Correct Now, Insufficient Later: Auditing Update Sufficiency in Context Compression
On the map: Efficient LLM inference and serving → KV cache optimization
Abstract (arXiv)
A memory can answer a current query correctly while discarding distinctions required by a later update. We investigate this failure with a paired-history audit: two histories have the same current answer, receive a shared future update, and require different subsequent answers. A pilot evaluates 24 history pairs across six synthetic mechanisms, 12 memory conditions, two repeats, and two model backends. A deterministic frontier selector obtains strict reveal accuracy of 96/96 on DeepSeek and 82/96 on GLM; a structured writer obtains 62 successes with one unresolved outcome and 56/96. The configured four-outcome joint contrast has finite-sample identification intervals of [0.521, 0.542] and [0.292, 0.313], not confidence intervals. A record-level audit distinguishes retained-state adequacy, response delivery, and answer-schema compliance without changing those original scores. It finds 26 and 25 well-formed but semantically wrong structured reveal memories, while all 14 GLM frontier reveal failures contain correct values in the wrong wrapper. Tombstone removal produces 16/16 exact replay failures in the targeted mechanism. Identifier renaming then exposes a separate flaw: original frontier late-reference adequacy falls from 8/8 to 94/320 transformed instances. We provide and test a label-equivariant repair, but it preserves only 2/8 original late-reference answers: eliminating a naming shortcut does not solve unknown future relevance. These results support a scoped evaluation methodology and reproducible failure analysis, not general superiority of the repaired algorithm. Paid pilot evidence, retrospective diagnostics, and new offline tests are reported separately; no independent held-out or natural-task validation is claimed.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in KV cache optimization, one of 12 directions in Efficient LLM inference and serving. Builds on 12 of its 18 references that are in the map. First posted on arXiv 17 Sep 2026.
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12 of 18 references.
References · 12
- 2025A-MEM: Agentic Memory for LLM Agentscited by 1,000
- 2025Mem0: Building Production-Ready AI Agents with Scalable Long-Term Memorycited by 722
- 2024LongMemEval: Benchmarking Chat Assistants on Long-Term Interactive Memorycited by 656
- 2025Zep: A Temporal Knowledge Graph Architecture for Agent Memorycited by 372
- 2024AppWorld: A Controllable World of Apps and People for Benchmarking Interactive Coding Agentscited by 359
- 2025ACON: Optimizing Context Compression for Long-horizon LLM Agentscited by 126
- 2025The Complexity Trap: Simple Observation Masking Is as Efficient as LLM Summarization for Agent Context Managementcited by 40
- 2026LongMemEval-V2: Evaluating Long-Term Agent Memory Toward Experienced Colleaguescited by 17
- 2025Reasoning or Not? A Comprehensive Evaluation of Reasoning LLMs for Dialogue Summarizationcited by 6
- 2026Toward Reliable Context Compression for Long-Horizon Agents: An Empirical Study of Execution Instabilitycited by 5
- 2026MEMAUDIT: An Exact Package-Oracle Evaluation Protocol for Budgeted Long-Term LLM Memory Writingcited by 3
- 2026The Compaction Cliff in Long-Running AI Agent Memorycited by 1
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- 2026Does Your Agent's Memory Survive a Model Upgrade? A Controlled Study of Memory Portability
- 2026LSREP: A Longitudinal State-Replay Protocol for Evaluating Conversational Memory, with ICE v2 as an Audited Local-First Architecture
- 2026A-TMA: Decoupling State-Aware Memory Failures in Long-Term Agent Memory
- 2026MemTrace: Probing What Final Accuracy Misses in Long-Term Memory
- 2026Faithfulness Is Not Free: Auditing Offline KV-Cache Quantization in Retrieval-Augmented Generation
- 2025Memory in Large Language Models: Mechanisms, Evaluation and Evolution