4 papers
ActMem: Bridging the Gap Between Memory Retrieval and Reasoning in LLM Agents
Xiaohui Zhang, Zequn Sun, Chengyuan Yang +3
Memory management is essential for LLM agents in long-term interactions. Current memory frameworks typically treat agents as passive ``recorders'' and retrieve information without…
Answering the Unanswerable Is to Err Knowingly: Analyzing and Mitigating Abstention Failures in Large Reasoning Models
Yi Liu, Xiangyu Liu, Zequn Sun +1
Large reasoning models (LRMs) have shown remarkable progress on complex reasoning tasks. However, some questions posed to LRMs are inherently unanswerable, such as math problems la…
Mitigating Lost-in-Retrieval Problems in Retrieval Augmented Multi-Hop Question Answering
Rongzhi Zhu, Xiangyu Liu, Zequn Sun +2
In this paper, we identify a critical problem, "lost-in-retrieval", in retrieval-augmented multi-hop question answering (QA): the key entities are missed in LLMs' sub-question deco…
When Can Large Reasoning Models Save Thinking? Mechanistic Analysis of Behavioral Divergence in Reasoning
Rongzhi Zhu, Yi Liu, Zequn Sun +2
Large reasoning models (LRMs) have significantly advanced performance on complex tasks, yet their tendency to overthink introduces inefficiencies. This study investigates the inter…