llm-diff: Behavioral Regression Testing for Large Language Models
Siddh07ETH
• • 1NEW Articles from Team or organizations will get promoted to the main section.
Prompt injection detectors, measured on every attack type
mihailgribov
• • 1DAVIDAU DELETED MY POST AND BANNED ME FOR ASKING ABOUT BENCHMARKS — HERE IS THE FULL ANALYSIS (9B + 27B + 390 MODELS)
DedeProGames
• • 10Beyond the Aggregate Score: Per-Country Domain Shift in the GWHD Wheat Head Detection Model Zoo
dronefreak
• • 7Post-training NVIDIA Nemotron 3.5 Lightning for enterprise domains
lujangusface
• EAGLE-3 speculative decoding for NVIDIA Nemotron 3.5 Lightning
lujangusface
• Exploring NVIDIA Nemotron 3.5 Lightning: Making it see with little resources
tngtech
• • 13Multi-Token Prediction with NVIDIA Nemotron 3.5 Lightning on HP ZGX Nano
curtburk
• The Map: The Whole of PyTorch on One Page
akhalilli
• • 1RAG vs. Tool-Calling Agents: Which Approach Works Better for Enterprise AI?
prismberry-technologies
• Your SOC Agent's Final Answer Is Lying to You
alirezaaminzadeh
• BrowserAct and CapSolver Integration for Agent CAPTCHA Handling
CapSolverAI
• Luth-2: Pushing the French Capabilities of SLMs with MOPD
MaxLSB
• • 5SraVaani: How Vision Helps Hearing
ARTPARK-IISc
• • 2Fine-Tune SraVaani on Your Own Speech Data
ARTPARK-IISc
• • 2quantal-ternary: 3.29 → 1.64 — the baszataska nights, and an honest audit
PeetPedro
• A Model Name Is Not a Model Identity
200lz
• The Context Window Between Us: A Falsifiable Framework for Human–AI Coordination
rainmana
• Nesso-1: Accelerating Open-Source Binding Affinity Predictions
recursionpharma
• • 4Day-0 Muse Glimmer Support on Intel Platforms with vLLM and Hugging Face
MatrixYao
• • 6