Publications

A self-improving framework (World Action Verifier) that decomposes action-conditioned prediction into state plausibility and action reachability, exploiting forward–inverse asymmetry for verification and data-efficient world-model learning.

An RL framework that learns physics-grounded Hamiltonian world models through symmetry exploration, using Hamiltonian-based curiosity to collect physically informative data for extrapolative generalization.

A multi-agent framework that models agentic workflows as self-organized graphs and co-evolves agent semantics and collaboration topology via the Semantic-Topological Evolution (STEV) algorithm.

A framework that merges semantic information from CLIP and spatial features from SAM for fine-grained vertebrae classification, with a vertebral positioning with cross-model synergy module and Wasserstein loss.