What Eviction Destroys: A Restore-Counterfactual Audit of Forgetting in Agent Memory

📅 2026-09-08
📈 Citations: 0
Influential: 0
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🤖 AI Summary
研究通过引入恢复反事实方法,区分了因遗忘导致的不可逆损失和可恢复的检索失败,评估了不同驱逐策略对代理记忆系统准确性的影响。
📝 Abstract
Agent memory systems must discard stored information when their history exceeds a fixed token budget. Existing budget-accuracy frontiers quantify the resulting loss in accuracy, but do not distinguish irreversible losses caused by eviction from recoverable retrieval failures. We introduce the restore counterfactual, a per-question paired intervention that reinstates the question's gold evidence in the read-time context and reruns the same reader. Combining the change in correctness with whether the evidence was retained after eviction classifies each oracle-answerable error as recoverable, irreversible, or residual; in the residual case, the answer remains incorrect after restoration. We evaluate FIFO, random, redundancy-aware, and LLM-importance eviction on LongMemEval-S at three budgets and under two retrieval regimes, using GPT-4o-mini as the primary reader and judge and GPT-5.4-mini as a robustness reader. Under top-k retrieval at an 80k-token budget, the irreversible share among errors corrected by restoration is 0.67-0.73 for FIFO, random, and redundancy-aware eviction, compared with 0.60 for LLM-importance. At 8k tokens, it reaches 1.00 for all four policies. Recoverable errors occur under top-k retrieval at 80k tokens but are absent under forced-gold injection by construction, so budget-accuracy results are not directly comparable unless the retrieval regime is reported. An exploratory matched-accuracy analysis detects no difference in irreversible rate among accuracy-matched policy pairs at a resolution of 1.2-6 percentage points. The same analysis detects the deliberately destructive control. To our knowledge, this is the first per-item, per-question restore-counterfactual audit of eviction for external agent-memory stores on a standard conversational benchmark.
Problem

Research questions and friction points this paper is trying to address.

eviction
agent memory
retrieval failure
irreversible loss
recoverable error
Innovation

Methods, ideas, or system contributions that make the work stand out.

restore counterfactual
eviction
agent memory
retrieval failure
irreversible loss
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