Agent memory grows as agents read inputs, reason, and call tools. Longer histories increase inference cost and eventually exceed the context window. LLM-based summarization reduces this history but adds latency and provides no explicit bound on information loss. We propose LAM, a Lossy Agent Memory system with three co...
Bai-Xi Sun, Le Chen, Anjir Ahmed Chowdhury et al.· 0 citations
This work focuses on semantic heterogeneity and studies how it should shape the management and evaluation of working memory in coding agents, finding that semantically different working-memory objects exhibit distinct retention and compression behavior.
Le Chen, Zi-Shen Wan, Baixi Sun et al.· 0 citations
CEDAR is presented, a counterexample-guided framework that grounds instructions as regular languages over environment event traces and represents both skills and specifications as deterministic finite automata, suggesting that regular languages offer a practical verification layer between natural-language instructions...
Le Chen, Alvaro Velasquez, Ashutosh Trivedi· 0 citations
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