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Co-RL: Multi-Agent RL Enables Unsupervised Reasoning Without Labels
Hugging Face researchers introduce Co-RL, a multi-agent reinforcement learning framework where models learn from peer-derived rewa...
Hugging Face Unveils Zetta: A Closed-Loop Harness for Self-Evolving Robots
Zetta, a new closed-loop embodied harness from Hugging Face, enables robots to evolve runtime critics and recovery skills online,...
OpenAI Reaffirms Zero Data Retention for API Customers, Previews Private Safety Processing
OpenAI reiterates its Zero Data Retention commitment for eligible API customers and introduces a preview of Private Safety Process...
OpenAI Reaffirms Zero Data Retention and Previews Private Safety Processing
OpenAI reiterates its Zero Data Retention commitment for eligible API customers and introduces Private Safety Processing, a new ap...
Replit Launches Free Mode Powered by GPT-5.6 Luna
Replit's new Free Mode, powered by GPT-5.6 Luna, lets anyone create software without token costs, democratizing app development.
HarnessRisk Benchmark Exposes Critical Configuration Flaws in AI Agent Safety
A new benchmark evaluates AI agent harness safety across six operational phases, finding that configuration vulnerabilities and de...
Energy-Guided Flow Matching: Coarse-to-Fine Generation with Moving Endpoints
A new method called Energy-Guided Flow Matching (EG-FM) improves image generation by explicitly modeling a coarse-to-fine trajecto...
SkillForge: Self-Distilling Agents Learn Project-Specific Skills Before Fixing Bugs
Hugging Face researchers introduce SkillForge, a self-distillation framework that synthesizes repository-specific issues to proact...
Hugging Face Researchers Propose Capability-Centric Data Design for Generalist Image Generation
A new paper from Hugging Face introduces a capability-driven data infrastructure with curriculum scheduling and specialized data e...
No One-Size-Fits-All Memory: New Study Calls for Adaptive Substrate Routing in LLM Agents
A comprehensive evaluation of memory substrates for long-horizon LLM agents reveals that no single memory type dominates, highligh...
Hugging Face Unveils MoE-ViE: Efficient Vision Encoders Outperform Dense Models
Researchers at Hugging Face introduce MoE-ViE, a family of Mixture-of-Experts vision encoders that achieve state-of-the-art perfor...
Hugging Face Researchers Unveil Multi-Byte Prediction to Speed Up Byte-Level Language Models
A new paper from Hugging Face introduces multi-byte prediction (MBP), a method that generates multiple bytes in parallel in byte-l...