Yang Liu

FunReason-MT is presented, a novel data synthesis framework for real-world multi-turn tool use that resolves the complexity barrier in multi-turn FC data by employing 1) Environment-API Graph Interactions to gather varied high-quality trajectories, 2) Advanced Tool-Query Synthesis to simplify hard query construction, and 3) Guided Iterative Chain for sophisticated CoT generation.

Reducing visual token exposure by leveraging selective retrieval can dramatically enhance the performance of 3D medical image question answering systems.

Emotion-aware art generation just got a major upgrade with ReART, achieving perfect alignment with target emotions through structured visual retrieval.

Achieving a 99.8% success rate in reconnecting initially disconnected convex regions transforms the feasibility of GCS-based motion planning.

Generating realistic 3D environments from satellite imagery in under 10 minutes could revolutionize how we visualize and interact with our planet.

Text world models can transform LLM-based agents from reactive responders into proactive planners, enhancing their performance in complex interactive tasks.

The hardest AI tasks remain largely unsolved, with current models achieving only a 2.6% success rate on economically valuable workflows.

A new video-based reward model beats GPT-5.2 and Gemini-3 Pro at evaluating computer-using agents, offering a scalable, model-agnostic alternative to traditional methods.

Forget random sampling – this framework crafts targeted, multi-turn function-calling data that catapults smaller LLMs to state-of-the-art performance.