agent/papers/items/2026-2605-10870-remember-the-decision-not-the-description-a-rate-distortion-framework-for-agent-.md

Paper: Remember the Decision, Not the Description: A Rate-Distortion Framework for Agent Memory

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type: paper title: "Remember the Decision, Not the Description: A Rate-Distortion Framework for Agent Memory" authors: Mingxi Zou, Zhihan Guo, Langzhang Liang, Zhuo Wang, Qifan Wang, Qingsong Wen, Irwin King, Lizhen Qu, et al. year: 2026 venue: arXiv url: https://arxiv.org/abs/2605.10870 code_url: source: arxiv collected_at: 2026-07-08 published_at: 2026-05-11 updated_at: 2026-05-11 status: skimmed relevance: high topics:

• agent-evaluation
• memory
• planning methods:
• decision-rate-distortion
• k-slot-memory
• certified-memory-split benchmarks:
• LoCoMo
• synthetic-decision-diagnostics models:

datasets:

• cs.AI related_concepts:
• decision-sufficient-state
• selective-forgetting related_jobs:

related_experiments:

• KC-001-agent-memory-pilot related_projects:
• learning/agent-memory collection_score: 13 collection_queries: language-agent

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One-line Takeaway

Memory 应保留会改变决策的历史差异，而不是优先保留描述最相似或最完整的记录。

Pilot Skim

• problem: relevance、salience 和 summary fidelity 没有直接刻画 memory 对未来决策的价值。
• method: 在 K 个 runtime state 预算下定义 decision distortion、forgetting boundary，并按 certified conflict 拆分 state。
• evidence: LoCoMo 子集上描述相似度与 evidence compatibility 的相关仅 0.103；matched budget 下 DeMem gold evidence recall 为 83%，描述检索为 66%。
• boundary: 理论主要在 contextual bandit 和有限状态设定中建立，实际 LLM 冲突判定仍依赖 judge 与近似算法。

Used In

• Agent Memory evidence matrix

Links

• arXiv: https://arxiv.org/abs/2605.10870