6 papers
Beyond Asking: A Pipeline for Personalized Game Generation that Reads Players from Behavior
Yifan Lu, Xiaopeng Yuan, Haohan Wang
Personalized game generation requires inferring a player's abilities and behavioral style from how they play. Large language models have made this inference more attainable than ev…
KV-PRM: Efficient Process Reward Modeling via KV-Cache Transfer for Multi-Agent Test-Time Scaling
Peng Kuang, Haibo Jin, Xiaoyu Han +5
Process Reward Models (PRMs) have been proven to be highly effective in guiding test-time scaling (TTS) methods, which significantly boost the capabilities of LLM-based multi-agent…
EASE-TTT: Evidence-Aligned Selective Test-Time Training for Long-Context Question Answering
Xiaopeng Yuan, Zebin Wang, Suwen Wang +3
Long-context question answering (QA) remains challenging for smaller language models even when answer-bearing evidence is already present in the input. Existing within-context retr…
Closing the Loop on Latent Reasoning via Test-Time Reconstruction
Xiaopeng Yuan, Haibo Jin, Ye Yu +4
Recent work moves intermediate reasoning from natural-language traces into latent or cache-level representations to reduce token overhead and avoid a discrete communication bottlen…
Learning to Communicate: Toward End-to-End Optimization of Multi-Agent Language Systems
Ye Yu, Heming Liu, Haibo Jin +3
Multi-agent systems built on large language models have shown strong performance on complex reasoning tasks, yet most work focuses on agent roles and orchestration while treating i…
Tracing LLM Reasoning Processes with Strategic Games: A Framework for Planning, Revision, and Resource-Constrained Decision Making
Xiaopeng Yuan, Xingjian Zhang, Ke Xu +5
Large language models (LLMs) are increasingly used for tasks that require complex reasoning. Most benchmarks focus on final outcomes but overlook the intermediate reasoning steps -…