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Gist-based context compression---summarising older conversation history into compact representations---is a common approach in long-horizon language model agents, yet its effect on different types of memory retrieval is poorly understood. We use Salience-Weighted Consolidation (SWC), a biologically-inspired compression framework motivated by sleep-based memory consolidation, as a diagnostic probe to study when gist compression helps and when it hurts.
LongHarness is a model‑agnostic harness that explicitly manages the interaction between acquired evidence and evolving conclusions for long‑context reasoning over corpora larger than a model’s context window. Its Evidence‑Conclusion Coevolution mechanism uses unresolved conclusions to steer evidence retrieval, while new evidence refines those conclusions, and its Evidence Lifecycle Management keeps evidence current and traceable. Evaluations on OfficeQA Pro, AA‑LCR, and LongBench v2 show substantial performance gains over direct reasoning and state‑of‑the‑art agent harnesses, with GLM‑5.3‑Flash exact match improving from 15.8% to 60.2% on V1 and from 4.4% to 61.1% on V2.
arXiv:2608. 11775v1 Announce Type: new Abstract: Gist-based context compression---summarising older conversation history into compact representations---is a common approach in long-horizon language model agents, yet its effect on different types of memory retrieval is poorly understood.
arXiv:2606. 09900v1 Announce Type: cross Abstract: Long-term memory is the missing layer for LLM agents: across sessions they forget, and the common workaround -- replaying the whole history into the prompt -- is expensive, slow, and, as distractors accumulate, less accurate.
The study evaluates an auditable patient‑timeline reconstruction system that tracks provenance, records revisions, and refuses to answer when evidence is missing. Using a synthetic corpus of 1,000 patients and 3,353 notes, two provenance‑aware Evidence Graph operators reduced graph size by 33–37% while preserving all answers across 6,813 query points; a fixed‑window baseline failed to answer over half of the points. The system’s evidence‑gating mechanisms (BioClinicalBERT and a zero‑shot LLM) responded appropriately to evidence‑unavailable controls, but performance varied on marker‑free controls, with BERT maintaining high accuracy but the LLM’s coverage dropping sharply. "whyItMatters":"The results demonstrate that provenance‑aware evidence graphs can significantly reduce data complexity while maintaining answer integrity, highlighting a practical approach to building auditable clinical NLP systems."
arXiv:2608. 08055v1 Announce Type: new Abstract: Large language model (LLM) agents that assist users over weeks of conversation must remember what is currently true, not merely what was once said.