What Should an Agent Forget? Separating What Is Stored from What Is Used
cs.AI
Quick verdict
RD-Forget: training-free split of agent memory storage vs query-time use; wins over ACE/ReasoningBank on 5 benchmarks, but no code, no significance tests, and self-favoring baselines.
Summary
RD-Forget is a training-free framework that separates what a language agent stores from what it uses to answer. A retained source archive preserves the full history, while a query-conditioned memory view is re-curated for each question: a frozen LLM curator extracts atomic facts into semantic slots (subject|relation|scope), same-slot replacement marks older values as superseded, and intent-aware retrieval with a rescue switch makes historical evidence eligible again. A selector and greedy packer fit the chosen evidence into a 2,048-token budget before a frozen model answers. Across four LLM backbones and five memory benchmarks (AMB-Text, LongMemEval-KU, MemoryAgentBench-FC, BEAM, PersonaMem), RD-Forget posts the best score in all twelve main and all eight intent model-benchmark combinations, beating the better of ACE and ReasoningBank by 1.16 to 26.00 percentage points; the largest gain is on fact consolidation. Ablations show removing forgetting causes the largest deficit, followed by removing query conditioning. Key limitations: baselines are only the experience-based ACE and ReasoningBank, with no budget-matched retrieval control; scoring uses a judge from the same model family as the answers, without human validation; no significance tests or variance are reported, and one matched batch deviates from Table 1's Luna row by up to three points; ablations cover a single backbone; code and prompts are not released.
Review complete
Assessment at a glance
6
risk flags identified
● high 1
● medium 4
● low 1
5 strengths · 5 weaknesses · 5 author questions
AI text signal = Binoculars zero-shot detector score over 20 sampled passages (Fast-DetectGPT co-reported) — absence does not prove human authorship, and a flag does not imply misconduct.
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Key claims under review
- Separating what an agent stores (a retained source archive) from what it uses (a query-conditioned memory view) lets a superseded fact be suppressed for current-state questions while remaining retrievable for historical queries.
- RD-Forget achieves the highest answer accuracy in all twelve model-benchmark combinations of the main evaluation and in all eight query-intent combinations, beating the better of ACE and ReasoningBank by 1.16-26.00 percentage points.
- Removing forgetting control (-FORGET) causes the largest accuracy deficit, followed by removing query conditioning (-QC); slot grouping, rescue, and multi-hop closure each contribute smaller complementary gains.
- A rate-distortion formulation (maximize answer utility under a 2,048-token memory budget) provides a principled account of budgeted evidence selection at answer time.
Strengths
Complete empirical sweep with consistent wins
Table 1: RD-Forget leads every cell across Qwen3.5-flash, GPT-5.6-Luna, MiniMax-M2.5, and Kimi-K2.5 on AMB-Text, LME-KU, and MAB-FC (e.g., Luna mean 86.71 vs ACE 67.26); Table 3 repeats the sweep on BEAM and PersonaMem.
Well-motivated core distinction
The archive + query-conditioned view design (Sec. 3) makes forgetting reversible: superseded entries stay linked (Eq. 4) and are re-admitted for historical intents (Eq. 5), directly answering the superseded-fact-vs-historical-query tension posed in the introduction.
Ablations support the headline mechanism
Unusually concrete pipeline specification
Evaluation breadth beyond the main suites
Weaknesses
Same-family LLM judging, unvalidated
Reported differences sit near observed batch-to-batch variance
Baseline set plausibly favors the method
Single-backbone ablations; oracle-ish intent flag
Rate-distortion framing is decorative; no cost analysis
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Preliminary Review: What Should an Agent Forget? Separating What Is Stored from What Is Used (arXiv:2609.10263)
The remaining sections cover Methodology Assessment, Statistical Evidence, Results & Reproducibility, Limitations & Risks, and Verdict & Recommendations.
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Frequently asked questions
What is RD-Forget and how does it work?
RD-Forget is a training-free agent-memory framework that keeps a full source archive of past observations but builds a fresh, query-conditioned "memory view" for every question. A frozen LLM curator extracts atomic facts into semantic slots (subject|relation|scope); when a newer fact replaces an older one in the same slot, the older fact is marked superseded and hidden from current-state questions, while intent-aware retrieval and a rescue switch can re-activate it for historical or evolution questions. A selector and greedy packer fit the chosen evidence into a 2,048-token budget before a frozen model answers.
Does RD-Forget actually delete an agent's memories?
Forgetting is query-local, not permanent: the raw archive is never deleted. A superseded fact (e.g., a former employer) is merely excluded from the evidence view for current-state questions, but historical or evolution queries can bring it back through re-curation or the rescue path (Eq. 5 in the paper).
Does RD-Forget require fine-tuning or training?
No. It is training-free: the curator, selector, and answering model are all frozen language models used via prompting, with slot-based supersession links and budgeted packing handled by deterministic code.
Which benchmarks and models does RD-Forget use, and how well does it perform?
The paper evaluates on AMB-Text (86 questions from LoCoMo), LME-KU (78 LongMemEval knowledge-update questions), and MAB-FC (100 MemoryAgentBench fact-consolidation questions), plus BEAM (150 questions) and PersonaMem (579 questions), using four LLM backbones. RD-Forget reports the best score in all 12 main and all 8 intent model-benchmark combinations, beating the better of ACE and ReasoningBank by 1.16 to 26.00 percentage points.
Is code available for RD-Forget?
No repository or code link is given anywhere in the paper text. The three suites are public benchmarks, but without released prompts (the curator and selector are prompt-driven) exact replication would require re-engineering the pipeline.
About this review
This is an AI-generated preliminary review by Evolve.Pub, produced from the openly available text of the paper. It is not peer review and does not represent the authors' views. The canonical record of the paper is at the publisher link above.
- Figure 1/2 pixel-level rendering is lost in text extraction (labels interleaved), but all values used in this review were read from Tables 1-3 and are unaffected.
- The extraction ends cleanly after reference [13]; no further content (e.g., appendix) is present in the provided text.
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