Author

Yi-Xin Ma

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Preprint Oct 2026

The Trinity: Capability-Driven Recursive Self-Improvement for Multimodal Agents

Multimodal intelligence can be viewed as human-guided co-evolution between worlds and agents through interaction. However, scaling this process faces two bottlenecks: constructing heterogeneous, verifiable environments is costly, and manual Harness iteration cannot keep pace with growing task diversity and complexity....

Wei-Dong Tang, Rui-Heng Wang, Jing-Xuan Zhang et al. · 2 citations

Preprint Oct 2026

REA: A Routed Embodied Agent Runtime for Asynchronous Brain-Cerebellum Control

REA (Routed Embodied Agent) is proposed, a unified runtime where a deliberative System 2, an embodied router System 1 and reactive System 0 policies cooperate under a single golden rule - no running policy waits on the brain.

P. Zhou, Yi-Xin Ma, Zhengfeiyang Zhang et al. · 0 citations

#artificial intelligence Preprint Oct 2026

The Trinity:Capability-Driven Recursive Self-Improvement for Multimodal Agents

The Trinity is introduced, a recursive self-improvement framework coupling world generation with Harness evolution through three roles: Creator, Explorer, and Evolver, which introduces Capability-Driven Real2Sim2Real: for heterogeneous tasks at the same node, Creator abstracts their common requirements into simplified,...

Wei-Dong Tang, Rui-Heng Wang, Jing-Xuan Zhang et al. · 0 citations

Preprint Aug 2026

Agentic Game Development as a Verifiable Trajectory Data Engine for Scaling World Models

This work proposes Reinforcement Learning with Human-Engine Verification (RLHEV), a post-training paradigm that combines dense engine signals with implicit human acceptance feedback from the development process to support RL post-training.

P. Zhou, Hesong Wang, Zhengfeiyang Zhang et al. · 1 citation

Preprint Aug 2026

Improving Generalization Robustness of Multimodal RLVR

Reinforcement Learning with Verifiable Rewards (RLVR) makes Multimodal Large Language Models more accurate, but the gains are brittle: simply paraphrasing a question or changing the prompt template can degrade them, which challenges reliable deployment in high-stakes scenarios like medical VQA. We trace this to two iss...

P. Zhou, Zhiwei Tang, Xiaopeng Peng et al. · 0 citations

#natural language process... Preprint Jul 2026

Unified Hallucination Fuzzing for Multimodal Large Language Models

This work introduces UniHall, a fine-grained dataset grounded in a unified taxonomy spanning Object, Instruction, and Knowledge dimensions, and proposes Self-Adaptive Multimodal Fuzzing (SAMF), a self-adaptive framework that employs evolutionary mutation strategies to explore the boundaries of model hallucinations.

P. Zhou, Jiajun Song, Zhiwei Tang et al. · 1 citation