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Large language model (LLM) agents that reason over clinical records must track changes in a patient's state while preserving the history needed to understand them. Simply accumulating memories leaves it unclear which information still applies, whereas overwriting earlier memories can erase evidence needed to reconstruc...
The results show that models often do not affirm culmination but nevertheless accept the corresponding simple-past hypothesis, a pattern the authors characterize as Sufficiency Bias, and show that prompting interventions produce a Decision Shift among labels without reliably improving the underlying semantic understand...
SeDeM is proposed, a selective decompression framework that decouples compact memory storage from decoder conditioning and reduces online time-to-first-token and improves autoregressive decoding throughput relative to ICAE.
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