Guan-Hua Chen

Real-world professional tasks demand agents that learn from exploration and feedback, a capability unlocked by a novel engine that reconstructs environments to force agents through the full problem-solving lifecycle.

Simple fusion of chemically-guided classical models with graph predictors unlocks competitive molecular property regression, challenging the necessity of complex multimodal architectures.

JustMem is introduced, which stores conversation history as compact atomic memories and adapts memory access along two dimensions to each query and achieves the highest mean accuracy and retrieval recall among the compared memory systems while using substantially fewer generative-model tokens for memory construction and inference.

The Profiling, Investigation, and Judgment (PIJ), comprising 2,500 real homicide cases from five countries, is introduced and it is found that performance degrades systematically as tasks shift from explicit fact extraction to implicit reasoning over unknown suspect profiles.

Agent memory breaks when models are forced to simultaneously interpret, update, and rewrite facts—decoupling maintenance into explicit pairwise relation classification and role-based fusion yields up to a 29.8 percentage point accuracy leap.

Fine-grained decision-making in agentic RL can boost performance by nearly 4 points, challenging the reliance on coarse heuristics.