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Orca: What If Next-Token, Next-Frame, and Next-Action Are the Same Task?
Orca (BAAI) replaces next-token, next-frame, and next-action prediction with a single Next-State-Prediction objective. A frozen 4B backbone pre-trained on 12.5K hours of video — with zero action labels — feeds three lightweight readouts, and the action readout, trained on just 200 trajectories per task, beats π0.5 on OOD robot manipulation (32.4 vs 29.4).
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Browse all articlesSWE-Explore: The Benchmark That Finally Asks — Did Your Coding Agent Read the Right Code?
SWE-Explore isolates repository exploration from patch generation, revealing that coding agents find the right files ~65% of the time but recall only ~15-19% of the lines that actually matter — and that context efficiency predicts downstream resolve rate with Pearson r = 0.950.
SkillOpt: Training Agent Skills Like Neural Network Weights - Without Touching the Model
SkillOpt applies deep-learning-style optimization - bounded edit budgets, validation gating, rejected-edit memory - to natural-language skill documents, improving frozen LLMs by up to +39 points across 52/52 evaluated cells without changing a single model weight.
MolmoAct2: The First Fully Open Robot Controller That Beats Closed-Source Giants
MolmoAct2 is a fully open vision-language-action model that outperforms π0.5 and matches Gemini Robotics ER, achieving 97.2% on LIBERO and 87.1% real-world success via per-layer KV-cache conditioning and adaptive depth reasoning.
RecursiveMAS: What If Your Multi-Agent System Was Just One Big Recursive Neural Network?
RecursiveMAS treats an entire multi-agent system as a single recursive computation in latent space, adding only 0.31% trainable parameters while achieving +8.3% accuracy, 2.4x speedup, and 75.6% token reduction over text-based multi-agent baselines.
Tstars-Tryon 1.0: Virtual Try-On as Multi-Image Editing at Taobao Scale
How a unified 5B MMDiT trained with multi-reward RL and step distillation reframes virtual try-on as multi-image editing — and runs in under 4 seconds in production.
ClawGUI: A Full-Stack Open-Source Pipeline for GUI Agents
ClawGUI unifies online RL training, reproducible evaluation, and real-device deployment of GUI agents into one open-source pipeline — and shows a 2B model trained inside it can beat 72B untrained baselines on MobileWorld.
SkillClaw: Making LLM Agent Skills Evolve Collectively
SkillClaw is a framework for collective skill evolution in multi-user LLM agent ecosystems. Instead of static skill libraries, the system automatically learns from interactions across users and propagates improvements to everyone.
TAPS: Why Your Draft Model's Training Data Matters More Than Its Architecture
TAPS shows draft training data drives speculative decoding: merged-tree verification of math and chat specialists hits 5.11 acceptance length, weight averaging 2.59.
Demystifying Video Reasoning: Models Don't Think in Frames - They Think in Denoising Steps
Chain-of-Steps shows video diffusion models reason across denoising steps, not frames, exploring and self-correcting before converging on a single answer.
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