Research Papers
New Framework Reconstructs Articulated Objects from a Single Rest-State Image
Researchers introduce a rest-state framework that reconstructs articulated objects from a single closed configuration, using visio...
Co-Evolution in Agentic Systems: A Path Beyond Human-Designed AI
A new survey from Hugging Face proposes a three-stage taxonomy for co-evolution in agentic systems, where multiple agents and thei...
Combodied Agents: A New Human-Centric Paradigm for Agentic AI
A new research paper from Hugging Face introduces 'Combodied Agents,' a closed-loop framework that integrates digital and physical...
RoMeRL: A New Method to Balance Feedback and Avoid Memory-Reward Traps in Self-Evolving Agents
Researchers introduce Reduced-Order Memory Reinforcement Learning (RoMeRL), a method that compresses trajectory-indexed memory uti...
RynnValue: Temporal Distance as a Scalable Reward Signal for Robot Learning
Hugging Face highlights RynnValue, an open-source value foundation model that uses temporal distance instead of preferences to lea...
Hugging Face Researchers Unveil Evidence-RL: A New Method to Make VLMs Reason from Visual Evidence
A new paper from Hugging Face introduces Counterfactual Evidence Disentanglement (CED), a training-time audit that ensures vision-...
Evo-Bench: New Benchmark Tests Whether LLMs Can Improve Their Own Agent Harness
Hugging Face researchers introduce Evo-Bench, the first benchmark designed to isolate and evaluate language models' ability to aut...
Business Arena: New Benchmark Reveals LLM Agents Struggle with Realistic Business Operations
A new benchmark from Hugging Face and Accio evaluates LLM agents in a realistic cross-border shop, finding a ninefold gap in perfo...
Interpretability Scales with Capability in New Training-Time Approach
A new paper from Hugging Face shows that making interpretability a training constraint yields scalable, disentangled representatio...
OasisKV: Boosting LLM Throughput by Prefetching Sparse KV Caches Beyond HBM
OasisKV, a new memory-centric inference system from Hugging Face researchers, stores full KV caches in cheaper memory tiers and us...
Hugging Face Research: Three-Stage Framework Boosts Follow-Up Edit Suggestions in Image Conversations
A new multimodal framework from Hugging Face improves follow-up edit suggestions in image-creation conversations, reducing visual...
Researchers Expose Flaw Allowing Theft of Hidden Reasoning from Major AI APIs
A new study reveals that encrypted reasoning traces from proprietary LLMs can be intercepted and decrypted by injecting them into...