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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...
Scientific applications increasingly rely on high-performance computing (HPC), yet translating a scientist's high-level goal into a correct target-scale execution remains brittle and labor-intensive. Large language model (LLM) agents promise to automate this, but two obstacles remain: granting a cloud-hosted model dire...
Error-bounded lossy compression is essential for storing and transferring the vector-field data produced by large-scale scientific simulations. Although it enforces a user-specified error bound to limit numerical distortion, it does not preserve the field's topology: small admissible perturbations can create or elimina...
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.
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