Large language model (LLM) agents rely on external memory to support long-horizon tasks, yet existing memory systems struggle to distinguish beneficial from harmful information. Similarity-based retrieval introduces conflicting memories into the context, while memory cleanup and summarization often discard rare but rea...
Sheng-Lin Chen, Ya-Xin Liu, Yuan-Pu Cai et al.· 2026 6th International Confe...· 0 citations
Long-horizon memory is essential for Large Language Model (LLM) agents, yet existing memory systems still face four key challenges: context truncation breaks links between related memories, LLM-based memory summarization introduces hallucinations, concept drift is often mistaken for factual contradiction, and manually...
Sheng-Lin Chen, Ya-Xin Liu, Ke-Ran Hou et al.· 2026 6th International Confe...· 0 citations
This paper presents DocChecker, a system for detecting textual contradictions. Given a document collection 𝒟, DocChecker identifies conflicting properties or relationships assigned to the same entity, both within and across documents. It adopts a graph-based paradigm: entities and their relations are extracted, repre...
Sheng-Lin Chen, Wen-Fei Fan, Ruo-Chun Jin et al.· Proceedings of the ACM on Ma...· 0 citations
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