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FactoSR: A Factorized Reinforcement Learning Framework Boosts 4D Spatial Reasoning in VLMs
Researchers introduce FactoSR, a reinforcement learning framework that decomposes 4D spatial reasoning into XY, Z, and T sub-objec...
MaxKernel: Multi-Agent System Automates Expert-Level TPU Kernel Generation
Hugging Face researchers introduce MaxKernel, a multi-agent system that automates TPU kernel development via three paradigms, achi...
LLM Reasoning Operations Map to Separable Geometric Structures in Hidden States
A new study from Hugging Face researchers reveals that distinct reasoning operations in LLMs, such as deduction and goal decomposi...
UniMate: One Diffusion Model to Animate Any Skeleton from Text and 3D Assets
Hugging Face researchers introduce UniMate, a unified diffusion transformer that generates articulated motion for arbitrary skelet...
Hugging Face Unveils Enoki: A Unified Framework for Efficient Multi-Level Hallucination Detection
Enoki, a new open information extraction framework from Hugging Face, unifies claim-level verification and span-level hallucinatio...
Attention Triangle Reveals Bidirectional Semantic Leakage in Audio-Video Diffusion Models
Researchers analyze the 'attention triangle' in audio-video diffusion models, showing that cross-modal attention causes bidirectio...
Dr. Claw: An Open-Source Workspace for Auditable AI Research Workflows
Hugging Face researchers introduce Dr. Claw, an open-source workspace that wraps existing command-line coding agents in a controll...
Hugging Face Paper Formalizes Multi-Agent LLM Coordination with Game Theory and New SRMA Algorithm
A new paper on Hugging Face models orchestrator-worker LLM systems as bilevel coordination games, proving limits of transcript-onl...
OpenAI and Partners Launch AI Program to Bolster Independent Journalism in Ukraine
OpenAI, AIRPPU, and WAN-IFRA have joined forces to launch an AI initiative aimed at helping Ukrainian news organizations enhance i...
Hugging Face Researchers Unveil RISE: A Self-Extrapolating Method to Boost AI Reasoning
A new paper from Hugging Face introduces RISE, a training method that uses a model's own reinforcement learning trajectory to gene...
Reviving Layer Dropout: New Study Shows Efficiency Gains in LLM Training and Inference
A comprehensive study from Hugging Face demonstrates that layer dropout, when properly configured, can reduce training FLOPs by up...
Hugging Face Researchers Introduce OR-Clarify and InterOPT to Make AI Ask Before Optimizing
A new benchmark and framework from Hugging Face researchers teach LLMs to ask clarifying questions before formulating optimization...