4 citations · 6 across the 13 of their papers we have counts for
5 papers · 1 filter
Long-Horizon Textual World Modeling through Structured Reasoning
Fangxin Wang, Xiang Gao, Yuguang Yao +3
World models must predict how an environment evolves under sequences of actions, enabling agents to compare possible futures and reason about counterfactual actions before acting.…
Textual Belief States for World Models: Identifiable Representation Learning Under Strict Mediation
Xiang Gao, Kaiwen Dong, Yuguang Yao +2
World models in partially observed environments rely on latent representations that summarize interaction history, but in many modern LLM-based architectures predictive performance…
Goal-Conditioned Supervised Learning for LLM Fine-Tuning
Shijun Li, Kaiwen Dong, Xiang Gao +1
Large language models often require fine-tuning to better align their behavior with user intent at deployment. Existing approaches are commonly divided into online and offline para…
Learning to Rewrite Tool Descriptions for Reliable LLM-Agent Tool Use
Ruocheng Guo, Kaiwen Dong, Xiang Gao +1
While most efforts to improve LLM-based tool-using agents focus on the agent itself - through larger models, better prompting, or fine-tuning - agent performance increasingly plate…
RIMRULE: Improving Tool-Using Language Agents via MDL-Guided Rule Learning
Xiang Gao, Yuguang Yao, Qi Zhang +5
Large language models (LLMs) often struggle to use tools reliably in domain-specific settings, where APIs may be idiosyncratic, under-documented, or tailored to private workflows.…