Research Papers
SkillZip: Compressing Agent Skills Without Evaluation Rollouts
Hugging Face researchers introduce SkillZip, an evaluation-free method that compresses self-evolving agent skills by finding minim...
VibeLifeBench: New Benchmark Shows Frontier AI Agents Fail at Long-Horizon Proactive Tasks
Hugging Face researchers introduce VibeLifeBench, a benchmark of 200 multi-week simulated tasks, revealing that even the best fron...
Latent-to-4D: Direct 4D Generation from Video Diffusion Latents
A new method, Latent-to-4D, enables reusable direct 4D generation from video diffusion latents, bypassing RGB and transferring acr...
Ex-Omni-2D: Giving AI Dialogue Models a Visible Presence
Hugging Face researchers introduce Ex-Omni-2D, an omni-modal dialogue framework that generates coordinated text, speech, and expre...
Hugging Face Unveils Mendel Gödel Machine: Smarter Self-Improving Coding Agents
Researchers introduce Mendel Gödel Machine (MGM), a new framework that accelerates self-improving coding agents by leveraging mult...
AdvFD: New Adversarial Fréchet Loss Boosts Visual Generator Post-Training
Researchers propose Adversarial Fréchet Distance (AdvFD), a novel loss that adds a learnable adversarial feature space to static F...
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-...