CAVE-Mem: Boundary-Aware Experience Validation for Memory Search

Xinyu Li

cs.CLSep 23, 2026

CAVE-Mem: Boundary-Aware Experience Validation for Memory Search

Authors: Xinyu Li

Organizations: Kent State University

Abstract

Long-term memory agents increasingly rely on it- erative search and reusable experience to answer questions over large personal, factual, or narrative histories. However, current experience-memory systems largely optimize relevance: they re- trieve past search lessons that appear similar to the current state and inject them into the prompt. A relevant experience can still be harmful when the memory substrate, question intent, answer granularity, or evidence boundary changes. We propose CAVE- Mem, a training-free framework that represents experience as a typed intervention operator with applicability, boundary, and utility conditions. CAVE-Mem first obtains a base memory-search answer, then allows an operator to change it only if the oper- ator matches the current substrate, answer contract, evidence boundary, and cross-fitted utility; otherwise the system abstains. Experiments across long-term conversational memory, multi-hop question answering, and long-document narrative reasoning show consistent gains over relevance-only experience reuse.

Figures & tables

Fig. 1: From memory compression to flat experience reuse and validated intervention. CAVE-Mem validates a candidate experience before it can change the base answer; otherwise, it abstains and preserves the base answer.

Fig. 2: Deep memory search iterates over planning, memory search, integration, and reflection, producing retrieved evidence

r_{t}

and a state trajectory

s_{0},\ldots,s_{T}

Fig. 3: CAVE-Mem framework. Offline diagnostics produce cached trajectories, candidate families, a utility ledger, and a veto map. Online, the base memory-search agent produces retrieved evidence and a base answer, while candidate interventions are routed through the CAVE-Mem validity-gate controller/shield. The gate checks Profile, Contract, Boundary, and Utility before choosing between applying the candidate and abstaining to the base answer.

Gate check	Valid intervention	Boundary veto	Role in CAVE-Mem
Substrate profile	Operator family is enabled for the current memory substrate, e.g., episodic dialogue, fact-pack QA, or narrative text.	Operator transfers a substrate-specific procedure outside its profiled domain.	Prevents cross-substrate transfer.
Answer contract	Candidate directly fills the requested slot, e.g., person, place, date, count, or object.	Candidate answers a nearby attribute or compresses a reason into an unsupported phrase.	Aligns answer shape with the question.
Temporal boundary	Candidate preserves the exact date or state transition anchored in retrieved evidence.	Candidate replaces a constrained date with a broader month/year or an unsupported current state.	Prevents temporal drift.
Count boundary	Candidate is backed by enumerated instances with stable wording.	Candidate uses vague forms such as “at least” when an exact count is required.	Converts evidence into exact counts.
Quote/reason boundary	Candidate preserves source-grounded reason, feeling, or quoted wording.	Candidate paraphrases away the answer-bearing phrase.	Protects short answer-bearing spans.
Utility ledger	Operator family has positive cross-fitted diagnostic effect under the current substrate.	Candidate source is high-variance under matched counterfactual replay.	Selects high-utility interventions.

TABLE I: Gate checks used by CAVE-Mem to convert retrieved experience into validated interventions.

Model	Method	Overall		Multi-hop		Temporal		Open		Single-hop
		F1	BLEU	F1	BLEU	F1	BLEU	F1	BLEU	F1	BLEU
GPT-4o-mini	RAG [ 5 ]	39.92	34.52	33.75	24.33	31.52	26.86	25.21	20.42	46.60	42.16
	Mem0 [ 11 ]	48.27	42.10	30.73	20.91	52.14	46.13	31.56	25.70	54.33	49.25
	A-Mem [ 12 ]	46.59	40.55	32.34	23.77	49.34	43.20	30.33	25.00	51.94	46.70
	MemoryOS [ 13 ]	45.26	39.28	29.17	20.00	52.67	46.62	30.46	24.71	49.35	44.40
	LightMem	45.80	39.90	29.01	20.48	47.50	41.92	26.71	21.53	52.67	47.43

TABLE II: LoCoMo results under GPT-4o-mini and Qwen2.5-7B-Instruct. We report token F1 and BLEU-1 on the 1,388-question evaluation split.

Method	NQA 87K	HP 56K	HP 224K	HP 448K
RAG [ 5 ]	32.54	46.86	31.52	25.98
GAM [ 14 ]	38.66	64.07	60.19	59.36
R2Mem [ 15 ]	37.24	62.74	61.27	61.70
CAVE-Mem (ours)	39.37	66.24	61.58	61.87

TABLE III: Cross-dataset long-context QA check under GPT-4o-mini. NarrativeQA uses the 300-question average-87K subset; HotpotQA reports the 56K, 224K, and 448K context regimes.

Fig. 4: Category-level component ablation on LoCoMo. Bars correspond to five diagnostic checkpoints: profile, ELAA arbitration, validity-boundary checking, exact-slot rescue, and utility-aware selection. Conceptually, ELAA arbitration and exact-slot rescue are two candidate-intervention generators within the same high-level component.

Fig. 5: Sensitivity analysis for utility-gated intervention on LoCoMo. Each point uses conversation-level cross-fitting; the shaded region marks the pre-specified operating range rather than a per-conversation optimum.

Fig. 6: Leave-one-boundary-family ablation on LoCoMo. Each panel reports the score drop from full CAVE-Mem after disabling one validation family.

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REM: What to Store, When to Recall, What to Forget in Experience Memory for Coding Agents

Xinyi Lisa Chen, Pedro Herrero-Vidal, Xiaohan Chen, Xiaojing Yu+5

Yale University · Amazon

Memory Management for LLM Agents Agent Memory Software Engineering

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Large language model (LLM) agents now autonomously complete long-horizon tasks, such as repository-level coding, through hundreds of think-act steps, yet the experience each run produces is rarely reused on the next. Prior memory systems concentrate on retrieval, finding the most relevant past trajectory. Retrieval cannot observe the running agent, so it cannot decide whether the agent needs an experience, at which point, or for how long. We introduce REM (Reusable Experience Memory), a framework that manages the memory lifecycle from inside the agent's think-act loop through three explicit decisions. What to store: each completed trajectory is consolidated into a phase-structured document, two orders of magnitude smaller than the trajectory. When to recall: the document is injected at the start of the task; the costlier implementation record is withheld until an in-loop gate detects the agent struggling. What to forget: the record is evicted after a single exposure. On repository-level bug fixing (SWE-ContextBench Lite), REM retrieves the related trajectory more accurately than every state-of-the-art memory system (68.7% vs. 55.6% match@1) and improves, in every replicate, end-task resolution (27.6% vs. 24.9%) at 17% lower cost. Injecting the same experience as a summary or raw trajectory, even when oracle-retrieved, does not. Ablating any of the three decisions reduces resolution. Altogether, these results demonstrate that the bottleneck in experience reuse is not retrieval fidelity but how experience is stored and governed across its lifecycle, opening a control-theoretic perspective on agent memory.

Abstract

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