Collects multi-step tool-use sessions from Codex Desktop, including 437 trajectories and 314 subagent delegation examples. The work is concentrated on Windows-based C#/C++ development and video-codec reverse engineering.
Uses explicit monitoring of evidence, assumptions, and open questions to adapt research goals and reasoning methods. It ranks first on all eight Taiyi-Bench metrics and reports expert-guided validation studies.
Lets a frozen model learn robot manipulation from one demonstration by rehearsing in a simulated world and storing feedback in context rather than weights. It reports 83.4% mean success across 50 RoboTwin 2.0 tasks.
Tests whether AI agents can interpret complex visual files, synthesize information across documents, and produce finished professional deliverables. Its 10 public tasks span distinct domains and require multimodal perception and multi-step tool use.
Evaluates fast-decision models across 2,919 tasks in 11 domains, measuring both capability and reliability through option-order changes and cases with no valid answer.
Runs a 145B agent model on a single consumer GPU using sparse latent MoE. The NVFP4 build reports 98.3 tok/s agent decoding on an RTX 5090 D, while also relying on host RAM.
Tests decision models as actors, trajectory judges, and reinforcement-learning teachers for long-horizon agents. It finds that a model can be a weak actor yet still improve policy learning by evaluating actions after seeing their consequences.
Turns plain-language prompts into working websites, web apps, and mobile apps in the browser. Chat-driven code generation, live preview, hosting, databases, and GitHub/Figma imports help builders move from idea to shipped project without local setup.
Turns plain-language app ideas into working software inside a browser workspace, then lets users preview, debug, and deploy without leaving the platform.
Memory layer that lets AI agents remember users and context across sessions.