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cs.AI2026

CARD: Controlled Agentic Reddit Discussions for Credit Card Simulation

Yaoning Yu, Kai-Min Chang, Ye Yu +3

Online credit card discussions provide a natural setting for studying how consumers communicate about financial products. Simulating these discussions requires more than just gener…

cs.AI2026

QuantumMind: Constraint-Grounded Agentic Reasoning for Speedup Analysis in Quantum Computing

Yijing Zuo, Zhe Fu, Zihan Nie +2

Identifying a meaningful quantum speedup requires more than matching a classical problem to a familiar quantum primitive: the claim must preserve the task, respect access and outpu…

cs.AI2026

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…

cs.AI2026

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…

cs.AI2026

Do Self-Evolving Agents Forget? Capability Degradation and Preservation in Lifelong LLM Agent Adaptation

Ye Yu, Xiaopeng Yuan, Haibo Jin +3

Recent advances in LLM agents enable systems that autonomously refine workflows, accumulate reusable skills, self-train their underlying models, and maintain persistent memory. How…

cs.AI2026

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…