8 papers
Dual-Cluster Memory Agent: Resolving Multi-Paradigm Ambiguity in Optimization Problem Solving
Xinyu Zhang, Yuchen Wan, Boxuan Zhang +4
Large Language Models (LLMs) often struggle with structural ambiguity in optimization problems, where a single problem admits multiple related but conflicting modeling paradigms, h…
Beyond Individual Intelligence: Surveying Collaboration, Failure Attribution, and Self-Evolution in LLM-based Multi-Agent Systems
Shihao Qi, Jie Ma, Rui Xing +15
LLM-based autonomous agents have demonstrated strong capabilities in reasoning, planning, and tool use, yet remain limited when tasks require sustained coordination across roles, t…
OptiVerse: A Comprehensive Benchmark towards Optimization Problem Solving
Xinyu Zhang, Boxuan Zhang, Yuchen Wan +5
While Large Language Models (LLMs) demonstrate remarkable reasoning, complex optimization tasks remain challenging, requiring domain knowledge and robust implementation. However, e…
$\textbf{AGT$^{AO}$}$: Robust and Stabilized LLM Unlearning via Adversarial Gating Training with Adaptive Orthogonality
Pengyu Li, Lingling Zhang, Zhitao Gao +5
While Large Language Models (LLMs) have achieved remarkable capabilities, they unintentionally memorize sensitive data, posing critical privacy and security risks. Machine unlearni…
ErrEval: Error-Aware Evaluation for Question Generation through Explicit Diagnostics
Weiping Fu, Bifan Wei, Jingyi Hao +7
Automatic Question Generation (QG) often produces outputs with critical defects, such as factual hallucinations and answer mismatches. However, existing evaluation methods, includi…
From Static to Dynamic: Adaptive Monte Carlo Search for Mathematical Process Supervision
Jie Ma, Shihao Qi, Rui Xing +4
The quality of process data plays a key role in training a Process Reward Model (PRM), which can enhance the complex mathematical reasoning capability of large language models. Exi…