | 2024-12-27T13:32:10 |
Xmodel-2 Technical Report
|
Xmodel-2 is a 1.2-billion-parameter large language model designed
specifically for reasoning tasks. Its architecture enables different model
scales to share a unified set of hyperparameters, allowing for extensive
experimentation on smaller models and seamless transfer of optimal
configurations to larger models. To maximize training efficiency and stability,
Xmodel-2 employs the WSD learning rate scheduler from MiniCPM. Pretrained on
1.5 trillion tokens from diverse sources, Xmodel-2 achieves state-of-the-art
performance in complex reasoning and agent-based tasks, while maintaining low
training costs. These results highlight the potential of efficient model design
and training strategies in advancing reasoning capabilities. Model checkpoints
and code are publicly available on GitHub at
https://github.com/XiaoduoAILab/Xmodel-2
| 26 |
677223b735722632fcc64063
| null | null |
|
2024-12-31T08:54:02.196000 |
Do NOT Think That Much for 2+3=? On the Overthinking of o1-Like LLMs
| 2 |
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2412.21187
|
[
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{
"_id": "6773f75a23a7829936cb36bf",
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"name": "Tian Liang",
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"name": "Dian Yu",
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},
{
"_id": "6773f75a23a7829936cb36c3",
"hidden": false,
"name": "Linfeng Song",
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},
{
"_id": "6773f75a23a7829936cb36c4",
"hidden": false,
"name": "Qiuzhi Liu",
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{
"_id": "6773f75a23a7829936cb36c5",
"hidden": false,
"name": "Mengfei Zhou",
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},
{
"_id": "6773f75a23a7829936cb36c6",
"hidden": false,
"name": "Zhuosheng Zhang",
"status": null,
"statusLastChangedAt": null,
"user": null
},
{
"_id": "6773f75a23a7829936cb36c7",
"hidden": false,
"name": "Rui Wang",
"status": null,
"statusLastChangedAt": null,
"user": null
},
{
"_id": "6773f75a23a7829936cb36c8",
"hidden": false,
"name": "Zhaopeng Tu",
"status": null,
"statusLastChangedAt": null,
"user": null
},
{
"_id": "6773f75a23a7829936cb36c9",
"hidden": false,
"name": "Haitao Mi",
"status": null,
"statusLastChangedAt": null,
"user": null
},
{
"_id": "6773f75a23a7829936cb36ca",
"hidden": false,
"name": "Dong Yu",
"status": null,
"statusLastChangedAt": null,
"user": null
}
] | 2024-12-30T18:55:12 |
Do NOT Think That Much for 2+3=? On the Overthinking of o1-Like LLMs
|
The remarkable performance of models like the OpenAI o1 can be attributed to
their ability to emulate human-like long-time thinking during inference. These
models employ extended chain-of-thought (CoT) processes, exploring multiple
strategies to enhance problem-solving capabilities. However, a critical
question remains: How to intelligently and efficiently scale computational
resources during testing. This paper presents the first comprehensive study on
the prevalent issue of overthinking in these models, where excessive
computational resources are allocated for simple problems with minimal benefit.
We introduce novel efficiency metrics from both outcome and process
perspectives to evaluate the rational use of computational resources by o1-like
models. Using a self-training paradigm, we propose strategies to mitigate
overthinking, streamlining reasoning processes without compromising accuracy.
Experimental results show that our approach successfully reduces computational
overhead while preserving model performance across a range of testsets with
varying difficulty levels, such as GSM8K, MATH500, GPQA, and AIME.
| 40 |
6773f75b23a7829936cb3729
| null | null |
|
2024-12-31T08:45:57.560000 |
Slow Perception: Let's Perceive Geometric Figures Step-by-step
| 2 |
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| false | null |
2412.20631
|
[
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{
"_id": "6773f578fb77c86d80d4c9fe",
"hidden": false,
"name": "Yumeng Li",
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},
{
"_id": "6773f578fb77c86d80d4c9ff",
"hidden": false,
"name": "Jia Wang",
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"statusLastChangedAt": null,
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},
{
"_id": "6773f578fb77c86d80d4ca00",
"hidden": false,
"name": "Liang Zhao",
"status": null,
"statusLastChangedAt": null,
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},
{
"_id": "6773f578fb77c86d80d4ca01",
"hidden": false,
"name": "Jianjian Sun",
"status": null,
"statusLastChangedAt": null,
"user": null
},
{
"_id": "6773f578fb77c86d80d4ca02",
"hidden": false,
"name": "Zheng Ge",
"status": null,
"statusLastChangedAt": null,
"user": null
},
{
"_id": "6773f578fb77c86d80d4ca03",
"hidden": false,
"name": "Xiangyu Zhang",
"status": null,
"statusLastChangedAt": null,
"user": null
}
] | 2024-12-30T00:40:35 |
Slow Perception: Let's Perceive Geometric Figures Step-by-step
|
Recently, "visual o1" began to enter people's vision, with expectations that
this slow-thinking design can solve visual reasoning tasks, especially
geometric math problems. However, the reality is that current LVLMs (Large
Vision Language Models) can hardly even accurately copy a geometric figure, let
alone truly understand the complex inherent logic and spatial relationships
within geometric shapes. We believe accurate copying (strong perception) is the
first step to visual o1. Accordingly, we introduce the concept of "slow
perception" (SP), which guides the model to gradually perceive basic point-line
combinations, as our humans, reconstruct complex geometric structures
progressively. There are two-fold stages in SP: a) perception decomposition.
Perception is not instantaneous. In this stage, complex geometric figures are
broken down into basic simple units to unify geometry representation. b)
perception flow, which acknowledges that accurately tracing a line is not an
easy task. This stage aims to avoid "long visual jumps" in regressing line
segments by using a proposed "perceptual ruler" to trace each line
stroke-by-stroke. Surprisingly, such a human-like perception manner enjoys an
inference time scaling law -- the slower, the better. Researchers strive to
speed up the model's perception in the past, but we slow it down again,
allowing the model to read the image step-by-step and carefully.
| 15 |
6773f579fb77c86d80d4ca4d
| null | null |
|
2024-12-31T08:39:13.913000 |
PERSE: Personalized 3D Generative Avatars from A Single Portrait
| 3 |
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[
"https://cdn-uploads.huggingface.co/production/uploads/62414e2b585605a4079c2f38/fvMRw70bYO-xBRQTYBFOO.mp4"
] |
2412.21206
|
[
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},
{
"_id": "67735ba473932c3aa94fe0ad",
"hidden": false,
"name": "Inhee Lee",
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},
{
"_id": "67735ba473932c3aa94fe0ae",
"hidden": false,
"name": "Hanbyul Joo",
"status": null,
"statusLastChangedAt": null,
"user": null
}
] | 2024-12-30T18:59:58 |
PERSE: Personalized 3D Generative Avatars from A Single Portrait
|
We present PERSE, a method for building an animatable personalized generative
avatar from a reference portrait. Our avatar model enables facial attribute
editing in a continuous and disentangled latent space to control each facial
attribute, while preserving the individual's identity. To achieve this, our
method begins by synthesizing large-scale synthetic 2D video datasets, where
each video contains consistent changes in the facial expression and viewpoint,
combined with a variation in a specific facial attribute from the original
input. We propose a novel pipeline to produce high-quality, photorealistic 2D
videos with facial attribute editing. Leveraging this synthetic attribute
dataset, we present a personalized avatar creation method based on the 3D
Gaussian Splatting, learning a continuous and disentangled latent space for
intuitive facial attribute manipulation. To enforce smooth transitions in this
latent space, we introduce a latent space regularization technique by using
interpolated 2D faces as supervision. Compared to previous approaches, we
demonstrate that PERSE generates high-quality avatars with interpolated
attributes while preserving identity of reference person.
| 18 |
67735ba873932c3aa94fe15b
| null | null |
|
2024-12-31T05:22:56.879000 |
Facilitating large language model Russian adaptation with Learned Embedding Propagation
| 2 |
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| true | null |
2412.21140
|
[
{
"_id": "6773c24cb272a4f186ec613c",
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"name": "Mikhail Tikhomirov",
"status": "extracted_pending",
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{
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"hidden": false,
"name": "Daniil Chernyshev",
"status": null,
"statusLastChangedAt": null,
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}
] | 2024-12-30T18:15:45 |
Facilitating large language model Russian adaptation with Learned
Embedding Propagation
|
Rapid advancements of large language model (LLM) technologies led to the
introduction of powerful open-source instruction-tuned LLMs that have the same
text generation quality as the state-of-the-art counterparts such as GPT-4.
While the emergence of such models accelerates the adoption of LLM technologies
in sensitive-information environments the authors of such models don not
disclose the training data necessary for replication of the results thus making
the achievements model-exclusive. Since those open-source models are also
multilingual this in turn reduces the benefits of training a language specific
LLMs as improved inference computation efficiency becomes the only guaranteed
advantage of such costly procedure. More cost-efficient options such as
vocabulary extension and subsequent continued pre-training are also inhibited
by the lack of access to high-quality instruction-tuning data since it is the
major factor behind the resulting LLM task-solving capabilities. To address the
limitations and cut the costs of the language adaptation pipeline we propose
Learned Embedding Propagation (LEP). Unlike existing approaches our method has
lower training data size requirements due to minimal impact on existing LLM
knowledge which we reinforce using novel ad-hoc embedding propagation procedure
that allows to skip the instruction-tuning step and instead implant the new
language knowledge directly into any existing instruct-tuned variant. We
evaluated four Russian vocabulary adaptations for LLaMa-3-8B and Mistral-7B,
showing that LEP is competitive with traditional instruction-tuning methods,
achieving performance comparable to OpenChat 3.5 and LLaMa-3-8B-Instruct, with
further improvements via self-calibration and continued tuning enhancing
task-solving capabilities.
| 18 |
6773c24db272a4f186ec6190
| null | null |
|
2024-12-31T01:26:05.226000 |
HumanEval Pro and MBPP Pro: Evaluating Large Language Models on Self-invoking Code Generation
| 3 |
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| true | null |
2412.21199
|
[
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{
"_id": "67738e22b8df35b9c86ace87",
"hidden": false,
"name": "Yilun Zhao",
"status": "claimed_verified",
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},
{
"_id": "67738e22b8df35b9c86ace88",
"hidden": false,
"name": "Arman Cohan",
"status": null,
"statusLastChangedAt": null,
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},
{
"_id": "67738e22b8df35b9c86ace89",
"hidden": false,
"name": "Xiao-Ping Zhang",
"status": null,
"statusLastChangedAt": null,
"user": null
}
] | 2024-12-30T18:58:58 |
HumanEval Pro and MBPP Pro: Evaluating Large Language Models on
Self-invoking Code Generation
|
We introduce self-invoking code generation, a new task designed to evaluate
the progressive reasoning and problem-solving capabilities of LLMs. In this
task, models are presented with a base problem and a related, more complex
problem. They must solve the base problem and then utilize its solution to
address the more complex one. This work features three key contributions.
First, we propose a general recipe for generating more challenging versions of
existing benchmarks, resulting in three new benchmarks: HumanEval Pro, MBPP
Pro, and BigCodeBench-Lite Pro, specifically designed to assess LLMs on
self-invoking code generation. Second, from the analysis of experimental
results over twenty LLMs on our benchmarks, we have two important observations:
(i) Most LLMs excel in traditional code generation benchmarks like HumanEval
and MBPP, but their performance declines on self-invoking tasks. For example,
o1-mini achieves 96.2% pass@1 on HumanEval but only 76.2% on HumanEval Pro.
(ii) On self-invoking code generation task, the instruction-tuned models
demonstrate only marginal improvements compared to the base models. Third, we
disclose the types of failure modes that exist in our evaluation results. All
these results underscore the need for further advancements in self-invoking
code generation tasks and provide a new direction for future research on
enhancing LLMs' code reasoning capabilities.
| 14 |
67738e24b8df35b9c86aced8
| null | null |
|
2024-12-31T01:20:42.246000 |
Bringing Objects to Life: 4D generation from 3D objects
| 2 |
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"name": "ohad204",
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| true |
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] |
2412.20422
|
[
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"type": "user",
"user": "ohad204"
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},
{
"_id": "67738c503be2064a656e7e71",
"hidden": false,
"name": "Ori Malca",
"status": "claimed_verified",
"statusLastChangedAt": "2025-01-01T20:12:41.929Z",
"user": {
"_id": "643ee7c8947f8be48fcb02c0",
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"isPro": false,
"type": "user",
"user": "Orimalca"
}
},
{
"_id": "67738c503be2064a656e7e72",
"hidden": false,
"name": "Dvir Samuel",
"status": null,
"statusLastChangedAt": null,
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},
{
"_id": "67738c503be2064a656e7e73",
"hidden": false,
"name": "Gal Chechik",
"status": null,
"statusLastChangedAt": null,
"user": null
}
] | 2024-12-29T10:12:01 |
Bringing Objects to Life: 4D generation from 3D objects
|
Recent advancements in generative modeling now enable the creation of 4D
content (moving 3D objects) controlled with text prompts. 4D generation has
large potential in applications like virtual worlds, media, and gaming, but
existing methods provide limited control over the appearance and geometry of
generated content. In this work, we introduce a method for animating
user-provided 3D objects by conditioning on textual prompts to guide 4D
generation, enabling custom animations while maintaining the identity of the
original object. We first convert a 3D mesh into a ``static" 4D Neural Radiance
Field (NeRF) that preserves the visual attributes of the input object. Then, we
animate the object using an Image-to-Video diffusion model driven by text. To
improve motion realism, we introduce an incremental viewpoint selection
protocol for sampling perspectives to promote lifelike movement and a masked
Score Distillation Sampling (SDS) loss, which leverages attention maps to focus
optimization on relevant regions. We evaluate our model in terms of temporal
coherence, prompt adherence, and visual fidelity and find that our method
outperforms baselines that are based on other approaches, achieving up to
threefold improvements in identity preservation measured using LPIPS scores,
and effectively balancing visual quality with dynamic content.
| 36 |
67738c513be2064a656e7ebd
| null | null |
|
2024-12-30T23:56:15.734000 |
Training Software Engineering Agents and Verifiers with SWE-Gym
| 2 |
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"fullname": "Jiayi Pan",
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"isMod": false,
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"name": "Jiayi-Pan",
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}
| true |
[
"https://cdn-uploads.huggingface.co/production/uploads/61568f37272f2d87a99ba884/lscibgXrTDtXtAzDwsbCF.png"
] |
2412.21139
|
[
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{
"_id": "67735961702f3c89046839f6",
"hidden": false,
"name": "Graham Neubig",
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},
{
"_id": "67735961702f3c89046839f7",
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"name": "Navdeep Jaitly",
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},
{
"_id": "67735961702f3c89046839f8",
"hidden": false,
"name": "Heng Ji",
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},
{
"_id": "67735961702f3c89046839f9",
"hidden": false,
"name": "Alane Suhr",
"status": "extracted_confirmed",
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{
"_id": "67735961702f3c89046839fa",
"hidden": false,
"name": "Yizhe Zhang",
"status": null,
"statusLastChangedAt": null,
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}
] | 2024-12-30T18:15:39 |
Training Software Engineering Agents and Verifiers with SWE-Gym
|
We present SWE-Gym, the first environment for training real-world software
engineering (SWE) agents. SWE-Gym contains 2,438 real-world Python task
instances, each comprising a codebase with an executable runtime environment,
unit tests, and a task specified in natural language. We use SWE-Gym to train
language model based SWE agents , achieving up to 19% absolute gains in resolve
rate on the popular SWE-Bench Verified and Lite test sets. We also experiment
with inference-time scaling through verifiers trained on agent trajectories
sampled from SWE-Gym. When combined with our fine-tuned SWE agents, we achieve
32.0% and 26.0% on SWE-Bench Verified and Lite, respectively, reflecting a new
state-of-the-art for open-weight SWE agents. To facilitate further research, we
publicly release SWE-Gym, models, and agent trajectories.
| 22 |
67735962702f3c8904683a22
| null | null |
|
2024-12-30T23:38:03.262000 |
Explanatory Instructions: Towards Unified Vision Tasks Understanding and Zero-shot Generalization
| 2 |
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| true | null |
2412.18525
|
[
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"name": "Yuxin Song",
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{
"_id": "67721383d565d51e49e7a913",
"hidden": false,
"name": "Tao Yuan",
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},
{
"_id": "67721383d565d51e49e7a914",
"hidden": false,
"name": "Jian Jin",
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"statusLastChangedAt": null,
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},
{
"_id": "67721383d565d51e49e7a915",
"hidden": false,
"name": "Heyang Xu",
"status": null,
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},
{
"_id": "67721383d565d51e49e7a916",
"hidden": false,
"name": "Yazhou Yao",
"status": null,
"statusLastChangedAt": null,
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},
{
"_id": "67721383d565d51e49e7a917",
"hidden": false,
"name": "Errui Ding",
"status": null,
"statusLastChangedAt": null,
"user": null
}
] | 2024-12-24T16:08:25 |
Explanatory Instructions: Towards Unified Vision Tasks Understanding and
Zero-shot Generalization
|
Computer Vision (CV) has yet to fully achieve the zero-shot task
generalization observed in Natural Language Processing (NLP), despite following
many of the milestones established in NLP, such as large transformer models,
extensive pre-training, and the auto-regression paradigm, among others. In this
paper, we explore the idea that CV adopts discrete and terminological task
definitions (\eg, ``image segmentation''), which may be a key barrier to
zero-shot task generalization. Our hypothesis is that without truly
understanding previously-seen tasks--due to these terminological
definitions--deep models struggle to generalize to novel tasks. To verify this,
we introduce Explanatory Instructions, which provide an intuitive way to define
CV task objectives through detailed linguistic transformations from input
images to outputs. We create a large-scale dataset comprising 12 million
``image input to explanatory instruction to output'' triplets, and train
an auto-regressive-based vision-language model (AR-based VLM) that takes both
images and explanatory instructions as input. By learning to follow these
instructions, the AR-based VLM achieves instruction-level zero-shot
capabilities for previously-seen tasks and demonstrates strong zero-shot
generalization for unseen CV tasks. Code and dataset will be openly available
on our GitHub repository.
| 75 |
67721386d565d51e49e7a9b7
| null | null |
|
2024-12-30T23:33:22.541000 |
Efficiently Serving LLM Reasoning Programs with Certaindex
| 2 |
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2412.20993
|
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"name": "Zhongdongming Dai",
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},
{
"_id": "6773560ad86f2a7187725990",
"hidden": false,
"name": "Aurick Qiao",
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"statusLastChangedAt": null,
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},
{
"_id": "6773560ad86f2a7187725991",
"hidden": false,
"name": "Hao Zhang",
"status": "claimed_verified",
"statusLastChangedAt": "2025-01-01T20:13:29.981Z",
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] | 2024-12-30T14:57:53 |
Efficiently Serving LLM Reasoning Programs with Certaindex
|
The rapid evolution of large language models (LLMs) has unlocked their
capabilities in advanced reasoning tasks like mathematical problem-solving,
code generation, and legal analysis. Central to this progress are
inference-time reasoning algorithms, which refine outputs by exploring multiple
solution paths, at the cost of increasing compute demands and response
latencies. Existing serving systems fail to adapt to the scaling behaviors of
these algorithms or the varying difficulty of queries, leading to inefficient
resource use and unmet latency targets.
We present Dynasor, a system that optimizes inference-time compute for LLM
reasoning queries. Unlike traditional engines, Dynasor tracks and schedules
requests within reasoning queries and uses Certaindex, a proxy that measures
statistical reasoning progress based on model certainty, to guide compute
allocation dynamically. Dynasor co-adapts scheduling with reasoning progress:
it allocates more compute to hard queries, reduces compute for simpler ones,
and terminates unpromising queries early, balancing accuracy, latency, and
cost. On diverse datasets and algorithms, Dynasor reduces compute by up to 50%
in batch processing and sustaining 3.3x higher query rates or 4.7x tighter
latency SLOs in online serving.
| 36 |
6773560bd86f2a71877259f6
| null | null |
|
2024-12-30T23:03:45.561000 |
TangoFlux: Super Fast and Faithful Text to Audio Generation with Flow Matching and Clap-Ranked Preference Optimization
| 4 |
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| false | null |
2412.21037
|
[
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{
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"name": "Zhifeng Kong",
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"hidden": false,
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},
{
"_id": "67735d5d4a4e0c546461a911",
"hidden": false,
"name": "Rafael Valle",
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},
{
"_id": "67735d5d4a4e0c546461a912",
"hidden": false,
"name": "Bryan Catanzaro",
"status": null,
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},
{
"_id": "67735d5d4a4e0c546461a913",
"hidden": false,
"name": "Soujanya Poria",
"status": null,
"statusLastChangedAt": null,
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}
] | 2024-12-30T16:02:44 |
TangoFlux: Super Fast and Faithful Text to Audio Generation with Flow
Matching and Clap-Ranked Preference Optimization
|
We introduce TangoFlux, an efficient Text-to-Audio (TTA) generative model
with 515M parameters, capable of generating up to 30 seconds of 44.1kHz audio
in just 3.7 seconds on a single A40 GPU. A key challenge in aligning TTA models
lies in the difficulty of creating preference pairs, as TTA lacks structured
mechanisms like verifiable rewards or gold-standard answers available for Large
Language Models (LLMs). To address this, we propose CLAP-Ranked Preference
Optimization (CRPO), a novel framework that iteratively generates and optimizes
preference data to enhance TTA alignment. We demonstrate that the audio
preference dataset generated using CRPO outperforms existing alternatives. With
this framework, TangoFlux achieves state-of-the-art performance across both
objective and subjective benchmarks. We open source all code and models to
support further research in TTA generation.
| 24 |
67735d5e4a4e0c546461a951
| null | null |
|
2024-12-30T22:51:09.824000 |
OneKE: A Dockerized Schema-Guided LLM Agent-based Knowledge Extraction System
| 2 |
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"https://cdn-uploads.huggingface.co/production/uploads/620b3bbb0668e435407c8d0a/1spJ3Z9MTeBY4AMTJUTX4.png"
] |
2412.20005
|
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{
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"name": "Lin Yuan",
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},
{
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"name": "Mengshu Sun",
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"name": "Lei Liang",
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},
{
"_id": "677368fbe182b937d586075f",
"hidden": false,
"name": "Zhiqiang Zhang",
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},
{
"_id": "677368fbe182b937d5860760",
"hidden": false,
"name": "Jun Zhou",
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},
{
"_id": "677368fbe182b937d5860761",
"hidden": false,
"name": "Lanning Wei",
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},
{
"_id": "677368fbe182b937d5860762",
"hidden": false,
"name": "Da Zheng",
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},
{
"_id": "677368fbe182b937d5860763",
"hidden": false,
"name": "Haofen Wang",
"status": null,
"statusLastChangedAt": null,
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},
{
"_id": "677368fbe182b937d5860764",
"hidden": false,
"name": "Huajun Chen",
"status": null,
"statusLastChangedAt": null,
"user": null
}
] | 2024-12-28T04:01:30 |
OneKE: A Dockerized Schema-Guided LLM Agent-based Knowledge Extraction
System
|
We introduce OneKE, a dockerized schema-guided knowledge extraction system,
which can extract knowledge from the Web and raw PDF Books, and support various
domains (science, news, etc.). Specifically, we design OneKE with multiple
agents and a configure knowledge base. Different agents perform their
respective roles, enabling support for various extraction scenarios. The
configure knowledge base facilitates schema configuration, error case debugging
and correction, further improving the performance. Empirical evaluations on
benchmark datasets demonstrate OneKE's efficacy, while case studies further
elucidate its adaptability to diverse tasks across multiple domains,
highlighting its potential for broad applications. We have open-sourced the
Code at https://github.com/zjunlp/OneKE and released a Video at
http://oneke.openkg.cn/demo.mp4.
| 18 |
677368fce182b937d58607c6
| null | null |
|
2024-12-30T22:29:50.947000 |
On the Compositional Generalization of Multimodal LLMs for Medical Imaging
| 4 |
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| true | null |
2412.20070
|
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"name": "Weihong Wang",
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},
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"_id": "67735f9f9cc5d33bf6af3cf3",
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"name": "Yonglin Deng",
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},
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"hidden": false,
"name": "Dingjie Song",
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"hidden": false,
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},
{
"_id": "67735f9f9cc5d33bf6af3cf6",
"hidden": false,
"name": "Zixu Zhang",
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},
{
"_id": "67735f9f9cc5d33bf6af3cf7",
"hidden": false,
"name": "Benyou Wang",
"status": null,
"statusLastChangedAt": null,
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}
] | 2024-12-28T07:50:00 |
On the Compositional Generalization of Multimodal LLMs for Medical
Imaging
|
Multimodal large language models (MLLMs) hold significant potential in the
medical field, but their capabilities are often limited by insufficient data in
certain medical domains, highlighting the need for understanding what kinds of
images can be used by MLLMs for generalization. Current research suggests that
multi-task training outperforms single-task as different tasks can benefit each
other, but they often overlook the internal relationships within these tasks,
providing limited guidance on selecting datasets to enhance specific tasks. To
analyze this phenomenon, we attempted to employ compositional generalization
(CG)-the ability of models to understand novel combinations by recombining
learned elements-as a guiding framework. Since medical images can be precisely
defined by Modality, Anatomical area, and Task, naturally providing an
environment for exploring CG. Therefore, we assembled 106 medical datasets to
create Med-MAT for comprehensive experiments. The experiments confirmed that
MLLMs can use CG to understand unseen medical images and identified CG as one
of the main drivers of the generalization observed in multi-task training.
Additionally, further studies demonstrated that CG effectively supports
datasets with limited data and delivers consistent performance across different
backbones, highlighting its versatility and broad applicability. Med-MAT is
publicly available at https://github.com/FreedomIntelligence/Med-MAT.
| 46 |
67735fa09cc5d33bf6af3d85
| null | null |
|
2024-12-30T22:14:42.572000 |
Edicho: Consistent Image Editing in the Wild
| 2 |
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2412.21079
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"_id": "67736096b272a4f186d161ff",
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},
{
"_id": "67736096b272a4f186d16200",
"hidden": false,
"name": "Qifeng Chen",
"status": null,
"statusLastChangedAt": null,
"user": null
}
] | 2024-12-30T16:56:44 |
Edicho: Consistent Image Editing in the Wild
|
As a verified need, consistent editing across in-the-wild images remains a
technical challenge arising from various unmanageable factors, like object
poses, lighting conditions, and photography environments. Edicho steps in with
a training-free solution based on diffusion models, featuring a fundamental
design principle of using explicit image correspondence to direct editing.
Specifically, the key components include an attention manipulation module and a
carefully refined classifier-free guidance (CFG) denoising strategy, both of
which take into account the pre-estimated correspondence. Such an
inference-time algorithm enjoys a plug-and-play nature and is compatible to
most diffusion-based editing methods, such as ControlNet and BrushNet.
Extensive results demonstrate the efficacy of Edicho in consistent cross-image
editing under diverse settings. We will release the code to facilitate future
studies.
| 23 |
6773609db272a4f186d16447
| null | null |
|
2024-12-30T14:51:31.064000 |
CypherBench: Towards Precise Retrieval over Full-scale Modern Knowledge Graphs in the LLM Era
| 2 |
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| true | null |
2412.18702
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] | 2024-12-24T23:22:04 |
CypherBench: Towards Precise Retrieval over Full-scale Modern Knowledge
Graphs in the LLM Era
|
Retrieval from graph data is crucial for augmenting large language models
(LLM) with both open-domain knowledge and private enterprise data, and it is
also a key component in the recent GraphRAG system (edge et al., 2024). Despite
decades of research on knowledge graphs and knowledge base question answering,
leading LLM frameworks (e.g. Langchain and LlamaIndex) have only minimal
support for retrieval from modern encyclopedic knowledge graphs like Wikidata.
In this paper, we analyze the root cause and suggest that modern RDF knowledge
graphs (e.g. Wikidata, Freebase) are less efficient for LLMs due to overly
large schemas that far exceed the typical LLM context window, use of resource
identifiers, overlapping relation types and lack of normalization. As a
solution, we propose property graph views on top of the underlying RDF graph
that can be efficiently queried by LLMs using Cypher. We instantiated this idea
on Wikidata and introduced CypherBench, the first benchmark with 11
large-scale, multi-domain property graphs with 7.8 million entities and over
10,000 questions. To achieve this, we tackled several key challenges, including
developing an RDF-to-property graph conversion engine, creating a systematic
pipeline for text-to-Cypher task generation, and designing new evaluation
metrics.
| 6 |
6771fca4117cc54ff8b99c5a
| null | null |
|
2024-12-30T12:24:56.133000 |
1.58-bit FLUX
| 6 |
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2412.18653
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] | 2024-12-24T19:00:02 |
1.58-bit FLUX
|
We present 1.58-bit FLUX, the first successful approach to quantizing the
state-of-the-art text-to-image generation model, FLUX.1-dev, using 1.58-bit
weights (i.e., values in {-1, 0, +1}) while maintaining comparable performance
for generating 1024 x 1024 images. Notably, our quantization method operates
without access to image data, relying solely on self-supervision from the
FLUX.1-dev model. Additionally, we develop a custom kernel optimized for
1.58-bit operations, achieving a 7.7x reduction in model storage, a 5.1x
reduction in inference memory, and improved inference latency. Extensive
evaluations on the GenEval and T2I Compbench benchmarks demonstrate the
effectiveness of 1.58-bit FLUX in maintaining generation quality while
significantly enhancing computational efficiency.
| 79 |
6772d70133efe31653f02bde
| null | null |
|
2024-12-30T08:43:12.896000 |
Next Token Prediction Towards Multimodal Intelligence: A Comprehensive Survey
| 2 |
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2412.18619
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}
},
{
"_id": "67720aa292c63806bde6d2d7",
"hidden": false,
"name": "Tianyu Liu",
"status": null,
"statusLastChangedAt": null,
"user": null
},
{
"_id": "67720aa292c63806bde6d2d8",
"hidden": false,
"name": "Baobao Chang",
"status": null,
"statusLastChangedAt": null,
"user": null
}
] | 2024-12-16T05:02:25 |
Next Token Prediction Towards Multimodal Intelligence: A Comprehensive
Survey
|
Building on the foundations of language modeling in natural language
processing, Next Token Prediction (NTP) has evolved into a versatile training
objective for machine learning tasks across various modalities, achieving
considerable success. As Large Language Models (LLMs) have advanced to unify
understanding and generation tasks within the textual modality, recent research
has shown that tasks from different modalities can also be effectively
encapsulated within the NTP framework, transforming the multimodal information
into tokens and predict the next one given the context. This survey introduces
a comprehensive taxonomy that unifies both understanding and generation within
multimodal learning through the lens of NTP. The proposed taxonomy covers five
key aspects: Multimodal tokenization, MMNTP model architectures, unified task
representation, datasets \& evaluation, and open challenges. This new taxonomy
aims to aid researchers in their exploration of multimodal intelligence. An
associated GitHub repository collecting the latest papers and repos is
available at https://github.com/LMM101/Awesome-Multimodal-Next-Token-Prediction
| 55 |
67720aa492c63806bde6d350
| null | null |
|
2024-12-30T05:58:47.315000 |
Safeguard Fine-Tuned LLMs Through Pre- and Post-Tuning Model Merging
| 2 |
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2412.19512
|
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"fullname": "Hung-yi Lee",
"isPro": false,
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"user": "hungyilee"
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}
] | 2024-12-27T08:03:22 |
Safeguard Fine-Tuned LLMs Through Pre- and Post-Tuning Model Merging
|
Fine-tuning large language models (LLMs) for downstream tasks is a widely
adopted approach, but it often leads to safety degradation in safety-aligned
LLMs. Currently, many solutions address this issue by incorporating additional
safety data, which can be impractical in many cases. In this paper, we address
the question: How can we improve downstream task performance while preserving
safety in LLMs without relying on additional safety data? We propose a simple
and effective method that maintains the inherent safety of LLMs while enhancing
their downstream task performance: merging the weights of pre- and
post-fine-tuned safety-aligned models. Experimental results across various
downstream tasks, models, and merging methods demonstrate that this approach
effectively mitigates safety degradation while improving downstream task
performance, offering a practical solution for adapting safety-aligned LLMs.
| 8 |
67727ca6986fbffa7a220934
| null | null |
|
2024-12-30T03:07:39.186000 |
The Superposition of Diffusion Models Using the Itô Density Estimator
| 2 |
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[
"https://cdn-uploads.huggingface.co/production/uploads/65a49ef27ec6af0f956a5c61/P2wF-TfD9U5L1rN3ezxCI.gif"
] |
2412.17762
|
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},
{
"_id": "676d0a2e0076ad5ba195b88d",
"hidden": false,
"name": "Alexander Tong",
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"statusLastChangedAt": null,
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},
{
"_id": "676d0a2e0076ad5ba195b88e",
"hidden": false,
"name": "Kirill Neklyudov",
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"isPro": false,
"type": "user",
"user": "necludov"
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}
] | 2024-12-23T18:18:07 |
The Superposition of Diffusion Models Using the Itô Density Estimator
|
The Cambrian explosion of easily accessible pre-trained diffusion models
suggests a demand for methods that combine multiple different pre-trained
diffusion models without incurring the significant computational burden of
re-training a larger combined model. In this paper, we cast the problem of
combining multiple pre-trained diffusion models at the generation stage under a
novel proposed framework termed superposition. Theoretically, we derive
superposition from rigorous first principles stemming from the celebrated
continuity equation and design two novel algorithms tailor-made for combining
diffusion models in SuperDiff. SuperDiff leverages a new scalable It\^o density
estimator for the log likelihood of the diffusion SDE which incurs no
additional overhead compared to the well-known Hutchinson's estimator needed
for divergence calculations. We demonstrate that SuperDiff is scalable to large
pre-trained diffusion models as superposition is performed solely through
composition during inference, and also enjoys painless implementation as it
combines different pre-trained vector fields through an automated re-weighting
scheme. Notably, we show that SuperDiff is efficient during inference time, and
mimics traditional composition operators such as the logical OR and the logical
AND. We empirically demonstrate the utility of using SuperDiff for generating
more diverse images on CIFAR-10, more faithful prompt conditioned image editing
using Stable Diffusion, and improved unconditional de novo structure design of
proteins. https://github.com/necludov/super-diffusion
| 12 |
676d0a330076ad5ba195b97c
| null | null |
|
2024-12-30T01:27:40.574000 |
SBS Figures: Pre-training Figure QA from Stage-by-Stage Synthesized Images
| 2 |
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| true | null |
2412.17606
|
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},
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},
{
"_id": "677204632f016f40c42e2184",
"hidden": false,
"name": "Yoshitaka Ushiku",
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] | 2024-12-23T14:25:33 |
SBS Figures: Pre-training Figure QA from Stage-by-Stage Synthesized
Images
|
Building a large-scale figure QA dataset requires a considerable amount of
work, from gathering and selecting figures to extracting attributes like text,
numbers, and colors, and generating QAs. Although recent developments in LLMs
have led to efforts to synthesize figures, most of these focus primarily on QA
generation. Additionally, creating figures directly using LLMs often encounters
issues such as code errors, similar-looking figures, and repetitive content in
figures. To address this issue, we present SBSFigures (Stage-by-Stage Synthetic
Figures), a dataset for pre-training figure QA. Our proposed pipeline enables
the creation of chart figures with complete annotations of the visualized data
and dense QA annotations without any manual annotation process. Our
stage-by-stage pipeline makes it possible to create diverse topic and
appearance figures efficiently while minimizing code errors. Our SBSFigures
demonstrate a strong pre-training effect, making it possible to achieve
efficient training with a limited amount of real-world chart data starting from
our pre-trained weights.
| 5 |
677204652f016f40c42e2367
| null | null |
|
2024-12-30T00:04:19.275000 |
VideoMaker: Zero-shot Customized Video Generation with the Inherent Force of Video Diffusion Models
| 2 |
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| false | null |
2412.19645
|
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},
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},
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},
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"hidden": false,
"name": "Xi Li",
"status": null,
"statusLastChangedAt": null,
"user": null
}
] | 2024-12-27T13:49:25 |
VideoMaker: Zero-shot Customized Video Generation with the Inherent
Force of Video Diffusion Models
|
Zero-shot customized video generation has gained significant attention due to
its substantial application potential. Existing methods rely on additional
models to extract and inject reference subject features, assuming that the
Video Diffusion Model (VDM) alone is insufficient for zero-shot customized
video generation. However, these methods often struggle to maintain consistent
subject appearance due to suboptimal feature extraction and injection
techniques. In this paper, we reveal that VDM inherently possesses the force to
extract and inject subject features. Departing from previous heuristic
approaches, we introduce a novel framework that leverages VDM's inherent force
to enable high-quality zero-shot customized video generation. Specifically, for
feature extraction, we directly input reference images into VDM and use its
intrinsic feature extraction process, which not only provides fine-grained
features but also significantly aligns with VDM's pre-trained knowledge. For
feature injection, we devise an innovative bidirectional interaction between
subject features and generated content through spatial self-attention within
VDM, ensuring that VDM has better subject fidelity while maintaining the
diversity of the generated video.Experiments on both customized human and
object video generation validate the effectiveness of our framework.
| 13 |
677229c48103ad52cb7032d5
| null | null |
|
2024-12-29T23:57:59.186000 |
Task Preference Optimization: Improving Multimodal Large Language Models with Vision Task Alignment
| 2 |
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| false | null |
2412.19326
|
[
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},
{
"_id": "67722817633a6043c33212ad",
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"name": "Chenting Wang",
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},
{
"_id": "67722817633a6043c33212ae",
"hidden": false,
"name": "Kunchang Li",
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},
{
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},
{
"_id": "67722817633a6043c33212b3",
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},
{
"_id": "67722817633a6043c33212b4",
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"name": "Limin Wang",
"status": null,
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},
{
"_id": "67722817633a6043c33212b5",
"hidden": false,
"name": "Yi Wang",
"status": null,
"statusLastChangedAt": null,
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}
] | 2024-12-26T18:56:05 |
Task Preference Optimization: Improving Multimodal Large Language Models
with Vision Task Alignment
|
Current multimodal large language models (MLLMs) struggle with fine-grained
or precise understanding of visuals though they give comprehensive perception
and reasoning in a spectrum of vision applications. Recent studies either
develop tool-using or unify specific visual tasks into the autoregressive
framework, often at the expense of overall multimodal performance. To address
this issue and enhance MLLMs with visual tasks in a scalable fashion, we
propose Task Preference Optimization (TPO), a novel method that utilizes
differentiable task preferences derived from typical fine-grained visual tasks.
TPO introduces learnable task tokens that establish connections between
multiple task-specific heads and the MLLM. By leveraging rich visual labels
during training, TPO significantly enhances the MLLM's multimodal capabilities
and task-specific performance. Through multi-task co-training within TPO, we
observe synergistic benefits that elevate individual task performance beyond
what is achievable through single-task training methodologies. Our
instantiation of this approach with VideoChat and LLaVA demonstrates an overall
14.6% improvement in multimodal performance compared to baseline models.
Additionally, MLLM-TPO demonstrates robust zero-shot capabilities across
various tasks, performing comparably to state-of-the-art supervised models. The
code will be released at https://github.com/OpenGVLab/TPO
| 18 |
6772281a633a6043c3321365
| null | null |
|
2024-12-29T23:24:15.208000 |
From Elements to Design: A Layered Approach for Automatic Graphic Design Composition
| 2 |
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[
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"https://cdn-uploads.huggingface.co/production/uploads/6440dda9cea37249a0f9b473/q1JODwSII9FQVA0dee4EA.png"
] |
2412.19712
|
[
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},
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"_id": "67721e8cb9d0358385f66290",
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},
{
"_id": "67721e8cb9d0358385f66291",
"hidden": false,
"name": "Ji Li",
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},
{
"_id": "67721e8cb9d0358385f66292",
"hidden": false,
"name": "Jiang Bian",
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}
] | 2024-12-27T16:13:08 |
From Elements to Design: A Layered Approach for Automatic Graphic Design
Composition
|
In this work, we investigate automatic design composition from multimodal
graphic elements. Although recent studies have developed various generative
models for graphic design, they usually face the following limitations: they
only focus on certain subtasks and are far from achieving the design
composition task; they do not consider the hierarchical information of graphic
designs during the generation process. To tackle these issues, we introduce the
layered design principle into Large Multimodal Models (LMMs) and propose a
novel approach, called LaDeCo, to accomplish this challenging task.
Specifically, LaDeCo first performs layer planning for a given element set,
dividing the input elements into different semantic layers according to their
contents. Based on the planning results, it subsequently predicts element
attributes that control the design composition in a layer-wise manner, and
includes the rendered image of previously generated layers into the context.
With this insightful design, LaDeCo decomposes the difficult task into smaller
manageable steps, making the generation process smoother and clearer. The
experimental results demonstrate the effectiveness of LaDeCo in design
composition. Furthermore, we show that LaDeCo enables some interesting
applications in graphic design, such as resolution adjustment, element filling,
design variation, etc. In addition, it even outperforms the specialized models
in some design subtasks without any task-specific training.
| 15 |
67721e92b9d0358385f66457
| null | null |
|
2024-12-29T22:31:54.173000 |
HuatuoGPT-o1, Towards Medical Complex Reasoning with LLMs
| 6 |
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| true | null |
2412.18925
|
[
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},
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"hidden": false,
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},
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"_id": "677209448e0ed7713b183678",
"hidden": false,
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},
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"_id": "677209448e0ed7713b183679",
"hidden": false,
"name": "Rongsheng Wang",
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{
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}
] | 2024-12-25T15:12:34 |
HuatuoGPT-o1, Towards Medical Complex Reasoning with LLMs
|
The breakthrough of OpenAI o1 highlights the potential of enhancing reasoning
to improve LLM. Yet, most research in reasoning has focused on mathematical
tasks, leaving domains like medicine underexplored. The medical domain, though
distinct from mathematics, also demands robust reasoning to provide reliable
answers, given the high standards of healthcare. However, verifying medical
reasoning is challenging, unlike those in mathematics. To address this, we
propose verifiable medical problems with a medical verifier to check the
correctness of model outputs. This verifiable nature enables advancements in
medical reasoning through a two-stage approach: (1) using the verifier to guide
the search for a complex reasoning trajectory for fine-tuning LLMs, (2)
applying reinforcement learning (RL) with verifier-based rewards to enhance
complex reasoning further. Finally, we introduce HuatuoGPT-o1, a medical LLM
capable of complex reasoning, which outperforms general and medical-specific
baselines using only 40K verifiable problems. Experiments show complex
reasoning improves medical problem-solving and benefits more from RL. We hope
our approach inspires advancements in reasoning across medical and other
specialized domains.
| 97 |
677209448e0ed7713b1836cb
| null | null |
|
2024-12-29T21:38:37.393000 |
Orient Anything: Learning Robust Object Orientation Estimation from Rendering 3D Models
| 4 |
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| true |
[
"https://cdn-uploads.huggingface.co/production/uploads/663b4d6aa55b0634634cd302/otb9alc3ESHg68f_TBcFp.png"
] |
2412.18605
|
[
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},
{
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"name": "Ziang Zhang",
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},
{
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"hidden": false,
"name": "Tianyu Pang",
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"statusLastChangedAt": "2024-12-30T20:52:17.280Z",
"user": {
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"type": "user",
"user": "P2333"
}
},
{
"_id": "676bb2c29063304d2d9ec679",
"hidden": false,
"name": "Chao Du",
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"statusLastChangedAt": null,
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},
{
"_id": "676bb2c29063304d2d9ec67a",
"hidden": false,
"name": "Hengshuang Zhao",
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},
{
"_id": "676bb2c29063304d2d9ec67b",
"hidden": false,
"name": "Zhou Zhao",
"status": null,
"statusLastChangedAt": null,
"user": null
}
] | 2024-12-24T18:58:43 |
Orient Anything: Learning Robust Object Orientation Estimation from
Rendering 3D Models
|
Orientation is a key attribute of objects, crucial for understanding their
spatial pose and arrangement in images. However, practical solutions for
accurate orientation estimation from a single image remain underexplored. In
this work, we introduce Orient Anything, the first expert and foundational
model designed to estimate object orientation in a single- and free-view image.
Due to the scarcity of labeled data, we propose extracting knowledge from the
3D world. By developing a pipeline to annotate the front face of 3D objects and
render images from random views, we collect 2M images with precise orientation
annotations. To fully leverage the dataset, we design a robust training
objective that models the 3D orientation as probability distributions of three
angles and predicts the object orientation by fitting these distributions.
Besides, we employ several strategies to improve synthetic-to-real transfer.
Our model achieves state-of-the-art orientation estimation accuracy in both
rendered and real images and exhibits impressive zero-shot ability in various
scenarios. More importantly, our model enhances many applications, such as
comprehension and generation of complex spatial concepts and 3D object pose
adjustment.
| 20 |
676bb2c49063304d2d9ec7d0
| null | null |
|
2024-12-27T07:29:26.502000 |
Molar: Multimodal LLMs with Collaborative Filtering Alignment for Enhanced Sequential Recommendation
| 2 |
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| false | null |
2412.18176
|
[
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"name": "Yucong Luo",
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},
{
"_id": "676e9d9d8126645611b73ecd",
"hidden": false,
"name": "Hao Zhang",
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},
{
"_id": "676e9d9d8126645611b73ece",
"hidden": false,
"name": "Mingyue Cheng",
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"user": {
"_id": "647f5222e9c81260ff87640d",
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"type": "user",
"user": "twigcheng"
}
},
{
"_id": "676e9d9d8126645611b73ecf",
"hidden": false,
"name": "Ruiran Yan",
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"statusLastChangedAt": "2024-12-30T21:12:07.779Z",
"user": {
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"type": "user",
"user": "Ruiran"
}
},
{
"_id": "676e9d9d8126645611b73ed0",
"hidden": false,
"name": "Kefan Wang",
"status": null,
"statusLastChangedAt": null,
"user": null
},
{
"_id": "676e9d9d8126645611b73ed1",
"hidden": false,
"name": "Jie Ouyang",
"status": "admin_assigned",
"statusLastChangedAt": "2024-12-30T21:12:23.124Z",
"user": {
"_id": "653780f5434f0b412aba51c2",
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"fullname": "Jie Ouyang",
"isPro": false,
"type": "user",
"user": "russwest404"
}
}
] | 2024-12-24T05:23:13 |
Molar: Multimodal LLMs with Collaborative Filtering Alignment for
Enhanced Sequential Recommendation
|
Sequential recommendation (SR) systems have evolved significantly over the
past decade, transitioning from traditional collaborative filtering to deep
learning approaches and, more recently, to large language models (LLMs). While
the adoption of LLMs has driven substantial advancements, these models
inherently lack collaborative filtering information, relying primarily on
textual content data neglecting other modalities and thus failing to achieve
optimal recommendation performance. To address this limitation, we propose
Molar, a Multimodal large language sequential recommendation framework that
integrates multiple content modalities with ID information to capture
collaborative signals effectively. Molar employs an MLLM to generate unified
item representations from both textual and non-textual data, facilitating
comprehensive multimodal modeling and enriching item embeddings. Additionally,
it incorporates collaborative filtering signals through a post-alignment
mechanism, which aligns user representations from content-based and ID-based
models, ensuring precise personalization and robust performance. By seamlessly
combining multimodal content with collaborative filtering insights, Molar
captures both user interests and contextual semantics, leading to superior
recommendation accuracy. Extensive experiments validate that Molar
significantly outperforms traditional and LLM-based baselines, highlighting its
strength in utilizing multimodal data and collaborative signals for sequential
recommendation tasks. The source code is available at
https://anonymous.4open.science/r/Molar-8B06/.
| 15 |
676e9d9d8126645611b73f18
| null | null |
|
2024-12-27T07:26:55.759000 |
MMFactory: A Universal Solution Search Engine for Vision-Language Tasks
| 2 |
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}
| false | null |
2412.18072
|
[
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"user": "ChrisFan"
}
},
{
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"hidden": false,
"name": "Tanzila Rahman",
"status": "admin_assigned",
"statusLastChangedAt": "2024-12-30T21:12:47.649Z",
"user": {
"_id": "64062c3ca577649430c1006b",
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"fullname": "Tanzila Rahman",
"isPro": false,
"type": "user",
"user": "trahman"
}
},
{
"_id": "676e9cfc11998b72ab00be62",
"hidden": false,
"name": "Leonid Sigal",
"status": null,
"statusLastChangedAt": null,
"user": null
}
] | 2024-12-24T00:59:16 |
MMFactory: A Universal Solution Search Engine for Vision-Language Tasks
|
With advances in foundational and vision-language models, and effective
fine-tuning techniques, a large number of both general and special-purpose
models have been developed for a variety of visual tasks. Despite the
flexibility and accessibility of these models, no single model is able to
handle all tasks and/or applications that may be envisioned by potential users.
Recent approaches, such as visual programming and multimodal LLMs with
integrated tools aim to tackle complex visual tasks, by way of program
synthesis. However, such approaches overlook user constraints (e.g.,
performance / computational needs), produce test-time sample-specific solutions
that are difficult to deploy, and, sometimes, require low-level instructions
that maybe beyond the abilities of a naive user. To address these limitations,
we introduce MMFactory, a universal framework that includes model and metrics
routing components, acting like a solution search engine across various
available models. Based on a task description and few sample input-output pairs
and (optionally) resource and/or performance constraints, MMFactory can suggest
a diverse pool of programmatic solutions by instantiating and combining
visio-lingual tools from its model repository. In addition to synthesizing
these solutions, MMFactory also proposes metrics and benchmarks performance /
resource characteristics, allowing users to pick a solution that meets their
unique design constraints. From the technical perspective, we also introduced a
committee-based solution proposer that leverages multi-agent LLM conversation
to generate executable, diverse, universal, and robust solutions for the user.
Experimental results show that MMFactory outperforms existing methods by
delivering state-of-the-art solutions tailored to user problem specifications.
Project page is available at https://davidhalladay.github.io/mmfactory_demo.
| 18 |
676e9cfd11998b72ab00bfe8
| null | null |
|
2024-12-27T02:39:50.862000 |
A Silver Bullet or a Compromise for Full Attention? A Comprehensive Study of Gist Token-based Context Compression
| 2 |
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"name": "ChenlongDeng",
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}
| true | null |
2412.17483
|
[
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"name": "Chenlong Deng",
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},
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},
{
"_id": "676a2a3ebce62ec5a02c4a68",
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"_id": "676a2a3ebce62ec5a02c4a6a",
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] | 2024-12-23T11:24:04 |
A Silver Bullet or a Compromise for Full Attention? A Comprehensive
Study of Gist Token-based Context Compression
|
In this work, we provide a thorough investigation of gist-based context
compression methods to improve long-context processing in large language
models. We focus on two key questions: (1) How well can these methods replace
full attention models? and (2) What potential failure patterns arise due to
compression? Through extensive experiments, we show that while gist-based
compression can achieve near-lossless performance on tasks like
retrieval-augmented generation and long-document QA, it faces challenges in
tasks like synthetic recall. Furthermore, we identify three key failure
patterns: lost by the boundary, lost if surprise, and lost along the way. To
mitigate these issues, we propose two effective strategies: fine-grained
autoencoding, which enhances the reconstruction of original token information,
and segment-wise token importance estimation, which adjusts optimization based
on token dependencies. Our work provides valuable insights into the
understanding of gist token-based context compression and offers practical
strategies for improving compression capabilities.
| 31 |
676a2a3fbce62ec5a02c4ace
| null | null |
|
2024-12-27T01:08:10.879000 |
YuLan-Mini: An Open Data-efficient Language Model
| 2 |
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2412.17743
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] | 2024-12-23T17:47:53 |
YuLan-Mini: An Open Data-efficient Language Model
|
Effective pre-training of large language models (LLMs) has been challenging
due to the immense resource demands and the complexity of the technical
processes involved. This paper presents a detailed technical report on
YuLan-Mini, a highly capable base model with 2.42B parameters that achieves
top-tier performance among models of similar parameter scale. Our pre-training
approach focuses on enhancing training efficacy through three key technical
contributions: an elaborate data pipeline combines data cleaning with data
schedule strategies, a robust optimization method to mitigate training
instability, and an effective annealing approach that incorporates targeted
data selection and long context training. Remarkably, YuLan-Mini, trained on
1.08T tokens, achieves performance comparable to industry-leading models that
require significantly more data. To facilitate reproduction, we release the
full details of the data composition for each training phase. Project details
can be accessed at the following link: https://github.com/RUC-GSAI/YuLan-Mini.
| 65 |
676d2c65310ca4eb39741682
| null | null |
|
2024-12-26T21:16:18.231000 |
VidTwin: Video VAE with Decoupled Structure and Dynamics
| 3 |
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2412.17726
|
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"name": "Xu Sun",
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"name": "Jiang Bian",
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}
] | 2024-12-23T17:16:58 |
VidTwin: Video VAE with Decoupled Structure and Dynamics
|
Recent advancements in video autoencoders (Video AEs) have significantly
improved the quality and efficiency of video generation. In this paper, we
propose a novel and compact video autoencoder, VidTwin, that decouples video
into two distinct latent spaces: Structure latent vectors, which capture
overall content and global movement, and Dynamics latent vectors, which
represent fine-grained details and rapid movements. Specifically, our approach
leverages an Encoder-Decoder backbone, augmented with two submodules for
extracting these latent spaces, respectively. The first submodule employs a
Q-Former to extract low-frequency motion trends, followed by downsampling
blocks to remove redundant content details. The second averages the latent
vectors along the spatial dimension to capture rapid motion. Extensive
experiments show that VidTwin achieves a high compression rate of 0.20% with
high reconstruction quality (PSNR of 28.14 on the MCL-JCV dataset), and
performs efficiently and effectively in downstream generative tasks. Moreover,
our model demonstrates explainability and scalability, paving the way for
future research in video latent representation and generation. Our code has
been released at https://github.com/microsoft/VidTok/tree/main/vidtwin.
| 8 |
676d52fb91c1773322dcc8b6
| null | null |
|
2024-12-26T15:44:06.575000 |
How "Real" is Your Real-Time Simultaneous Speech-to-Text Translation System?
| 2 |
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| true | null |
2412.18495
|
[
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},
{
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"name": "Ondřej Bojar",
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},
{
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"hidden": false,
"name": "Dominik Macháček",
"status": null,
"statusLastChangedAt": null,
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}
] | 2024-12-24T15:26:31 |
How "Real" is Your Real-Time Simultaneous Speech-to-Text Translation
System?
|
Simultaneous speech-to-text translation (SimulST) translates source-language
speech into target-language text concurrently with the speaker's speech,
ensuring low latency for better user comprehension. Despite its intended
application to unbounded speech, most research has focused on human
pre-segmented speech, simplifying the task and overlooking significant
challenges. This narrow focus, coupled with widespread terminological
inconsistencies, is limiting the applicability of research outcomes to
real-world applications, ultimately hindering progress in the field. Our
extensive literature review of 110 papers not only reveals these critical
issues in current research but also serves as the foundation for our key
contributions. We 1) define the steps and core components of a SimulST system,
proposing a standardized terminology and taxonomy; 2) conduct a thorough
analysis of community trends, and 3) offer concrete recommendations and future
directions to bridge the gaps in existing literature, from evaluation
frameworks to system architectures, for advancing the field towards more
realistic and effective SimulST solutions.
| 8 |
676dbfe37fff9075b5388918
| null | null |
|
2024-12-26T12:13:44.072000 |
WavePulse: Real-time Content Analytics of Radio Livestreams
| 4 |
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| true | null |
2412.17998
|
[
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},
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},
{
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},
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"name": "Nasir Memon",
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] | 2024-12-23T21:42:31 |
WavePulse: Real-time Content Analytics of Radio Livestreams
|
Radio remains a pervasive medium for mass information dissemination, with
AM/FM stations reaching more Americans than either smartphone-based social
networking or live television. Increasingly, radio broadcasts are also streamed
online and accessed over the Internet. We present WavePulse, a framework that
records, documents, and analyzes radio content in real-time. While our
framework is generally applicable, we showcase the efficacy of WavePulse in a
collaborative project with a team of political scientists focusing on the 2024
Presidential Elections. We use WavePulse to monitor livestreams of 396 news
radio stations over a period of three months, processing close to 500,000 hours
of audio streams. These streams were converted into time-stamped, diarized
transcripts and analyzed to track answer key political science questions at
both the national and state levels. Our analysis revealed how local issues
interacted with national trends, providing insights into information flow. Our
results demonstrate WavePulse's efficacy in capturing and analyzing content
from radio livestreams sourced from the Web. Code and dataset can be accessed
at https://wave-pulse.io.
| 10 |
676d8ebc8771d55751f14311
| null | null |
|
2024-12-26T11:33:39.769000 |
Video-Panda: Parameter-efficient Alignment for Encoder-free Video-Language Models
| 2 |
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| false | null |
2412.18609
|
[
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"name": "Yanan Luo",
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},
{
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] | 2024-12-24T18:59:56 |
Video-Panda: Parameter-efficient Alignment for Encoder-free
Video-Language Models
|
We present an efficient encoder-free approach for video-language
understanding that achieves competitive performance while significantly
reducing computational overhead. Current video-language models typically rely
on heavyweight image encoders (300M-1.1B parameters) or video encoders (1B-1.4B
parameters), creating a substantial computational burden when processing
multi-frame videos. Our method introduces a novel Spatio-Temporal Alignment
Block (STAB) that directly processes video inputs without requiring pre-trained
encoders while using only 45M parameters for visual processing - at least a
6.5times reduction compared to traditional approaches. The STAB architecture
combines Local Spatio-Temporal Encoding for fine-grained feature extraction,
efficient spatial downsampling through learned attention and separate
mechanisms for modeling frame-level and video-level relationships. Our model
achieves comparable or superior performance to encoder-based approaches for
open-ended video question answering on standard benchmarks. The fine-grained
video question-answering evaluation demonstrates our model's effectiveness,
outperforming the encoder-based approaches Video-ChatGPT and Video-LLaVA in key
aspects like correctness and temporal understanding. Extensive ablation studies
validate our architectural choices and demonstrate the effectiveness of our
spatio-temporal modeling approach while achieving 3-4times faster processing
speeds than previous methods. Code is available at
https://github.com/jh-yi/Video-Panda.
| 17 |
676d854e8771d55751ee108f
| null | null |
|
2024-12-26T11:29:06.978000 |
Mulberry: Empowering MLLM with o1-like Reasoning and Reflection via Collective Monte Carlo Tree Search
| 2 |
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] | 2024-12-24T10:07:51 |
Mulberry: Empowering MLLM with o1-like Reasoning and Reflection via
Collective Monte Carlo Tree Search
|
In this work, we aim to develop an MLLM that understands and solves questions
by learning to create each intermediate step of the reasoning involved till the
final answer. To this end, we propose Collective Monte Carlo Tree Search
(CoMCTS), a new learning-to-reason method for MLLMs, which introduces the
concept of collective learning into ``tree search'' for effective and efficient
reasoning-path searching and learning. The core idea of CoMCTS is to leverage
collective knowledge from multiple models to collaboratively conjecture, search
and identify effective reasoning paths toward correct answers via four
iterative operations including Expansion, Simulation and Error Positioning,
Backpropagation, and Selection. Using CoMCTS, we construct Mulberry-260k, a
multimodal dataset with a tree of rich, explicit and well-defined reasoning
nodes for each question. With Mulberry-260k, we perform collective SFT to train
our model, Mulberry, a series of MLLMs with o1-like step-by-step Reasoning and
Reflection capabilities. Extensive experiments demonstrate the superiority of
our proposed methods on various benchmarks. Code will be available at
https://github.com/HJYao00/Mulberry
| 37 |
676d843e92c4a8fe495328d3
| null | null |
|
2024-12-26T10:26:14.913000 |
PepTune: De Novo Generation of Therapeutic Peptides with Multi-Objective-Guided Discrete Diffusion
| 2 |
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2412.17780
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] | 2024-12-23T18:38:49 |
PepTune: De Novo Generation of Therapeutic Peptides with
Multi-Objective-Guided Discrete Diffusion
|
Peptide therapeutics, a major class of medicines, have achieved remarkable
success across diseases such as diabetes and cancer, with landmark examples
such as GLP-1 receptor agonists revolutionizing the treatment of type-2
diabetes and obesity. Despite their success, designing peptides that satisfy
multiple conflicting objectives, such as target binding affinity, solubility,
and membrane permeability, remains a major challenge. Classical drug
development and structure-based design are ineffective for such tasks, as they
fail to optimize global functional properties critical for therapeutic
efficacy. Existing generative frameworks are largely limited to continuous
spaces, unconditioned outputs, or single-objective guidance, making them
unsuitable for discrete sequence optimization across multiple properties. To
address this, we present PepTune, a multi-objective discrete diffusion model
for the simultaneous generation and optimization of therapeutic peptide SMILES.
Built on the Masked Discrete Language Model (MDLM) framework, PepTune ensures
valid peptide structures with state-dependent masking schedules and
penalty-based objectives. To guide the diffusion process, we propose a Monte
Carlo Tree Search (MCTS)-based strategy that balances exploration and
exploitation to iteratively refine Pareto-optimal sequences. MCTS integrates
classifier-based rewards with search-tree expansion, overcoming gradient
estimation challenges and data sparsity inherent to discrete spaces. Using
PepTune, we generate diverse, chemically-modified peptides optimized for
multiple therapeutic properties, including target binding affinity, membrane
permeability, solubility, hemolysis, and non-fouling characteristics on various
disease-relevant targets. In total, our results demonstrate that MCTS-guided
discrete diffusion is a powerful and modular approach for multi-objective
sequence design in discrete state spaces.
| 4 |
676d74d396a84bb36f9d0660
| null | null |
|
2024-12-25T22:21:50.545000 |
Token-Budget-Aware LLM Reasoning
| 2 |
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2412.18547
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] | 2024-12-24T16:55:45 |
Token-Budget-Aware LLM Reasoning
|
Reasoning is critical for large language models (LLMs) to excel in a wide
range of tasks. While methods like Chain-of-Thought (CoT) reasoning enhance LLM
performance by decomposing problems into intermediate steps, they also incur
significant overhead in token usage, leading to increased costs. We find that
the reasoning process of current LLMs is unnecessarily lengthy and it can be
compressed by including a reasonable token budget in the prompt, but the choice
of token budget plays a crucial role in the actual compression effectiveness.
We then propose a token-budget-aware LLM reasoning framework, which dynamically
estimates token budgets for different problems based on reasoning complexity
and uses the estimated token budgets to guide the reasoning process.
Experiments show that our method effectively reduces token costs in CoT
reasoning with only a slight performance reduction, offering a practical
solution to balance efficiency and accuracy in LLM reasoning. Code:
https://github.com/GeniusHTX/TALE.
| 46 |
676c40d719a21c8b928d13ea
| null | null |
|
2024-12-25T15:44:24.255000 |
Bridging the Data Provenance Gap Across Text, Speech and Video
| 2 |
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"hidden": false,
"name": "Jad Kabbara",
"status": null,
"statusLastChangedAt": null,
"user": null
}
] | 2024-12-19T01:30:19 |
Bridging the Data Provenance Gap Across Text, Speech and Video
|
Progress in AI is driven largely by the scale and quality of training data.
Despite this, there is a deficit of empirical analysis examining the attributes
of well-established datasets beyond text. In this work we conduct the largest
and first-of-its-kind longitudinal audit across modalities--popular text,
speech, and video datasets--from their detailed sourcing trends and use
restrictions to their geographical and linguistic representation. Our manual
analysis covers nearly 4000 public datasets between 1990-2024, spanning 608
languages, 798 sources, 659 organizations, and 67 countries. We find that
multimodal machine learning applications have overwhelmingly turned to
web-crawled, synthetic, and social media platforms, such as YouTube, for their
training sets, eclipsing all other sources since 2019. Secondly, tracing the
chain of dataset derivations we find that while less than 33% of datasets are
restrictively licensed, over 80% of the source content in widely-used text,
speech, and video datasets, carry non-commercial restrictions. Finally, counter
to the rising number of languages and geographies represented in public AI
training datasets, our audit demonstrates measures of relative geographical and
multilingual representation have failed to significantly improve their coverage
since 2013. We believe the breadth of our audit enables us to empirically
examine trends in data sourcing, restrictions, and Western-centricity at an
ecosystem-level, and that visibility into these questions are essential to
progress in responsible AI. As a contribution to ongoing improvements in
dataset transparency and responsible use, we release our entire multimodal
audit, allowing practitioners to trace data provenance across text, speech, and
video.
| 9 |
676b6f6f1f5ca46174ac9777
| null | null |
|
2024-12-25T13:09:18.809000 |
MotiF: Making Text Count in Image Animation with Motion Focal Loss
| 2 |
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| true | null |
2412.16153
|
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"_id": "676c4a04295f85d93ef6d6b7",
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"name": "Samaneh Azadi",
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},
{
"_id": "676c4a04295f85d93ef6d6b8",
"hidden": false,
"name": "Rohit Girdhar",
"status": null,
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},
{
"_id": "676c4a04295f85d93ef6d6b9",
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"name": "Saketh Rambhatla",
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},
{
"_id": "676c4a04295f85d93ef6d6ba",
"hidden": false,
"name": "Chen Sun",
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},
{
"_id": "676c4a04295f85d93ef6d6bb",
"hidden": false,
"name": "Xi Yin",
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"statusLastChangedAt": null,
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}
] | 2024-12-20T18:57:06 |
MotiF: Making Text Count in Image Animation with Motion Focal Loss
|
Text-Image-to-Video (TI2V) generation aims to generate a video from an image
following a text description, which is also referred to as text-guided image
animation. Most existing methods struggle to generate videos that align well
with the text prompts, particularly when motion is specified. To overcome this
limitation, we introduce MotiF, a simple yet effective approach that directs
the model's learning to the regions with more motion, thereby improving the
text alignment and motion generation. We use optical flow to generate a motion
heatmap and weight the loss according to the intensity of the motion. This
modified objective leads to noticeable improvements and complements existing
methods that utilize motion priors as model inputs. Additionally, due to the
lack of a diverse benchmark for evaluating TI2V generation, we propose TI2V
Bench, a dataset consists of 320 image-text pairs for robust evaluation. We
present a human evaluation protocol that asks the annotators to select an
overall preference between two videos followed by their justifications. Through
a comprehensive evaluation on TI2V Bench, MotiF outperforms nine open-sourced
models, achieving an average preference of 72%. The TI2V Bench is released in
https://wang-sj16.github.io/motif/.
| 6 |
676c4a08295f85d93ef6d7b3
| null | null |
|
2024-12-25T08:46:30.796000 |
Ensembling Large Language Models with Process Reward-Guided Tree Search for Better Complex Reasoning
| 3 |
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| true | null |
2412.15797
|
[
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{
"_id": "676c0c92dd95830fd9d60012",
"hidden": false,
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},
{
"_id": "676c0c92dd95830fd9d60013",
"hidden": false,
"name": "Edward Choi",
"status": null,
"statusLastChangedAt": null,
"user": null
}
] | 2024-12-20T11:14:29 |
Ensembling Large Language Models with Process Reward-Guided Tree Search
for Better Complex Reasoning
|
Despite recent advances in large language models, open-source models often
struggle to consistently perform well on complex reasoning tasks. Existing
ensemble methods, whether applied at the token or output levels, fail to
address these challenges. In response, we present Language model Ensemble with
Monte Carlo Tree Search (LE-MCTS), a novel framework for process-level
ensembling of language models. LE-MCTS formulates step-by-step reasoning with
an ensemble of language models as a Markov decision process. In this framework,
states represent intermediate reasoning paths, while actions consist of
generating the next reasoning step using one of the language models selected
from a predefined pool. Guided by a process-based reward model, LE-MCTS
performs a tree search over the reasoning steps generated by different language
models, identifying the most accurate reasoning chain. Experimental results on
five mathematical reasoning benchmarks demonstrate that our approach
outperforms both single language model decoding algorithms and language model
ensemble methods. Notably, LE-MCTS improves performance by 3.6% and 4.3% on the
MATH and MQA datasets, respectively, highlighting its effectiveness in solving
complex reasoning problems.
| 18 |
676c0c98dd95830fd9d60172
| null | null |
|
2024-12-25T04:33:08.560000 |
In Case You Missed It: ARC 'Challenge' Is Not That Challenging
| 2 |
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| true | null |
2412.17758
|
[
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] | 2024-12-23T18:14:36 |
In Case You Missed It: ARC 'Challenge' Is Not That Challenging
|
ARC Challenge appears more difficult than ARC Easy for modern LLMs primarily
due to an evaluation setup that prevents direct comparison of answer choices
rather than inherent complexity. Although some researchers have quietly shifted
to a more appropriate scheme over the last year, the implications of this
change have yet to be widely acknowledged. We highlight this overlooked shift,
show how similar evaluation practices falsely imply reasoning deficits in other
benchmarks, and demonstrate that fairer methods dramatically reduce performance
gaps (e.g. on SIQA) and even yield superhuman results (OpenBookQA). In doing
so, we reveal how evaluation shapes perceived difficulty and offer guidelines
to ensure that multiple-choice evaluations accurately reflect actual model
capabilities.
| 16 |
676bd06724bd46fa1990dd63
| null | null |
|
2024-12-25T04:26:52.921000 |
3DGraphLLM: Combining Semantic Graphs and Large Language Models for 3D Scene Understanding
| 2 |
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] |
2412.18450
|
[
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{
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"user": "yuddim"
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}
] | 2024-12-24T14:21:58 |
3DGraphLLM: Combining Semantic Graphs and Large Language Models for 3D
Scene Understanding
|
A 3D scene graph represents a compact scene model, storing information about
the objects and the semantic relationships between them, making its use
promising for robotic tasks. When interacting with a user, an embodied
intelligent agent should be capable of responding to various queries about the
scene formulated in natural language. Large Language Models (LLMs) are
beneficial solutions for user-robot interaction due to their natural language
understanding and reasoning abilities. Recent methods for creating learnable
representations of 3D scenes have demonstrated the potential to improve the
quality of LLMs responses by adapting to the 3D world. However, the existing
methods do not explicitly utilize information about the semantic relationships
between objects, limiting themselves to information about their coordinates. In
this work, we propose a method 3DGraphLLM for constructing a learnable
representation of a 3D scene graph. The learnable representation is used as
input for LLMs to perform 3D vision-language tasks. In our experiments on
popular ScanRefer, RIORefer, Multi3DRefer, ScanQA, Sqa3D, and Scan2cap
datasets, we demonstrate the advantage of this approach over baseline methods
that do not use information about the semantic relationships between objects.
The code is publicly available at
https://github.com/CognitiveAISystems/3DGraphLLM.
| 34 |
676bbe599484d105b89dbac5
| null | null |
|
2024-12-25T01:53:21.705000 |
PartGen: Part-level 3D Generation and Reconstruction with Multi-View Diffusion Models
| 2 |
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| false | null |
2412.18608
|
[
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"hidden": false,
"name": "Minghao Chen",
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{
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"name": "Tom Monnier",
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},
{
"_id": "676baa15295f85d93eb48f28",
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"name": "Jianyuan Wang",
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},
{
"_id": "676baa15295f85d93eb48f29",
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"name": "David Novotny",
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},
{
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"name": "Andrea Vedaldi",
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}
] | 2024-12-24T18:59:43 |
PartGen: Part-level 3D Generation and Reconstruction with Multi-View
Diffusion Models
|
Text- or image-to-3D generators and 3D scanners can now produce 3D assets
with high-quality shapes and textures. These assets typically consist of a
single, fused representation, like an implicit neural field, a Gaussian
mixture, or a mesh, without any useful structure. However, most applications
and creative workflows require assets to be made of several meaningful parts
that can be manipulated independently. To address this gap, we introduce
PartGen, a novel approach that generates 3D objects composed of meaningful
parts starting from text, an image, or an unstructured 3D object. First, given
multiple views of a 3D object, generated or rendered, a multi-view diffusion
model extracts a set of plausible and view-consistent part segmentations,
dividing the object into parts. Then, a second multi-view diffusion model takes
each part separately, fills in the occlusions, and uses those completed views
for 3D reconstruction by feeding them to a 3D reconstruction network. This
completion process considers the context of the entire object to ensure that
the parts integrate cohesively. The generative completion model can make up for
the information missing due to occlusions; in extreme cases, it can hallucinate
entirely invisible parts based on the input 3D asset. We evaluate our method on
generated and real 3D assets and show that it outperforms segmentation and
part-extraction baselines by a large margin. We also showcase downstream
applications such as 3D part editing.
| 15 |
676baa1f295f85d93eb4928a
| null | null |
|
2024-12-25T00:47:02.418000 |
DiTCtrl: Exploring Attention Control in Multi-Modal Diffusion Transformer for Tuning-Free Multi-Prompt Longer Video Generation
| 2 |
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2412.18597
|
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},
{
"_id": "676b9b876fb4876383b8591f",
"hidden": false,
"name": "Zhaoyang Zhang",
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},
{
"_id": "676b9b876fb4876383b85920",
"hidden": false,
"name": "Yong Zhang",
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},
{
"_id": "676b9b876fb4876383b85921",
"hidden": false,
"name": "Ying Shan",
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},
{
"_id": "676b9b876fb4876383b85922",
"hidden": false,
"name": "Xiangyu Yue",
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}
] | 2024-12-24T18:51:19 |
DiTCtrl: Exploring Attention Control in Multi-Modal Diffusion
Transformer for Tuning-Free Multi-Prompt Longer Video Generation
|
Sora-like video generation models have achieved remarkable progress with a
Multi-Modal Diffusion Transformer MM-DiT architecture. However, the current
video generation models predominantly focus on single-prompt, struggling to
generate coherent scenes with multiple sequential prompts that better reflect
real-world dynamic scenarios. While some pioneering works have explored
multi-prompt video generation, they face significant challenges including
strict training data requirements, weak prompt following, and unnatural
transitions. To address these problems, we propose DiTCtrl, a training-free
multi-prompt video generation method under MM-DiT architectures for the first
time. Our key idea is to take the multi-prompt video generation task as
temporal video editing with smooth transitions. To achieve this goal, we first
analyze MM-DiT's attention mechanism, finding that the 3D full attention
behaves similarly to that of the cross/self-attention blocks in the UNet-like
diffusion models, enabling mask-guided precise semantic control across
different prompts with attention sharing for multi-prompt video generation.
Based on our careful design, the video generated by DiTCtrl achieves smooth
transitions and consistent object motion given multiple sequential prompts
without additional training. Besides, we also present MPVBench, a new benchmark
specially designed for multi-prompt video generation to evaluate the
performance of multi-prompt generation. Extensive experiments demonstrate that
our method achieves state-of-the-art performance without additional training.
| 19 |
676b9b886fb4876383b8597d
| null | null |
|
2024-12-24T23:37:35.368000 |
Fourier Position Embedding: Enhancing Attention's Periodic Extension for Length Generalization
| 26 |
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| true | null |
2412.17739
|
[
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{
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"name": "Che Jiang",
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},
{
"_id": "676a6844bee647b8c004f46b",
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"name": "Xingtai Lv",
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},
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},
{
"_id": "676a6844bee647b8c004f46d",
"hidden": false,
"name": "Ning Ding",
"status": "claimed_verified",
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"_id": "60cf4bcb1ce3775ebb86e5d5",
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"fullname": "Ning Ding",
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"user": "stingning"
}
},
{
"_id": "676a6844bee647b8c004f46e",
"hidden": false,
"name": "Youbang Sun",
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"statusLastChangedAt": null,
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},
{
"_id": "676a6844bee647b8c004f46f",
"hidden": false,
"name": "Biqing Qi",
"status": null,
"statusLastChangedAt": null,
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},
{
"_id": "676a6844bee647b8c004f470",
"hidden": false,
"name": "Yuchen Fan",
"status": null,
"statusLastChangedAt": null,
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},
{
"_id": "676a6844bee647b8c004f471",
"hidden": false,
"name": "Xue Kai Zhu",
"status": null,
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},
{
"_id": "676a6844bee647b8c004f472",
"hidden": false,
"name": "Bowen Zhou",
"status": null,
"statusLastChangedAt": null,
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}
] | 2024-12-23T17:44:01 |
Fourier Position Embedding: Enhancing Attention's Periodic Extension for
Length Generalization
|
Extending the context length of Language Models (LMs) by improving Rotary
Position Embedding (RoPE) has become a trend. While existing works mainly
address RoPE's limitations within attention mechanism, this paper provides an
analysis across nearly all parts of LMs, uncovering their adverse effects on
length generalization for RoPE-based attention. Using Discrete Signal
Processing theory, we show that RoPE enables periodic attention by implicitly
achieving Non-Uniform Discrete Fourier Transform. However, this periodicity is
undermined by the spectral damage caused by: 1) linear layers and activation
functions outside of attention; 2) insufficiently trained frequency components
brought by time-domain truncation. Building on our observations, we propose
Fourier Position Embedding (FoPE), which enhances attention's frequency-domain
properties to improve both its periodic extension and length generalization.
FoPE constructs Fourier Series and zero-outs the destructive frequency
components, increasing model robustness against the spectrum damage.
Experiments across various model scales show that, within varying context
windows, FoPE can maintain a more stable perplexity and a more consistent
accuracy in a needle-in-haystack task compared to RoPE and ALiBi. Several
analyses and ablations bring further support to our method and theoretical
modeling.
| 41 |
676a6845bee647b8c004f51c
| null | null |
|
2024-12-24T22:42:12.778000 |
ReMoE: Fully Differentiable Mixture-of-Experts with ReLU Routing
| 2 |
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| false | null |
2412.14711
|
[
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"name": "Jianfei Chen",
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{
"_id": "676a25362d7ae887c4f20b6f",
"hidden": false,
"name": "Jun Zhu",
"status": null,
"statusLastChangedAt": null,
"user": null
}
] | 2024-12-19T10:21:20 |
ReMoE: Fully Differentiable Mixture-of-Experts with ReLU Routing
|
Sparsely activated Mixture-of-Experts (MoE) models are widely adopted to
scale up model capacity without increasing the computation budget. However,
vanilla TopK routers are trained in a discontinuous, non-differentiable way,
limiting their performance and scalability. To address this issue, we propose
ReMoE, a fully differentiable MoE architecture that offers a simple yet
effective drop-in replacement for the conventional TopK+Softmax routing,
utilizing ReLU as the router instead. We further propose methods to regulate
the router's sparsity while balancing the load among experts. ReMoE's
continuous nature enables efficient dynamic allocation of computation across
tokens and layers, while also exhibiting domain specialization. Our experiments
demonstrate that ReMoE consistently outperforms vanilla TopK-routed MoE across
various model sizes, expert counts, and levels of granularity. Furthermore,
ReMoE exhibits superior scalability with respect to the number of experts,
surpassing traditional MoE architectures. The implementation based on
Megatron-LM is available at https://github.com/thu-ml/ReMoE.
| 16 |
676a25372d7ae887c4f20bb3
| null | null |
|
2024-12-24T22:37:30.445000 |
DepthLab: From Partial to Complete
| 2 |
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"fullname": "Zhiheng Liu",
"isHf": false,
"isMod": false,
"isPro": false,
"name": "Johanan0528",
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}
| false |
[
"https://cdn-uploads.huggingface.co/production/uploads/6479925ab77e18dbf640bd67/kJ_cJvqOflDjH6dllp3Xe.mp4"
] |
2412.18153
|
[
{
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"hidden": false,
"name": "Zhiheng Liu",
"status": null,
"statusLastChangedAt": null,
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},
{
"_id": "676b7d07d886f8125a4fb856",
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"name": "Ka Leong Cheng",
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{
"_id": "676b7d07d886f8125a4fb857",
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"name": "Qiuyu Wang",
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},
{
"_id": "676b7d07d886f8125a4fb859",
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"name": "Hao Ouyang",
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},
{
"_id": "676b7d07d886f8125a4fb85a",
"hidden": false,
"name": "Bin Tan",
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"statusLastChangedAt": null,
"user": null
},
{
"_id": "676b7d07d886f8125a4fb85b",
"hidden": false,
"name": "Kai Zhu",
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},
{
"_id": "676b7d07d886f8125a4fb85c",
"hidden": false,
"name": "Yujun Shen",
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"statusLastChangedAt": null,
"user": null
},
{
"_id": "676b7d07d886f8125a4fb85d",
"hidden": false,
"name": "Qifeng Chen",
"status": null,
"statusLastChangedAt": null,
"user": null
},
{
"_id": "676b7d07d886f8125a4fb85e",
"hidden": false,
"name": "Ping Luo",
"status": null,
"statusLastChangedAt": null,
"user": null
}
] | 2024-12-24T04:16:38 |
DepthLab: From Partial to Complete
|
Missing values remain a common challenge for depth data across its wide range
of applications, stemming from various causes like incomplete data acquisition
and perspective alteration. This work bridges this gap with DepthLab, a
foundation depth inpainting model powered by image diffusion priors. Our model
features two notable strengths: (1) it demonstrates resilience to
depth-deficient regions, providing reliable completion for both continuous
areas and isolated points, and (2) it faithfully preserves scale consistency
with the conditioned known depth when filling in missing values. Drawing on
these advantages, our approach proves its worth in various downstream tasks,
including 3D scene inpainting, text-to-3D scene generation, sparse-view
reconstruction with DUST3R, and LiDAR depth completion, exceeding current
solutions in both numerical performance and visual quality. Our project page
with source code is available at https://johanan528.github.io/depthlab_web/.
| 34 |
676b7d0bd886f8125a4fb983
| null | null |
|
2024-12-24T22:25:12.161000 |
SKETCH: Structured Knowledge Enhanced Text Comprehension for Holistic Retrieval
| 2 |
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}
| true | null |
2412.15443
|
[
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"_id": "676b765f038795095f73b558",
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"name": "Aman Chadha",
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"_id": "676b765f038795095f73b559",
"hidden": false,
"name": "Vinija Jain",
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},
{
"_id": "676b765f038795095f73b55a",
"hidden": false,
"name": "Divya Chaudhary",
"status": null,
"statusLastChangedAt": null,
"user": null
}
] | 2024-12-19T22:51:56 |
SKETCH: Structured Knowledge Enhanced Text Comprehension for Holistic
Retrieval
|
Retrieval-Augmented Generation (RAG) systems have become pivotal in
leveraging vast corpora to generate informed and contextually relevant
responses, notably reducing hallucinations in Large Language Models. Despite
significant advancements, these systems struggle to efficiently process and
retrieve information from large datasets while maintaining a comprehensive
understanding of the context. This paper introduces SKETCH, a novel methodology
that enhances the RAG retrieval process by integrating semantic text retrieval
with knowledge graphs, thereby merging structured and unstructured data for a
more holistic comprehension. SKETCH, demonstrates substantial improvements in
retrieval performance and maintains superior context integrity compared to
traditional methods. Evaluated across four diverse datasets: QuALITY, QASPER,
NarrativeQA, and Italian Cuisine-SKETCH consistently outperforms baseline
approaches on key RAGAS metrics such as answer_relevancy, faithfulness,
context_precision and context_recall. Notably, on the Italian Cuisine dataset,
SKETCH achieved an answer relevancy of 0.94 and a context precision of 0.99,
representing the highest performance across all evaluated metrics. These
results highlight SKETCH's capability in delivering more accurate and
contextually relevant responses, setting new benchmarks for future retrieval
systems.
| 9 |
676b7660038795095f73b583
| null | null |
|
2024-12-24T07:42:01.751000 |
ResearchTown: Simulator of Human Research Community
| 2 |
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"fullname": "Haofei Yu",
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"isMod": false,
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"name": "lwaekfjlk",
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}
| false |
[
"https://cdn-uploads.huggingface.co/production/uploads/636453547cf2c0b4f0a3ee1e/SHXI2ZTH3G7RYb7lG8UhL.png"
] |
2412.17767
|
[
{
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"name": "Haofei Yu",
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{
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"hidden": false,
"name": "Zhaochen Hong",
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},
{
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"hidden": false,
"name": "Zirui Cheng",
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},
{
"_id": "676aaa45d4000ace4575110f",
"hidden": false,
"name": "Kunlun Zhu",
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},
{
"_id": "676aaa45d4000ace45751110",
"hidden": false,
"name": "Keyang Xuan",
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},
{
"_id": "676aaa45d4000ace45751111",
"hidden": false,
"name": "Jinwei Yao",
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"statusLastChangedAt": null,
"user": null
},
{
"_id": "676aaa45d4000ace45751112",
"hidden": false,
"name": "Tao Feng",
"status": null,
"statusLastChangedAt": null,
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},
{
"_id": "676aaa45d4000ace45751113",
"hidden": false,
"name": "Jiaxuan You",
"status": null,
"statusLastChangedAt": null,
"user": null
}
] | 2024-12-23T18:26:53 |
ResearchTown: Simulator of Human Research Community
|
Large Language Models (LLMs) have demonstrated remarkable potential in
scientific domains, yet a fundamental question remains unanswered: Can we
simulate human research communities with LLMs? Addressing this question can
deepen our understanding of the processes behind idea brainstorming and inspire
the automatic discovery of novel scientific insights. In this work, we propose
ResearchTown, a multi-agent framework for research community simulation. Within
this framework, the human research community is simplified and modeled as an
agent-data graph, where researchers and papers are represented as agent-type
and data-type nodes, respectively, and connected based on their collaboration
relationships. We also introduce TextGNN, a text-based inference framework that
models various research activities (e.g., paper reading, paper writing, and
review writing) as special forms of a unified message-passing process on the
agent-data graph. To evaluate the quality of the research simulation, we
present ResearchBench, a benchmark that uses a node-masking prediction task for
scalable and objective assessment based on similarity. Our experiments reveal
three key findings: (1) ResearchTown can provide a realistic simulation of
collaborative research activities, including paper writing and review writing;
(2) ResearchTown can maintain robust simulation with multiple researchers and
diverse papers; (3) ResearchTown can generate interdisciplinary research ideas
that potentially inspire novel research directions.
| 14 |
676aaa46d4000ace457511b2
| null | null |
|
2024-12-24T05:04:23.303000 |
OpenRFT: Adapting Reasoning Foundation Model for Domain-specific Tasks with Reinforcement Fine-Tuning
| 2 |
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| false | null |
2412.16849
|
[
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},
{
"_id": "676a85a619a646a97e21e8e7",
"hidden": false,
"name": "Jiangming Shu",
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},
{
"_id": "676a85a619a646a97e21e8e8",
"hidden": false,
"name": "Yuhang Wang",
"status": "claimed_verified",
"statusLastChangedAt": "2025-01-14T08:30:16.733Z",
"user": {
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},
{
"_id": "676a85a619a646a97e21e8e9",
"hidden": false,
"name": "Jinlin Xiao",
"status": null,
"statusLastChangedAt": null,
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},
{
"_id": "676a85a619a646a97e21e8ea",
"hidden": false,
"name": "Jitao Sang",
"status": null,
"statusLastChangedAt": null,
"user": null
}
] | 2024-12-22T04:21:30 |
OpenRFT: Adapting Reasoning Foundation Model for Domain-specific Tasks
with Reinforcement Fine-Tuning
|
OpenAI's recent introduction of Reinforcement Fine-Tuning (RFT) showcases the
potential of reasoning foundation model and offers a new paradigm for
fine-tuning beyond simple pattern imitation. This technical report presents
OpenRFT, our attempt to fine-tune generalist reasoning models for
domain-specific tasks under the same settings as RFT. OpenRFT addresses two key
challenges of lacking reasoning step data and the limited quantity of training
samples, by leveraging the domain-specific samples in three ways: question
augmentation, synthesizing reasoning-process data, and few-shot ICL. The
evaluation is conducted on SciKnowEval, where OpenRFT achieves notable
performance gains with only 100 domain-specific samples for each task. More
experimental results will be updated continuously in later versions. Source
codes, datasets, and models are disclosed at:
https://github.com/ADaM-BJTU/OpenRFT
| 9 |
676a85a719a646a97e21e92d
| null | null |
|
2024-12-24T04:45:51.603000 |
PC Agent: While You Sleep, AI Works -- A Cognitive Journey into Digital World
| 2 |
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"fullname": "Run-Ze Fan",
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| true | null |
2412.17589
|
[
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"name": "Yanheng He",
"status": "claimed_verified",
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},
{
"_id": "676a8186b88f71d971444401",
"hidden": false,
"name": "Jiahe Jin",
"status": null,
"statusLastChangedAt": null,
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},
{
"_id": "676a8186b88f71d971444402",
"hidden": false,
"name": "Shijie Xia",
"status": "claimed_verified",
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},
{
"_id": "676a8186b88f71d971444403",
"hidden": false,
"name": "Jiadi Su",
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"statusLastChangedAt": null,
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},
{
"_id": "676a8186b88f71d971444404",
"hidden": false,
"name": "Runze Fan",
"status": "claimed_verified",
"statusLastChangedAt": "2024-12-30T19:34:41.541Z",
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"_id": "616bfc2b40e2f69baa1c7add",
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"type": "user",
"user": "Vfrz"
}
},
{
"_id": "676a8186b88f71d971444405",
"hidden": false,
"name": "Haoyang Zou",
"status": null,
"statusLastChangedAt": null,
"user": null
},
{
"_id": "676a8186b88f71d971444406",
"hidden": false,
"name": "Xiangkun Hu",
"status": null,
"statusLastChangedAt": null,
"user": null
},
{
"_id": "676a8186b88f71d971444407",
"hidden": false,
"name": "Pengfei Liu",
"status": null,
"statusLastChangedAt": null,
"user": null
}
] | 2024-12-23T14:02:12 |
PC Agent: While You Sleep, AI Works -- A Cognitive Journey into Digital
World
|
Imagine a world where AI can handle your work while you sleep - organizing
your research materials, drafting a report, or creating a presentation you need
for tomorrow. However, while current digital agents can perform simple tasks,
they are far from capable of handling the complex real-world work that humans
routinely perform. We present PC Agent, an AI system that demonstrates a
crucial step toward this vision through human cognition transfer. Our key
insight is that the path from executing simple "tasks" to handling complex
"work" lies in efficiently capturing and learning from human cognitive
processes during computer use. To validate this hypothesis, we introduce three
key innovations: (1) PC Tracker, a lightweight infrastructure that efficiently
collects high-quality human-computer interaction trajectories with complete
cognitive context; (2) a two-stage cognition completion pipeline that
transforms raw interaction data into rich cognitive trajectories by completing
action semantics and thought processes; and (3) a multi-agent system combining
a planning agent for decision-making with a grounding agent for robust visual
grounding. Our preliminary experiments in PowerPoint presentation creation
reveal that complex digital work capabilities can be achieved with a small
amount of high-quality cognitive data - PC Agent, trained on just 133 cognitive
trajectories, can handle sophisticated work scenarios involving up to 50 steps
across multiple applications. This demonstrates the data efficiency of our
approach, highlighting that the key to training capable digital agents lies in
collecting human cognitive data. By open-sourcing our complete framework,
including the data collection infrastructure and cognition completion methods,
we aim to lower the barriers for the research community to develop truly
capable digital agents.
| 12 |
676a8188b88f71d971444477
| null | null |
|
2024-12-24T04:00:22.597000 |
Agent-SafetyBench: Evaluating the Safety of LLM Agents
| 2 |
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| true | null |
2412.14470
|
[
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},
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"name": "Shiyao Cui",
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},
{
"_id": "676a77cf5d76485cb34417b5",
"hidden": false,
"name": "Yida Lu",
"status": null,
"statusLastChangedAt": null,
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},
{
"_id": "676a77cf5d76485cb34417b6",
"hidden": false,
"name": "Jingzhuo Zhou",
"status": null,
"statusLastChangedAt": null,
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},
{
"_id": "676a77cf5d76485cb34417b7",
"hidden": false,
"name": "Junxiao Yang",
"status": null,
"statusLastChangedAt": null,
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},
{
"_id": "676a77cf5d76485cb34417b8",
"hidden": false,
"name": "Hongning Wang",
"status": null,
"statusLastChangedAt": null,
"user": null
},
{
"_id": "676a77cf5d76485cb34417b9",
"hidden": false,
"name": "Minlie Huang",
"status": null,
"statusLastChangedAt": null,
"user": null
}
] | 2024-12-19T02:35:15 |
Agent-SafetyBench: Evaluating the Safety of LLM Agents
|
As large language models (LLMs) are increasingly deployed as agents, their
integration into interactive environments and tool use introduce new safety
challenges beyond those associated with the models themselves. However, the
absence of comprehensive benchmarks for evaluating agent safety presents a
significant barrier to effective assessment and further improvement. In this
paper, we introduce Agent-SafetyBench, a comprehensive benchmark designed to
evaluate the safety of LLM agents. Agent-SafetyBench encompasses 349
interaction environments and 2,000 test cases, evaluating 8 categories of
safety risks and covering 10 common failure modes frequently encountered in
unsafe interactions. Our evaluation of 16 popular LLM agents reveals a
concerning result: none of the agents achieves a safety score above 60%. This
highlights significant safety challenges in LLM agents and underscores the
considerable need for improvement. Through quantitative analysis, we identify
critical failure modes and summarize two fundamental safety detects in current
LLM agents: lack of robustness and lack of risk awareness. Furthermore, our
findings suggest that reliance on defense prompts alone is insufficient to
address these safety issues, emphasizing the need for more advanced and robust
strategies. We release Agent-SafetyBench at
https://github.com/thu-coai/Agent-SafetyBench to facilitate further
research and innovation in agent safety evaluation and improvement.
| 12 |
676a77d15d76485cb3441886
| null | null |
|
2024-12-24T03:56:19.269000 |
Friends-MMC: A Dataset for Multi-modal Multi-party Conversation Understanding
| 2 |
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"isHf": false,
"isMod": false,
"isPro": false,
"name": "ColorfulAI",
"type": "user"
}
| true | null |
2412.17295
|
[
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"name": "Yueqian Wang",
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{
"_id": "676a3878eabbef01bb66f7fb",
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"name": "Jianxin Liang",
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},
{
"_id": "676a3878eabbef01bb66f7fc",
"hidden": false,
"name": "Qun Liu",
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{
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"hidden": false,
"name": "Dongyan Zhao",
"status": null,
"statusLastChangedAt": null,
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}
] | 2024-12-23T05:32:48 |
Friends-MMC: A Dataset for Multi-modal Multi-party Conversation
Understanding
|
Multi-modal multi-party conversation (MMC) is a less studied yet important
topic of research due to that it well fits real-world scenarios and thus
potentially has more widely-used applications. Compared with the traditional
multi-modal conversations, MMC requires stronger character-centered
understanding abilities as there are many interlocutors appearing in both the
visual and textual context. To facilitate the study of this problem, we present
Friends-MMC in this paper, an MMC dataset that contains 24,000+ unique
utterances paired with video context. To explore the character-centered
understanding of the dialogue, we also annotate the speaker of each utterance,
the names and bounding bboxes of faces that appear in the video. Based on this
Friends-MMC dataset, we further study two fundamental MMC tasks: conversation
speaker identification and conversation response prediction, both of which have
the multi-party nature with the video or image as visual context. For
conversation speaker identification, we demonstrate the inefficiencies of
existing methods such as pre-trained models, and propose a simple yet effective
baseline method that leverages an optimization solver to utilize the context of
two modalities to achieve better performance. For conversation response
prediction, we fine-tune generative dialogue models on Friend-MMC, and analyze
the benefits of speaker information. The code and dataset is publicly available
at https://github.com/yellow-binary-tree/Friends-MMC and thus we call for more
attention on modeling speaker information when understanding conversations.
| 9 |
676a387eeabbef01bb66ff15
| null | null |
|
2024-12-24T02:25:15.795000 |
LearnLM: Improving Gemini for Learning
| 2 |
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2412.16429
|
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{
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"name": "Divya Pandya",
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"name": "Filip Bar",
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{
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"name": "Garth Graham",
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"name": "Holger Winnemoeller",
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"_id": "676a61bda89cd26e3da3149e",
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"_id": "676a61bda89cd26e3da3149f",
"hidden": false,
"name": "Prateek Kolhar",
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"name": "Renee Schneider",
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},
{
"_id": "676a61bda89cd26e3da314a1",
"hidden": false,
"name": "Shaojian Zhu",
"status": null,
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},
{
"_id": "676a61bda89cd26e3da314a2",
"hidden": false,
"name": "Stephanie Chan",
"status": null,
"statusLastChangedAt": null,
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},
{
"_id": "676a61bda89cd26e3da314a3",
"hidden": false,
"name": "Steve Yadlowsky",
"status": null,
"statusLastChangedAt": null,
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},
{
"_id": "676a61bda89cd26e3da314a4",
"hidden": false,
"name": "Viknesh Sounderajah",
"status": null,
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},
{
"_id": "676a61bda89cd26e3da314a5",
"hidden": false,
"name": "Yannis Assael",
"status": null,
"statusLastChangedAt": null,
"user": null
}
] | 2024-12-21T01:34:05 |
LearnLM: Improving Gemini for Learning
|
Today's generative AI systems are tuned to present information by default
rather than engage users in service of learning as a human tutor would. To
address the wide range of potential education use cases for these systems, we
reframe the challenge of injecting pedagogical behavior as one of
pedagogical instruction following, where training and evaluation
examples include system-level instructions describing the specific pedagogy
attributes present or desired in subsequent model turns. This framing avoids
committing our models to any particular definition of pedagogy, and instead
allows teachers or developers to specify desired model behavior. It also clears
a path to improving Gemini models for learning -- by enabling the addition of
our pedagogical data to post-training mixtures -- alongside their rapidly
expanding set of capabilities. Both represent important changes from our
initial tech report. We show how training with pedagogical instruction
following produces a LearnLM model (available on Google AI Studio) that is
preferred substantially by expert raters across a diverse set of learning
scenarios, with average preference strengths of 31\% over GPT-4o, 11\% over
Claude 3.5, and 13\% over the Gemini 1.5 Pro model LearnLM was based on.
| 22 |
676a61bfa89cd26e3da3150e
| null | null |
|
2024-12-24T02:18:55.342000 |
DRT-o1: Optimized Deep Reasoning Translation via Long Chain-of-Thought
| 4 |
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}
| true | null |
2412.17498
|
[
{
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{
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"hidden": false,
"name": "Jie Zhou",
"status": null,
"statusLastChangedAt": null,
"user": null
}
] | 2024-12-23T11:55:33 |
DRT-o1: Optimized Deep Reasoning Translation via Long Chain-of-Thought
|
Recently, O1-like models have emerged as representative examples,
illustrating the effectiveness of long chain-of-thought (CoT) in reasoning
tasks such as math and coding tasks. In this paper, we introduce DRT-o1, an
attempt to bring the success of long CoT to neural machine translation (MT).
Specifically, in view of the literature books that might involve similes and
metaphors, translating these texts to a target language is very difficult in
practice due to cultural differences. In such cases, literal translation often
fails to convey the intended meaning effectively. Even for professional human
translators, considerable thought must be given to preserving semantics
throughout the translation process. To simulate LLMs' long thought ability in
MT, we first mine sentences containing similes or metaphors from existing
literature books, and then develop a multi-agent framework to translate these
sentences via long thought. In the multi-agent framework, a translator is used
to iteratively translate the source sentence under the suggestions provided by
an advisor. To ensure the effectiveness of the long thoughts, an evaluator is
also employed to judge whether the translation in the current round is better
than the previous one or not. In this manner, we collect tens of thousands of
long-thought MT data, which is used to train our DRT-o1. The experimental
results on literature translation demonstrate the effectiveness of the DRT-o1.
Using Qwen2.5-7B and Qwen2.5-14B as the backbones, the improvement brought by
DRT-o1 achieves 7.33~8.26 BLEU and 1.66~3.36 CometScore. Besides, DRT-o1-7B can
outperform QwQ-32B-Preview by 7.82 BLEU and 1.46 CometScore, showing its
effectiveness. The project is available at https://github.com/krystalan/DRT-o1
| 22 |
676a1e90acedf3baab442c22
| null | null |
|
2024-12-24T02:15:11.940000 |
OpenAI o1 System Card
| 2 |
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| false | null |
2412.16720
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"status": null,
"statusLastChangedAt": null,
"user": null
},
{
"_id": "676a5f1127c8341daf37c941",
"hidden": false,
"name": "Yuchen Zhang",
"status": null,
"statusLastChangedAt": null,
"user": null
},
{
"_id": "676a5f1127c8341daf37c942",
"hidden": false,
"name": "Yunyun Wang",
"status": null,
"statusLastChangedAt": null,
"user": null
},
{
"_id": "676a5f1127c8341daf37c943",
"hidden": false,
"name": "Zheng Shao",
"status": null,
"statusLastChangedAt": null,
"user": null
},
{
"_id": "676a5f1127c8341daf37c944",
"hidden": false,
"name": "Zhuohan Li",
"status": null,
"statusLastChangedAt": null,
"user": null
}
] | 2024-12-21T18:04:31 |
OpenAI o1 System Card
|
The o1 model series is trained with large-scale reinforcement learning to
reason using chain of thought. These advanced reasoning capabilities provide
new avenues for improving the safety and robustness of our models. In
particular, our models can reason about our safety policies in context when
responding to potentially unsafe prompts, through deliberative alignment. This
leads to state-of-the-art performance on certain benchmarks for risks such as
generating illicit advice, choosing stereotyped responses, and succumbing to
known jailbreaks. Training models to incorporate a chain of thought before
answering has the potential to unlock substantial benefits, while also
increasing potential risks that stem from heightened intelligence. Our results
underscore the need for building robust alignment methods, extensively
stress-testing their efficacy, and maintaining meticulous risk management
protocols. This report outlines the safety work carried out for the OpenAI o1
and OpenAI o1-mini models, including safety evaluations, external red teaming,
and Preparedness Framework evaluations.
| 31 |
676a5f1427c8341daf37c9c1
| null | null |
|
2024-12-24T02:03:44.880000 |
Large Motion Video Autoencoding with Cross-modal Video VAE
| 3 |
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"https://cdn-uploads.huggingface.co/production/uploads/630231bf7e137e3d6b3b0645/TYdqz6KUOeiRZ2ZAYaiyq.mp4"
] |
2412.17805
|
[
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"name": "Yazhou Xing",
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{
"_id": "676a5396207235e4b020dfde",
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"name": "Jiaxin Xie",
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},
{
"_id": "676a5396207235e4b020dfdf",
"hidden": false,
"name": "Xiaowei Chi",
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},
{
"_id": "676a5396207235e4b020dfe0",
"hidden": false,
"name": "Qifeng Chen",
"status": null,
"statusLastChangedAt": null,
"user": null
}
] | 2024-12-23T18:58:24 |
Large Motion Video Autoencoding with Cross-modal Video VAE
|
Learning a robust video Variational Autoencoder (VAE) is essential for
reducing video redundancy and facilitating efficient video generation. Directly
applying image VAEs to individual frames in isolation can result in temporal
inconsistencies and suboptimal compression rates due to a lack of temporal
compression. Existing Video VAEs have begun to address temporal compression;
however, they often suffer from inadequate reconstruction performance. In this
paper, we present a novel and powerful video autoencoder capable of
high-fidelity video encoding. First, we observe that entangling spatial and
temporal compression by merely extending the image VAE to a 3D VAE can
introduce motion blur and detail distortion artifacts. Thus, we propose
temporal-aware spatial compression to better encode and decode the spatial
information. Additionally, we integrate a lightweight motion compression model
for further temporal compression. Second, we propose to leverage the textual
information inherent in text-to-video datasets and incorporate text guidance
into our model. This significantly enhances reconstruction quality,
particularly in terms of detail preservation and temporal stability. Third, we
further improve the versatility of our model through joint training on both
images and videos, which not only enhances reconstruction quality but also
enables the model to perform both image and video autoencoding. Extensive
evaluations against strong recent baselines demonstrate the superior
performance of our method. The project website can be found
at~https://yzxing87.github.io/vae/{https://yzxing87.github.io/vae/}.
| 24 |
676a5398207235e4b020e097
| null | null |
|
2024-12-24T00:41:11.109000 |
Outcome-Refining Process Supervision for Code Generation
| 2 |
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| true | null |
2412.15118
|
[
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},
{
"_id": "676a444d9315022860ac1f10",
"hidden": false,
"name": "Yidong Wang",
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},
{
"_id": "676a444d9315022860ac1f11",
"hidden": false,
"name": "Zhengran Zeng",
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},
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},
{
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"hidden": false,
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},
{
"_id": "676a444d9315022860ac1f14",
"hidden": false,
"name": "Shikun Zhang",
"status": null,
"statusLastChangedAt": null,
"user": null
}
] | 2024-12-19T17:59:42 |
Outcome-Refining Process Supervision for Code Generation
|
Large Language Models have demonstrated remarkable capabilities in code
generation, yet they often struggle with complex programming tasks that require
deep algorithmic reasoning. While process supervision through learned reward
models shows promise in guiding reasoning steps, it requires expensive training
data and suffers from unreliable evaluation. We propose Outcome-Refining
Process Supervision, a novel paradigm that treats outcome refinement itself as
the process to be supervised. Our framework leverages concrete execution
signals to ground the supervision of reasoning steps, while using
tree-structured exploration to maintain multiple solution trajectories
simultaneously. Experiments demonstrate that our approach enables even smaller
models to achieve high success accuracy and performance metrics on competitive
programming tasks, creates more reliable verification than traditional reward
models without requiring training PRMs. Our approach achieves significant
improvements across 5 models and 3 datasets: an average of 26.9% increase in
correctness and 42.2% in efficiency. The results suggest that providing
structured reasoning space with concrete verification signals is crucial for
solving complex programming tasks. We open-source all our code and data at:
https://github.com/zhuohaoyu/ORPS
| 19 |
676a444f9315022860ac1f70
| null | null |
|
2024-12-24T00:05:04.046000 |
Deliberation in Latent Space via Differentiable Cache Augmentation
| 5 |
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| false | null |
2412.17747
|
[
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},
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},
{
"_id": "676a3ec0b91a321e164a8782",
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},
{
"_id": "676a3ec0b91a321e164a8783",
"hidden": false,
"name": "Jun Xie",
"status": null,
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},
{
"_id": "676a3ec0b91a321e164a8784",
"hidden": false,
"name": "Arthur Szlam",
"status": null,
"statusLastChangedAt": null,
"user": null
}
] | 2024-12-23T18:02:25 |
Deliberation in Latent Space via Differentiable Cache Augmentation
|
Techniques enabling large language models (LLMs) to "think more" by
generating and attending to intermediate reasoning steps have shown promise in
solving complex problems. However, the standard approaches generate sequences
of discrete tokens immediately before responding, and so they can incur
significant latency costs and be challenging to optimize. In this work, we
demonstrate that a frozen LLM can be augmented with an offline coprocessor that
operates on the model's key-value (kv) cache. This coprocessor augments the
cache with a set of latent embeddings designed to improve the fidelity of
subsequent decoding. We train this coprocessor using the language modeling loss
from the decoder on standard pretraining data, while keeping the decoder itself
frozen. This approach enables the model to learn, in an end-to-end
differentiable fashion, how to distill additional computation into its
kv-cache. Because the decoder remains unchanged, the coprocessor can operate
offline and asynchronously, and the language model can function normally if the
coprocessor is unavailable or if a given cache is deemed not to require extra
computation. We show experimentally that when a cache is augmented, the decoder
achieves lower perplexity on numerous subsequent tokens. Furthermore, even
without any task-specific training, our experiments demonstrate that cache
augmentation consistently reduces perplexity and improves performance across a
range of reasoning-intensive tasks.
| 30 |
676a3ec1b91a321e164a87ca
| null | null |
|
2024-12-23T23:23:30.988000 |
Distilled Decoding 1: One-step Sampling of Image Auto-regressive Models with Flow Matching
| 2 |
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| true | null |
2412.17153
|
[
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"_id": "676a3070b1618113354d99fc",
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},
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}
] | 2024-12-22T20:21:54 |
Distilled Decoding 1: One-step Sampling of Image Auto-regressive Models
with Flow Matching
|
Autoregressive (AR) models have achieved state-of-the-art performance in text
and image generation but suffer from slow generation due to the token-by-token
process. We ask an ambitious question: can a pre-trained AR model be adapted to
generate outputs in just one or two steps? If successful, this would
significantly advance the development and deployment of AR models. We notice
that existing works that try to speed up AR generation by generating multiple
tokens at once fundamentally cannot capture the output distribution due to the
conditional dependencies between tokens, limiting their effectiveness for
few-step generation. To address this, we propose Distilled Decoding (DD), which
uses flow matching to create a deterministic mapping from Gaussian distribution
to the output distribution of the pre-trained AR model. We then train a network
to distill this mapping, enabling few-step generation. DD doesn't need the
training data of the original AR model, making it more practical.We evaluate DD
on state-of-the-art image AR models and present promising results on
ImageNet-256. For VAR, which requires 10-step generation, DD enables one-step
generation (6.3times speed-up), with an acceptable increase in FID from 4.19
to 9.96. For LlamaGen, DD reduces generation from 256 steps to 1, achieving an
217.8times speed-up with a comparable FID increase from 4.11 to 11.35. In
both cases, baseline methods completely fail with FID>100. DD also excels on
text-to-image generation, reducing the generation from 256 steps to 2 for
LlamaGen with minimal FID increase from 25.70 to 28.95. As the first work to
demonstrate the possibility of one-step generation for image AR models, DD
challenges the prevailing notion that AR models are inherently slow, and opens
up new opportunities for efficient AR generation. The project website is at
https://imagination-research.github.io/distilled-decoding.
| 34 |
676a3072b1618113354d9aa1
| null | null |
|
2024-12-23T22:18:25.419000 |
NILE: Internal Consistency Alignment in Large Language Models
| 2 |
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| true | null |
2412.16686
|
[
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},
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"_id": "676a20cbeabbef01bb5f825f",
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"name": "Bowei He",
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},
{
"_id": "676a20cbeabbef01bb5f8260",
"hidden": false,
"name": "Hongru Wang",
"status": null,
"statusLastChangedAt": null,
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},
{
"_id": "676a20cbeabbef01bb5f8261",
"hidden": false,
"name": "Jingyan Zhou",
"status": null,
"statusLastChangedAt": null,
"user": null
},
{
"_id": "676a20cbeabbef01bb5f8262",
"hidden": false,
"name": "Liangyou Li",
"status": null,
"statusLastChangedAt": null,
"user": null
},
{
"_id": "676a20cbeabbef01bb5f8263",
"hidden": false,
"name": "Yasheng Wang",
"status": null,
"statusLastChangedAt": null,
"user": null
},
{
"_id": "676a20cbeabbef01bb5f8264",
"hidden": false,
"name": "Chen Ma",
"status": null,
"statusLastChangedAt": null,
"user": null
},
{
"_id": "676a20cbeabbef01bb5f8265",
"hidden": false,
"name": "Irwin King",
"status": null,
"statusLastChangedAt": null,
"user": null
}
] | 2024-12-21T16:25:16 |
NILE: Internal Consistency Alignment in Large Language Models
|
As a crucial step to enhance LLMs alignment with human intentions,
Instruction Fine-Tuning (IFT) has a high demand on dataset quality. However,
existing IFT datasets often contain knowledge that is inconsistent with LLMs'
internal knowledge learned from the pre-training phase, which can greatly
affect the efficacy of IFT. To address this issue, we introduce NILE (iNternal
consIstency aLignmEnt) framework, aimed at optimizing IFT datasets to unlock
LLMs' capability further. NILE operates by eliciting target pre-trained LLM's
internal knowledge corresponding to instruction data. The internal knowledge is
leveraged to revise the answer in IFT datasets. Additionally, we propose a
novel Internal Consistency Filtering (ICF) method to filter training samples,
ensuring its high consistency with LLM's internal knowledge. Our experiments
demonstrate that NILE-aligned IFT datasets sharply boost LLM performance across
multiple LLM ability evaluation datasets, achieving up to 66.6% gain on
Arena-Hard and 68.5% on Alpaca-Eval V2. Further analysis confirms that each
component of the NILE}framework contributes to these substantial performance
improvements, and provides compelling evidence that dataset consistency with
pre-trained internal knowledge is pivotal for maximizing LLM potential.
| 8 |
676a20cceabbef01bb5f82df
| null | null |
|
2024-12-23T22:09:53.346000 |
Diving into Self-Evolving Training for Multimodal Reasoning
| 2 |
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| false | null |
2412.17451
|
[
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},
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},
{
"_id": "676a25e38ffab02f2c91a9a0",
"hidden": false,
"name": "Xiwen Zhang",
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},
{
"_id": "676a25e38ffab02f2c91a9a1",
"hidden": false,
"name": "Fan Zhou",
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},
{
"_id": "676a25e38ffab02f2c91a9a2",
"hidden": false,
"name": "Yu Cheng",
"status": "claimed_verified",
"statusLastChangedAt": "2025-01-23T15:05:17.014Z",
"user": {
"_id": "67017abfe4d49b157ac534d9",
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"fullname": "Yu Cheng",
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"user": "ych133"
}
},
{
"_id": "676a25e38ffab02f2c91a9a3",
"hidden": false,
"name": "Junxian He",
"status": null,
"statusLastChangedAt": null,
"user": null
}
] | 2024-12-23T10:18:41 |
Diving into Self-Evolving Training for Multimodal Reasoning
|
Reasoning ability is essential for Large Multimodal Models (LMMs). In the
absence of multimodal chain-of-thought annotated data, self-evolving training,
where the model learns from its own outputs, has emerged as an effective and
scalable approach for enhancing reasoning abilities. Despite its growing usage,
a comprehensive understanding of self-evolving training, particularly in the
context of multimodal reasoning, remains limited. In this paper, we delve into
the intricacies of self-evolving training for multimodal reasoning, pinpointing
three key factors: Training Method, Reward Model, and Prompt Variation. We
systematically examine each factor and explore how various configurations
affect the training's effectiveness. Our analysis leads to a set of best
practices for each factor, aimed at optimizing multimodal reasoning.
Furthermore, we explore the Self-Evolution Dynamics during training and the
impact of automatic balancing mechanisms in boosting performance. After all the
investigations, we present a final recipe for self-evolving training in
multimodal reasoning, encapsulating these design choices into a framework we
call MSTaR (Multimodal Self-evolving Training for Reasoning), which is
universally effective for models with different sizes on various benchmarks,
e.g., surpassing the pre-evolved model significantly on 5 multimodal reasoning
benchmarks without using additional human annotations, as demonstrated on
MiniCPM-V-2.5 (8B), Phi-3.5-Vision (4B) and InternVL2 (2B). We believe this
study fills a significant gap in the understanding of self-evolving training
for multimodal reasoning and offers a robust framework for future research. Our
policy and reward models, as well as the collected data, is released to
facilitate further investigation in multimodal reasoning.
| 43 |
676a25e48ffab02f2c91a9e3
| null | null |
|
2024-12-23T22:04:28.157000 |
B-STaR: Monitoring and Balancing Exploration and Exploitation in Self-Taught Reasoners
| 2 |
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2412.17256
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{
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{
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{
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}
] | 2024-12-23T03:58:34 |
B-STaR: Monitoring and Balancing Exploration and Exploitation in
Self-Taught Reasoners
|
In the absence of extensive human-annotated data for complex reasoning tasks,
self-improvement -- where models are trained on their own outputs -- has
emerged as a primary method for enhancing performance. However, the critical
factors underlying the mechanism of these iterative self-improving methods
remain poorly understood, such as under what conditions self-improvement is
effective, and what are the bottlenecks in the current iterations. In this
work, we identify and propose methods to monitor two pivotal factors in this
iterative process: (1) the model's ability to generate sufficiently diverse
responses (exploration); and (2) the effectiveness of external rewards in
distinguishing high-quality candidates from lower-quality ones (exploitation).
Using mathematical reasoning as a case study, we begin with a quantitative
analysis to track the dynamics of exploration and exploitation, discovering
that a model's exploratory capabilities rapidly deteriorate over iterations,
and the effectiveness of exploiting external rewards diminishes as well.
Motivated by these findings, we introduce B-STaR, a Self-Taught Reasoning
framework that autonomously adjusts configurations across iterations to Balance
exploration and exploitation, thereby optimizing the self-improving
effectiveness based on the current policy model and available rewards. Our
experiments on mathematical reasoning, coding, and commonsense reasoning
demonstrate that B-STaR not only enhances the model's exploratory capabilities
throughout training but also achieves a more effective balance between
exploration and exploitation, leading to superior performance.
| 46 |
676a23c29fc612bf4a3b943b
| null | null |
|
2024-12-23T22:03:04.208000 |
RobustFT: Robust Supervised Fine-tuning for Large Language Models under Noisy Response
| 2 |
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2412.14922
|
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},
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},
{
"_id": "676a2354463437b5e1217e1a",
"hidden": false,
"name": "Ming Zhang",
"status": null,
"statusLastChangedAt": null,
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}
] | 2024-12-19T15:00:18 |
RobustFT: Robust Supervised Fine-tuning for Large Language Models under
Noisy Response
|
Supervised fine-tuning (SFT) plays a crucial role in adapting large language
models (LLMs) to specific domains or tasks. However, as demonstrated by
empirical experiments, the collected data inevitably contains noise in
practical applications, which poses significant challenges to model performance
on downstream tasks. Therefore, there is an urgent need for a noise-robust SFT
framework to enhance model capabilities in downstream tasks. To address this
challenge, we introduce a robust SFT framework (RobustFT) that performs noise
detection and relabeling on downstream task data. For noise identification, our
approach employs a multi-expert collaborative system with inference-enhanced
models to achieve superior noise detection. In the denoising phase, we utilize
a context-enhanced strategy, which incorporates the most relevant and confident
knowledge followed by careful assessment to generate reliable annotations.
Additionally, we introduce an effective data selection mechanism based on
response entropy, ensuring only high-quality samples are retained for
fine-tuning. Extensive experiments conducted on multiple LLMs across five
datasets demonstrate RobustFT's exceptional performance in noisy scenarios.
| 86 |
676a2354463437b5e1217e51
| null | null |
|
2024-12-23T21:55:42.534000 |
Revisiting In-Context Learning with Long Context Language Models
| 2 |
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2412.16926
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},
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"name": "Prakhar Gupta",
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},
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},
{
"_id": "676a211eb161811335476821",
"hidden": false,
"name": "Prateek Kolhar",
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"statusLastChangedAt": null,
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}
] | 2024-12-22T08:55:19 |
Revisiting In-Context Learning with Long Context Language Models
|
In-Context Learning (ICL) is a technique by which language models make
predictions based on examples provided in their input context. Previously,
their context window size imposed a limit on the number of examples that can be
shown, making example selection techniques crucial for identifying the
maximally effective set of examples. However, the recent advent of Long Context
Language Models (LCLMs) has significantly increased the number of examples that
can be included in context, raising an important question of whether ICL
performance in a many-shot regime is still sensitive to the method of sample
selection. To answer this, we revisit these approaches in the context of LCLMs
through extensive experiments on 18 datasets spanning 4 tasks. Surprisingly, we
observe that sophisticated example selection techniques do not yield
significant improvements over a simple random sample selection method. Instead,
we find that the advent of LCLMs has fundamentally shifted the challenge of ICL
from that of selecting the most effective examples to that of collecting
sufficient examples to fill the context window. Specifically, in certain
datasets, including all available examples does not fully utilize the context
window; however, by augmenting the examples in context with a simple data
augmentation approach, we substantially improve ICL performance by 5%.
| 30 |
676a211fb161811335476846
| null | null |
|
2024-12-23T20:23:03.222000 |
Toward Robust Hyper-Detailed Image Captioning: A Multiagent Approach and Dual Evaluation Metrics for Factuality and Coverage
| 2 |
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2412.15484
|
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},
{
"_id": "6768c08c75d8e8d042beda82",
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"name": "Trung Bui",
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{
"_id": "6768c08c75d8e8d042beda83",
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}
] | 2024-12-20T01:37:22 |
Toward Robust Hyper-Detailed Image Captioning: A Multiagent Approach and
Dual Evaluation Metrics for Factuality and Coverage
|
Multimodal large language models (MLLMs) excel at generating highly detailed
captions but often produce hallucinations. Our analysis reveals that existing
hallucination detection methods struggle with detailed captions. We attribute
this to the increasing reliance of MLLMs on their generated text, rather than
the input image, as the sequence length grows. To address this issue, we
propose a multiagent approach that leverages LLM-MLLM collaboration to correct
given captions. Additionally, we introduce an evaluation framework and a
benchmark dataset to facilitate the systematic analysis of detailed captions.
Our experiments demonstrate that our proposed evaluation method better aligns
with human judgments of factuality than existing metrics and that existing
approaches to improve the MLLM factuality may fall short in hyper-detailed
image captioning tasks. In contrast, our proposed method significantly enhances
the factual accuracy of captions, even improving those generated by GPT-4V.
Finally, we highlight a limitation of VQA-centric benchmarking by demonstrating
that an MLLM's performance on VQA benchmarks may not correlate with its ability
to generate detailed image captions.
| 15 |
6768c08d75d8e8d042bedab9
| null | null |
|
2024-12-23T14:18:33.125000 |
TRecViT: A Recurrent Video Transformer
| 3 |
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| false | null |
2412.14294
|
[
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},
{
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"name": "Joseph Heyward",
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{
"_id": "6769b72361c7635a1e1e5dc3",
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"name": "Chuhan Zhang",
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},
{
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},
{
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"name": "Artem Zholus",
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},
{
"_id": "6769b72361c7635a1e1e5dc7",
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"name": "Mahdi Karami",
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},
{
"_id": "6769b72361c7635a1e1e5dc8",
"hidden": false,
"name": "Ross Goroshin",
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},
{
"_id": "6769b72361c7635a1e1e5dc9",
"hidden": false,
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},
{
"_id": "6769b72361c7635a1e1e5dca",
"hidden": false,
"name": "Simon Osindero",
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},
{
"_id": "6769b72361c7635a1e1e5dcb",
"hidden": false,
"name": "João Carreira",
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},
{
"_id": "6769b72361c7635a1e1e5dcc",
"hidden": false,
"name": "Razvan Pascanu",
"status": null,
"statusLastChangedAt": null,
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}
] | 2024-12-18T19:44:30 |
TRecViT: A Recurrent Video Transformer
|
We propose a novel block for video modelling. It relies on a
time-space-channel factorisation with dedicated blocks for each dimension:
gated linear recurrent units (LRUs) perform information mixing over time,
self-attention layers perform mixing over space, and MLPs over channels. The
resulting architecture TRecViT performs well on sparse and dense tasks, trained
in supervised or self-supervised regimes. Notably, our model is causal and
outperforms or is on par with a pure attention model ViViT-L on large scale
video datasets (SSv2, Kinetics400), while having 3times less parameters,
12times smaller memory footprint, and 5times lower FLOPs count. Code and
checkpoints will be made available online at
https://github.com/google-deepmind/trecvit.
| 13 |
6769b72661c7635a1e1e5e85
| null | null |
|
2024-12-23T10:41:23.825000 |
Multi-LLM Text Summarization
| 2 |
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| false | null |
2412.15487
|
[
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},
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},
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"name": "Jieun Kim",
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},
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"name": "Yash Bhedaru",
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},
{
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},
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},
{
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},
{
"_id": "676984a1edea1efd81017964",
"hidden": false,
"name": "Hanieh Deilamsalehy",
"status": null,
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] | 2024-12-20T01:55:26 |
Multi-LLM Text Summarization
|
In this work, we propose a Multi-LLM summarization framework, and investigate
two different multi-LLM strategies including centralized and decentralized. Our
multi-LLM summarization framework has two fundamentally important steps at each
round of conversation: generation and evaluation. These steps are different
depending on whether our multi-LLM decentralized summarization is used or
centralized. In both our multi-LLM decentralized and centralized strategies, we
have k different LLMs that generate diverse summaries of the text. However,
during evaluation, our multi-LLM centralized summarization approach leverages a
single LLM to evaluate the summaries and select the best one whereas k LLMs are
used for decentralized multi-LLM summarization. Overall, we find that our
multi-LLM summarization approaches significantly outperform the baselines that
leverage only a single LLM by up to 3x. These results indicate the
effectiveness of multi-LLM approaches for summarization.
| 6 |
676984a2edea1efd810179ae
| null | null |
|
2024-12-23T08:39:28.353000 |
IDOL: Instant Photorealistic 3D Human Creation from a Single Image
| 2 |
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| true | null |
2412.14963
|
[
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},
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{
"_id": "67690222fbd79d33cf57d524",
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{
"_id": "67690222fbd79d33cf57d525",
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{
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] | 2024-12-19T15:43:05 |
IDOL: Instant Photorealistic 3D Human Creation from a Single Image
|
Creating a high-fidelity, animatable 3D full-body avatar from a single image
is a challenging task due to the diverse appearance and poses of humans and the
limited availability of high-quality training data. To achieve fast and
high-quality human reconstruction, this work rethinks the task from the
perspectives of dataset, model, and representation. First, we introduce a
large-scale HUman-centric GEnerated dataset, HuGe100K, consisting of 100K
diverse, photorealistic sets of human images. Each set contains 24-view frames
in specific human poses, generated using a pose-controllable
image-to-multi-view model. Next, leveraging the diversity in views, poses, and
appearances within HuGe100K, we develop a scalable feed-forward transformer
model to predict a 3D human Gaussian representation in a uniform space from a
given human image. This model is trained to disentangle human pose, body shape,
clothing geometry, and texture. The estimated Gaussians can be animated without
post-processing. We conduct comprehensive experiments to validate the
effectiveness of the proposed dataset and method. Our model demonstrates the
ability to efficiently reconstruct photorealistic humans at 1K resolution from
a single input image using a single GPU instantly. Additionally, it seamlessly
supports various applications, as well as shape and texture editing tasks.
| 6 |
67690226fbd79d33cf57d65a
| null | null |
|
2024-12-23T04:48:38.485000 |
LLMs Lost in Translation: M-ALERT uncovers Cross-Linguistic Safety Gaps
| 3 |
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2412.15035
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{
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] | 2024-12-19T16:46:54 |
LLMs Lost in Translation: M-ALERT uncovers Cross-Linguistic Safety Gaps
|
Building safe Large Language Models (LLMs) across multiple languages is
essential in ensuring both safe access and linguistic diversity. To this end,
we introduce M-ALERT, a multilingual benchmark that evaluates the safety of
LLMs in five languages: English, French, German, Italian, and Spanish. M-ALERT
includes 15k high-quality prompts per language, totaling 75k, following the
detailed ALERT taxonomy. Our extensive experiments on 10 state-of-the-art LLMs
highlight the importance of language-specific safety analysis, revealing that
models often exhibit significant inconsistencies in safety across languages and
categories. For instance, Llama3.2 shows high unsafety in the category
crime_tax for Italian but remains safe in other languages. Similar differences
can be observed across all models. In contrast, certain categories, such as
substance_cannabis and crime_propaganda, consistently trigger unsafe responses
across models and languages. These findings underscore the need for robust
multilingual safety practices in LLMs to ensure safe and responsible usage
across diverse user communities.
| 4 |
676931b9bc5af30a79d40ad2
| null | null |
|
2024-12-23T04:21:04.641000 |
Sequence Matters: Harnessing Video Models in 3D Super-Resolution
| 2 |
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2412.11525
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] | 2024-12-16T08:00:50 |
Sequence Matters: Harnessing Video Models in 3D Super-Resolution
|
3D super-resolution aims to reconstruct high-fidelity 3D models from
low-resolution (LR) multi-view images. Early studies primarily focused on
single-image super-resolution (SISR) models to upsample LR images into
high-resolution images. However, these methods often lack view consistency
because they operate independently on each image. Although various
post-processing techniques have been extensively explored to mitigate these
inconsistencies, they have yet to fully resolve the issues. In this paper, we
perform a comprehensive study of 3D super-resolution by leveraging video
super-resolution (VSR) models. By utilizing VSR models, we ensure a higher
degree of spatial consistency and can reference surrounding spatial
information, leading to more accurate and detailed reconstructions. Our
findings reveal that VSR models can perform remarkably well even on sequences
that lack precise spatial alignment. Given this observation, we propose a
simple yet practical approach to align LR images without involving fine-tuning
or generating 'smooth' trajectory from the trained 3D models over LR images.
The experimental results show that the surprisingly simple algorithms can
achieve the state-of-the-art results of 3D super-resolution tasks on standard
benchmark datasets, such as the NeRF-synthetic and MipNeRF-360 datasets.
Project page: https://ko-lani.github.io/Sequence-Matters
| 10 |
676687cc5b17ac358c9ff2b9
| null | null |
|
2024-12-23T02:30:57.045000 |
Fietje: An open, efficient LLM for Dutch
| 3 |
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| true | null |
2412.15450
|
[
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] | 2024-12-19T23:06:01 |
Fietje: An open, efficient LLM for Dutch
|
This paper introduces Fietje, a family of small language models (SLMs)
specifically designed for the Dutch language. The model is based on Phi 2, an
English-centric model of 2.7 billion parameters. Fietje demonstrated
competitive results with larger language models upon its release. A core
emphasis of this work is transparency and reproducibility: Fietje is fully
open-source, with model weights, datasets, training, and evaluation code all
publicly accessible.
The paper discusses the performance of Fietje and many other models on an
extensive evaluation suite of benchmarks on reasoning, sentiment analysis,
world knowledge, linguistic acceptability and word sense disambiguation.
Evaluation results illustrate the rapid progress in the field of LLMs, where
recent small models outperform older, larger models that were fine-tuned for
Dutch. This trend signals an exciting future for Dutch language processing,
suggesting that even compact LLMs are becoming increasingly capable.
Furthermore, ongoing and future efforts to adapt LLMs to Dutch are poised to
enhance these models even further, broadening their applicability and
accessibility. Fietje is only an intermediate step in improving accessibility
to language technology for users of the Dutch language.
| 4 |
676911963727573d69c04b01
| null | null |
|
2024-12-23T00:09:09.595000 |
Offline Reinforcement Learning for LLM Multi-Step Reasoning
| 6 |
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| true | null |
2412.16145
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] | 2024-12-20T18:49:45 |
Offline Reinforcement Learning for LLM Multi-Step Reasoning
|
Improving the multi-step reasoning ability of large language models (LLMs)
with offline reinforcement learning (RL) is essential for quickly adapting them
to complex tasks. While Direct Preference Optimization (DPO) has shown promise
in aligning LLMs with human preferences, it is less suitable for multi-step
reasoning tasks because (1) DPO relies on paired preference data, which is not
readily available for multi-step reasoning tasks, and (2) it treats all tokens
uniformly, making it ineffective for credit assignment in multi-step reasoning
tasks, which often come with sparse reward. In this work, we propose OREO
(Offline Reasoning Optimization), an offline RL method for enhancing LLM
multi-step reasoning. Building on insights from previous works of maximum
entropy reinforcement learning, it jointly learns a policy model and value
function by optimizing the soft Bellman Equation. We show in principle that it
reduces the need to collect pairwise data and enables better credit assignment.
Empirically, OREO surpasses existing offline learning methods on multi-step
reasoning benchmarks, including mathematical reasoning tasks (GSM8K, MATH) and
embodied agent control (ALFWorld). The approach can be extended to a
multi-iteration framework when additional resources are available. Furthermore,
the learned value function can be leveraged to guide the tree search for free,
which can further boost performance during test time.
| 38 |
6768f051bf7c0f8d9a17c53a
| null | null |
|
2024-12-22T23:39:08.449000 |
MixLLM: LLM Quantization with Global Mixed-precision between Output-features and Highly-efficient System Design
| 5 |
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| true | null |
2412.14590
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] | 2024-12-19T07:15:15 |
MixLLM: LLM Quantization with Global Mixed-precision between
Output-features and Highly-efficient System Design
|
Quantization has become one of the most effective methodologies to compress
LLMs into smaller size. However, the existing quantization solutions still show
limitations of either non-negligible accuracy drop or system inefficiency. In
this paper, we make a comprehensive analysis of the general quantization
principles on their effect to the triangle of accuracy, memory consumption and
system efficiency. We propose MixLLM that explores the new optimization space
of mixed-precision quantization between output features based on the insight
that different output features matter differently in the model. MixLLM
identifies the output features with high salience in the global view rather
than within each single layer, effectively assigning the larger bit-width to
output features that need it most to achieve good accuracy with low memory
consumption. We present the sweet spot of quantization configuration of
algorithm-system co-design that leads to high accuracy and system efficiency.
To address the system challenge, we design the two-step dequantization to make
use of the int8 Tensor Core easily and fast data type conversion to reduce
dequantization overhead significantly, and present the software pipeline to
overlap the memory access, dequantization and the MatMul to the best. Extensive
experiments show that with only 10% more bits, the PPL increasement can be
reduced from about 0.5 in SOTA to within 0.2 for Llama 3.1 70B, while on
average MMLU-Pro improves by 0.93 over the SOTA of three popular models. In
addition to its superior accuracy, MixLLM also achieves state-of-the-art system
efficiency.
| 14 |
676827d9bc5af30a798576c2
| null | null |
|
2024-12-22T22:25:16.875000 |
CLEAR: Conv-Like Linearization Revs Pre-Trained Diffusion Transformers Up
| 5 |
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2412.16112
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] | 2024-12-20T17:57:09 |
CLEAR: Conv-Like Linearization Revs Pre-Trained Diffusion Transformers
Up
|
Diffusion Transformers (DiT) have become a leading architecture in image
generation. However, the quadratic complexity of attention mechanisms, which
are responsible for modeling token-wise relationships, results in significant
latency when generating high-resolution images. To address this issue, we aim
at a linear attention mechanism in this paper that reduces the complexity of
pre-trained DiTs to linear. We begin our exploration with a comprehensive
summary of existing efficient attention mechanisms and identify four key
factors crucial for successful linearization of pre-trained DiTs: locality,
formulation consistency, high-rank attention maps, and feature integrity. Based
on these insights, we introduce a convolution-like local attention strategy
termed CLEAR, which limits feature interactions to a local window around each
query token, and thus achieves linear complexity. Our experiments indicate
that, by fine-tuning the attention layer on merely 10K self-generated samples
for 10K iterations, we can effectively transfer knowledge from a pre-trained
DiT to a student model with linear complexity, yielding results comparable to
the teacher model. Simultaneously, it reduces attention computations by 99.5%
and accelerates generation by 6.3 times for generating 8K-resolution images.
Furthermore, we investigate favorable properties in the distilled attention
layers, such as zero-shot generalization cross various models and plugins, and
improved support for multi-GPU parallel inference. Models and codes are
available here: https://github.com/Huage001/CLEAR.
| 22 |
6768d7fa97a8f966b3362bcf
| null | null |
|
2024-12-22T22:11:03.096000 |
Parallelized Autoregressive Visual Generation
| 2 |
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] |
2412.15119
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] | 2024-12-19T17:59:54 |
Parallelized Autoregressive Visual Generation
|
Autoregressive models have emerged as a powerful approach for visual
generation but suffer from slow inference speed due to their sequential
token-by-token prediction process. In this paper, we propose a simple yet
effective approach for parallelized autoregressive visual generation that
improves generation efficiency while preserving the advantages of
autoregressive modeling. Our key insight is that parallel generation depends on
visual token dependencies-tokens with weak dependencies can be generated in
parallel, while strongly dependent adjacent tokens are difficult to generate
together, as their independent sampling may lead to inconsistencies. Based on
this observation, we develop a parallel generation strategy that generates
distant tokens with weak dependencies in parallel while maintaining sequential
generation for strongly dependent local tokens. Our approach can be seamlessly
integrated into standard autoregressive models without modifying the
architecture or tokenizer. Experiments on ImageNet and UCF-101 demonstrate that
our method achieves a 3.6x speedup with comparable quality and up to 9.5x
speedup with minimal quality degradation across both image and video generation
tasks. We hope this work will inspire future research in efficient visual
generation and unified autoregressive modeling. Project page:
https://epiphqny.github.io/PAR-project.
| 51 |
6764fda210330426aecde36f
| null | null |
|
2024-12-22T21:59:13.541000 |
Taming Multimodal Joint Training for High-Quality Video-to-Audio Synthesis
| 2 |
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2412.15322
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] | 2024-12-19T18:59:55 |
Taming Multimodal Joint Training for High-Quality Video-to-Audio
Synthesis
|
We propose to synthesize high-quality and synchronized audio, given video and
optional text conditions, using a novel multimodal joint training framework
MMAudio. In contrast to single-modality training conditioned on (limited) video
data only, MMAudio is jointly trained with larger-scale, readily available
text-audio data to learn to generate semantically aligned high-quality audio
samples. Additionally, we improve audio-visual synchrony with a conditional
synchronization module that aligns video conditions with audio latents at the
frame level. Trained with a flow matching objective, MMAudio achieves new
video-to-audio state-of-the-art among public models in terms of audio quality,
semantic alignment, and audio-visual synchronization, while having a low
inference time (1.23s to generate an 8s clip) and just 157M parameters. MMAudio
also achieves surprisingly competitive performance in text-to-audio generation,
showing that joint training does not hinder single-modality performance. Code
and demo are available at: https://hkchengrex.github.io/MMAudio
| 18 |
6768d18926eb881162077079
| null | null |
|
2024-12-22T21:39:56.930000 |
SCOPE: Optimizing Key-Value Cache Compression in Long-context Generation
| 3 |
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2412.13649
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] | 2024-12-18T09:27:33 |
SCOPE: Optimizing Key-Value Cache Compression in Long-context Generation
|
Key-Value (KV) cache has become a bottleneck of LLMs for long-context
generation. Despite the numerous efforts in this area, the optimization for the
decoding phase is generally ignored. However, we believe such optimization is
crucial, especially for long-output generation tasks based on the following two
observations: (i) Excessive compression during the prefill phase, which
requires specific full context impairs the comprehension of the reasoning task;
(ii) Deviation of heavy hitters occurs in the reasoning tasks with long
outputs. Therefore, SCOPE, a simple yet efficient framework that separately
performs KV cache optimization during the prefill and decoding phases, is
introduced. Specifically, the KV cache during the prefill phase is preserved to
maintain the essential information, while a novel strategy based on sliding is
proposed to select essential heavy hitters for the decoding phase. Memory usage
and memory transfer are further optimized using adaptive and discontinuous
strategies. Extensive experiments on LongGenBench show the effectiveness and
generalization of SCOPE and its compatibility as a plug-in to other
prefill-only KV compression methods.
| 20 |
6768cd62aa9027defefa2b1b
| null | null |
|
2024-12-20T15:56:56.140000 |
AV-Link: Temporally-Aligned Diffusion Features for Cross-Modal Audio-Video Generation
| 2 |
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2412.15191
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{
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}
] | 2024-12-19T18:57:21 |
AV-Link: Temporally-Aligned Diffusion Features for Cross-Modal
Audio-Video Generation
|
We propose AV-Link, a unified framework for Video-to-Audio and Audio-to-Video
generation that leverages the activations of frozen video and audio diffusion
models for temporally-aligned cross-modal conditioning. The key to our
framework is a Fusion Block that enables bidirectional information exchange
between our backbone video and audio diffusion models through a
temporally-aligned self attention operation. Unlike prior work that uses
feature extractors pretrained for other tasks for the conditioning signal,
AV-Link can directly leverage features obtained by the complementary modality
in a single framework i.e. video features to generate audio, or audio features
to generate video. We extensively evaluate our design choices and demonstrate
the ability of our method to achieve synchronized and high-quality audiovisual
content, showcasing its potential for applications in immersive media
generation. Project Page: snap-research.github.io/AVLink/
| 5 |
6765d9f9bde4bc579f5b078b
| null | null |
|
2024-12-20T09:51:46.571000 |
PixelMan: Consistent Object Editing with Diffusion Models via Pixel Manipulation and Generation
| 4 |
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2412.14283
|
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}
] | 2024-12-18T19:24:15 |
PixelMan: Consistent Object Editing with Diffusion Models via Pixel
Manipulation and Generation
|
Recent research explores the potential of Diffusion Models (DMs) for
consistent object editing, which aims to modify object position, size, and
composition, etc., while preserving the consistency of objects and background
without changing their texture and attributes. Current inference-time methods
often rely on DDIM inversion, which inherently compromises efficiency and the
achievable consistency of edited images. Recent methods also utilize energy
guidance which iteratively updates the predicted noise and can drive the
latents away from the original image, resulting in distortions. In this paper,
we propose PixelMan, an inversion-free and training-free method for achieving
consistent object editing via Pixel Manipulation and generation, where we
directly create a duplicate copy of the source object at target location in the
pixel space, and introduce an efficient sampling approach to iteratively
harmonize the manipulated object into the target location and inpaint its
original location, while ensuring image consistency by anchoring the edited
image to be generated to the pixel-manipulated image as well as by introducing
various consistency-preserving optimization techniques during inference.
Experimental evaluations based on benchmark datasets as well as extensive
visual comparisons show that in as few as 16 inference steps, PixelMan
outperforms a range of state-of-the-art training-based and training-free
methods (usually requiring 50 steps) on multiple consistent object editing
tasks.
| 3 |
67651533e07adde9c961fce3
| null | null |
|
2024-12-20T08:57:11.189000 |
DateLogicQA: Benchmarking Temporal Biases in Large Language Models
| 2 |
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| true | null |
2412.13377
|
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{
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"status": null,
"statusLastChangedAt": null,
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}
] | 2024-12-17T23:25:47 |
DateLogicQA: Benchmarking Temporal Biases in Large Language Models
|
This paper introduces DateLogicQA, a benchmark with 190 questions covering
diverse date formats, temporal contexts, and reasoning types. We propose the
Semantic Integrity Metric to assess tokenization quality and analyse two
biases: Representation-Level Bias, affecting embeddings, and Logical-Level
Bias, influencing reasoning outputs. Our findings provide a comprehensive
evaluation of LLMs' capabilities and limitations in temporal reasoning,
highlighting key challenges in handling temporal data accurately. The GitHub
repository for our work is available at
https://github.com/gagan3012/EAIS-Temporal-Bias
| 2 |
676577aaabcd70b404ad687b
| null | null |
|
2024-12-20T05:35:43.828000 |
Move-in-2D: 2D-Conditioned Human Motion Generation
| 2 |
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| true | null |
2412.13185
|
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{
"_id": "6762c4d82faaf11234a44937",
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"name": "Yang Zhou",
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},
{
"_id": "6762c4d82faaf11234a44938",
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"name": "Jui-Hsien Wang",
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},
{
"_id": "6762c4d82faaf11234a44939",
"hidden": false,
"name": "Difan Liu",
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},
{
"_id": "6762c4d82faaf11234a4493a",
"hidden": false,
"name": "Feng Liu",
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{
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"name": "Ming-Hsuan Yang",
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},
{
"_id": "6762c4d82faaf11234a4493c",
"hidden": false,
"name": "Zhan Xu",
"status": null,
"statusLastChangedAt": null,
"user": null
}
] | 2024-12-17T18:58:07 |
Move-in-2D: 2D-Conditioned Human Motion Generation
|
Generating realistic human videos remains a challenging task, with the most
effective methods currently relying on a human motion sequence as a control
signal. Existing approaches often use existing motion extracted from other
videos, which restricts applications to specific motion types and global scene
matching. We propose Move-in-2D, a novel approach to generate human motion
sequences conditioned on a scene image, allowing for diverse motion that adapts
to different scenes. Our approach utilizes a diffusion model that accepts both
a scene image and text prompt as inputs, producing a motion sequence tailored
to the scene. To train this model, we collect a large-scale video dataset
featuring single-human activities, annotating each video with the corresponding
human motion as the target output. Experiments demonstrate that our method
effectively predicts human motion that aligns with the scene image after
projection. Furthermore, we show that the generated motion sequence improves
human motion quality in video synthesis tasks.
| 2 |
6762c4d92faaf11234a449a9
| null | null |
|
2024-12-19T23:42:38.162000 |
LeviTor: 3D Trajectory Oriented Image-to-Video Synthesis
| 3 |
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| true | null |
2412.15214
|
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{
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] | 2024-12-19T18:59:56 |
LeviTor: 3D Trajectory Oriented Image-to-Video Synthesis
|
The intuitive nature of drag-based interaction has led to its growing
adoption for controlling object trajectories in image-to-video synthesis.
Still, existing methods that perform dragging in the 2D space usually face
ambiguity when handling out-of-plane movements. In this work, we augment the
interaction with a new dimension, i.e., the depth dimension, such that users
are allowed to assign a relative depth for each point on the trajectory. That
way, our new interaction paradigm not only inherits the convenience from 2D
dragging, but facilitates trajectory control in the 3D space, broadening the
scope of creativity. We propose a pioneering method for 3D trajectory control
in image-to-video synthesis by abstracting object masks into a few cluster
points. These points, accompanied by the depth information and the instance
information, are finally fed into a video diffusion model as the control
signal. Extensive experiments validate the effectiveness of our approach,
dubbed LeviTor, in precisely manipulating the object movements when producing
photo-realistic videos from static images. Project page:
https://ppetrichor.github.io/levitor.github.io/
| 15 |
6764f549cee1fdbd9765ea31
| null | null |
|
2024-12-19T22:27:39.645000 |
AceMath: Advancing Frontier Math Reasoning with Post-Training and Reward Modeling
| 2 |
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| true | null |
2412.15084
|
[
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},
{
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"statusLastChangedAt": "2024-12-20T09:27:23.912Z",
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}
] | 2024-12-19T17:29:44 |
AceMath: Advancing Frontier Math Reasoning with Post-Training and Reward
Modeling
|
In this paper, we introduce AceMath, a suite of frontier math models that
excel in solving complex math problems, along with highly effective reward
models capable of evaluating generated solutions and reliably identifying the
correct ones. To develop the instruction-tuned math models, we propose a
supervised fine-tuning (SFT) process that first achieves competitive
performance across general domains, followed by targeted fine-tuning for the
math domain using a carefully curated set of prompts and synthetically
generated responses. The resulting model, AceMath-72B-Instruct greatly
outperforms Qwen2.5-Math-72B-Instruct, GPT-4o and Claude-3.5 Sonnet. To develop
math-specialized reward model, we first construct AceMath-RewardBench, a
comprehensive and robust benchmark for evaluating math reward models across
diverse problems and difficulty levels. After that, we present a systematic
approach to build our math reward models. The resulting model, AceMath-72B-RM,
consistently outperforms state-of-the-art reward models. Furthermore, when
combining AceMath-72B-Instruct with AceMath-72B-RM, we achieve the highest
average rm@8 score across the math reasoning benchmarks. We will release model
weights, training data, and evaluation benchmarks at:
https://research.nvidia.com/labs/adlr/acemath
| 13 |
6764e3e40afbb34519fd206d
| null | null |
|
2024-12-19T22:27:13.562000 |
Descriptive Caption Enhancement with Visual Specialists for Multimodal Perception
| 2 |
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| true | null |
2412.14233
|
[
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{
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},
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"name": "Gang Zhang",
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},
{
"_id": "6764e3c22086097d58dc7fcb",
"hidden": false,
"name": "Zechao Li",
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},
{
"_id": "6764e3c22086097d58dc7fcc",
"hidden": false,
"name": "Jingdong Wang",
"status": null,
"statusLastChangedAt": null,
"user": null
}
] | 2024-12-18T18:45:43 |
Descriptive Caption Enhancement with Visual Specialists for Multimodal
Perception
|
Training Large Multimodality Models (LMMs) relies on descriptive image
caption that connects image and language. Existing methods either distill the
caption from the LMM models or construct the captions from the internet images
or by human. We propose to leverage off-the-shelf visual specialists, which
were trained from annotated images initially not for image captioning, for
enhancing the image caption.
Our approach, named DCE, explores object low-level and fine-grained
attributes (e.g., depth, emotion and fine-grained categories) and object
relations (e.g., relative location and human-object-interaction (HOI)), and
combine the attributes into the descriptive caption. Experiments demonstrate
that such visual specialists are able to improve the performance for visual
understanding tasks as well as reasoning that benefits from more accurate
visual understanding. We will release the source code and the pipeline so that
other visual specialists are easily combined into the pipeline. The complete
source code of DCE pipeline and datasets will be available at
https://github.com/syp2ysy/DCE.
| 6 |
6764e3c32086097d58dc8000
| null | null |
|
2024-12-19T22:24:46.171000 |
DI-PCG: Diffusion-based Efficient Inverse Procedural Content Generation for High-quality 3D Asset Creation
| 2 |
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| true | null |
2412.15200
|
[
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},
{
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},
{
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},
{
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}
}
] | 2024-12-19T18:58:46 |
DI-PCG: Diffusion-based Efficient Inverse Procedural Content Generation
for High-quality 3D Asset Creation
|
Procedural Content Generation (PCG) is powerful in creating high-quality 3D
contents, yet controlling it to produce desired shapes is difficult and often
requires extensive parameter tuning. Inverse Procedural Content Generation aims
to automatically find the best parameters under the input condition. However,
existing sampling-based and neural network-based methods still suffer from
numerous sample iterations or limited controllability. In this work, we present
DI-PCG, a novel and efficient method for Inverse PCG from general image
conditions. At its core is a lightweight diffusion transformer model, where PCG
parameters are directly treated as the denoising target and the observed images
as conditions to control parameter generation. DI-PCG is efficient and
effective. With only 7.6M network parameters and 30 GPU hours to train, it
demonstrates superior performance in recovering parameters accurately, and
generalizing well to in-the-wild images. Quantitative and qualitative
experiment results validate the effectiveness of DI-PCG in inverse PCG and
image-to-3D generation tasks. DI-PCG offers a promising approach for efficient
inverse PCG and represents a valuable exploration step towards a 3D generation
path that models how to construct a 3D asset using parametric models.
| 9 |
6764da76bdc5692a8d6bcedf
| null | null |
|
2024-12-19T22:23:57.229000 |
How to Synthesize Text Data without Model Collapse?
| 4 |
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| true | null |
2412.14689
|
[
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{
"_id": "6764e1dfc51db09f8c3cd764",
"hidden": false,
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"_id": "669f614b59adf5b56e05bce3",
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] | 2024-12-19T09:43:39 |
How to Synthesize Text Data without Model Collapse?
|
Model collapse in synthetic data indicates that iterative training on
self-generated data leads to a gradual decline in performance. With the
proliferation of AI models, synthetic data will fundamentally reshape the web
data ecosystem. Future GPT-{n} models will inevitably be trained on a blend
of synthetic and human-produced data. In this paper, we focus on two questions:
what is the impact of synthetic data on language model training, and how to
synthesize data without model collapse? We first pre-train language models
across different proportions of synthetic data, revealing a negative
correlation between the proportion of synthetic data and model performance. We
further conduct statistical analysis on synthetic data to uncover
distributional shift phenomenon and over-concentration of n-gram features.
Inspired by the above findings, we propose token editing on human-produced data
to obtain semi-synthetic data. As a proof of concept, we theoretically
demonstrate that token-level editing can prevent model collapse, as the test
error is constrained by a finite upper bound. We conduct extensive experiments
on pre-training from scratch, continual pre-training, and supervised
fine-tuning. The results validate our theoretical proof that token-level
editing improves data quality and enhances model performance.
| 51 |
6764e1e0c51db09f8c3cd793
| null | null |
|
2024-12-19T22:20:23.883000 |
Affordance-Aware Object Insertion via Mask-Aware Dual Diffusion
| 2 |
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| true |
[
"https://cdn-uploads.huggingface.co/production/uploads/658bb7e47459b6e471b9d2e6/BeBx0G4iyjtUIO5RHxGdL.qt"
] |
2412.14462
|
[
{
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"name": "Jixuan He",
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"user": "Kakituken"
}
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"user": {
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}
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{
"_id": "6764e0339aeafaa0d8405e76",
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},
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"name": "Junsik Kim",
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},
{
"_id": "6764e0339aeafaa0d8405e78",
"hidden": false,
"name": "Donglai Wei",
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"_id": "628c2c8ab80bb09700d6cb1d",
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}
},
{
"_id": "6764e0339aeafaa0d8405e79",
"hidden": false,
"name": "Hanspeter Pfister",
"status": "admin_assigned",
"statusLastChangedAt": "2024-12-20T09:24:54.200Z",
"user": {
"_id": "62acc69e36f7c7b7f65fccca",
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"fullname": "Hanspeter Pfister",
"isPro": false,
"type": "user",
"user": "hpfister"
}
}
] | 2024-12-19T02:23:13 |
Affordance-Aware Object Insertion via Mask-Aware Dual Diffusion
|
As a common image editing operation, image composition involves integrating
foreground objects into background scenes. In this paper, we expand the
application of the concept of Affordance from human-centered image composition
tasks to a more general object-scene composition framework, addressing the
complex interplay between foreground objects and background scenes. Following
the principle of Affordance, we define the affordance-aware object insertion
task, which aims to seamlessly insert any object into any scene with various
position prompts. To address the limited data issue and incorporate this task,
we constructed the SAM-FB dataset, which contains over 3 million examples
across more than 3,000 object categories. Furthermore, we propose the
Mask-Aware Dual Diffusion (MADD) model, which utilizes a dual-stream
architecture to simultaneously denoise the RGB image and the insertion mask. By
explicitly modeling the insertion mask in the diffusion process, MADD
effectively facilitates the notion of affordance. Extensive experimental
results show that our method outperforms the state-of-the-art methods and
exhibits strong generalization performance on in-the-wild images. Please refer
to our code on https://github.com/KaKituken/affordance-aware-any.
| 15 |
6764e0389aeafaa0d8405f9e
| null | null |
|
2024-12-19T22:04:22.958000 |
UIP2P: Unsupervised Instruction-based Image Editing via Cycle Edit Consistency
| 3 |
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| true | null |
2412.15216
|
[
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{
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},
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},
{
"_id": "6764deae8ae9bee01173392b",
"hidden": false,
"name": "Thomas Hofmann",
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"statusLastChangedAt": "2024-12-20T09:28:42.685Z",
"user": {
"_id": "6630831ce888d89069e6276a",
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},
{
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"name": "Federico Tombari",
"status": null,
"statusLastChangedAt": null,
"user": null
}
] | 2024-12-19T18:59:58 |
UIP2P: Unsupervised Instruction-based Image Editing via Cycle Edit
Consistency
|
We propose an unsupervised model for instruction-based image editing that
eliminates the need for ground-truth edited images during training. Existing
supervised methods depend on datasets containing triplets of input image,
edited image, and edit instruction. These are generated by either existing
editing methods or human-annotations, which introduce biases and limit their
generalization ability. Our method addresses these challenges by introducing a
novel editing mechanism called Cycle Edit Consistency (CEC), which applies
forward and backward edits in one training step and enforces consistency in
image and attention spaces. This allows us to bypass the need for ground-truth
edited images and unlock training for the first time on datasets comprising
either real image-caption pairs or image-caption-edit triplets. We empirically
show that our unsupervised technique performs better across a broader range of
edits with high fidelity and precision. By eliminating the need for
pre-existing datasets of triplets, reducing biases associated with supervised
methods, and proposing CEC, our work represents a significant advancement in
unblocking scaling of instruction-based image editing.
| 5 |
6764deaf8ae9bee0117339a6
| null | null |
|
2024-12-19T22:03:19.323000 |
TOMG-Bench: Evaluating LLMs on Text-based Open Molecule Generation
| 2 |
{
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| true | null |
2412.14642
|
[
{
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"name": "Jiatong Li",
"status": "claimed_verified",
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{
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"user": {
"_id": "656ae4088fb1ddf0d5ec9ac5",
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"fullname": "Junxian Li",
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"type": "user",
"user": "Duke-de-Artois"
}
},
{
"_id": "6764de0ca246952fabef430b",
"hidden": false,
"name": "Yunqing Liu",
"status": null,
"statusLastChangedAt": null,
"user": null
},
{
"_id": "6764de0ca246952fabef430c",
"hidden": false,
"name": "Dongzhan Zhou",
"status": "admin_assigned",
"statusLastChangedAt": "2024-12-20T09:30:10.820Z",
"user": {
"_id": "6538b861613fe158bd581e35",
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"fullname": "Dongzhan Zhou",
"isPro": false,
"type": "user",
"user": "schrodingers-tiger"
}
},
{
"_id": "6764de0ca246952fabef430d",
"hidden": false,
"name": "Qing Li",
"status": null,
"statusLastChangedAt": null,
"user": null
}
] | 2024-12-19T08:51:16 |
TOMG-Bench: Evaluating LLMs on Text-based Open Molecule Generation
|
In this paper, we propose Text-based Open Molecule Generation Benchmark
(TOMG-Bench), the first benchmark to evaluate the open-domain molecule
generation capability of LLMs. TOMG-Bench encompasses a dataset of three major
tasks: molecule editing (MolEdit), molecule optimization (MolOpt), and
customized molecule generation (MolCustom). Each task further contains three
subtasks, with each subtask comprising 5,000 test samples. Given the inherent
complexity of open molecule generation, we have also developed an automated
evaluation system that helps measure both the quality and the accuracy of the
generated molecules. Our comprehensive benchmarking of 25 LLMs reveals the
current limitations and potential areas for improvement in text-guided molecule
discovery. Furthermore, with the assistance of OpenMolIns, a specialized
instruction tuning dataset proposed for solving challenges raised by
TOMG-Bench, Llama3.1-8B could outperform all the open-source general LLMs, even
surpassing GPT-3.5-turbo by 46.5\% on TOMG-Bench. Our codes and datasets are
available through https://github.com/phenixace/TOMG-Bench.
| 4 |
6764de0da246952fabef4389
| null | null |
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