{"id":"44c6bbb3-c089-41b3-b17e-fd4144a2fe99","arxiv_id":"2607.22562","paper_version":1,"verdict":"REJECT","confidence":"MODERATE","novelty_score":5.0,"correctness_risk":"medium","formal_verification":"none","parameter_count":10,"one_line_summary":"A utility-driven forgetting framework with composite importance scoring improves long-context reasoning in LLM agents by up to +9.65 F1 on LoCoMo and +3.32 ACC on LongMemEval-s.","lead":"SF-AMS is a memory manager for LLM agents that scores each stored memory by importance and gradually drops low-value items, keeping a compact set for reasoning. It reports higher F1 than several existing memory frameworks on long-conversation benchmarks, with the largest gains in multi-hop and temporal questions.","discovery_kind":"new_method","skeptic_critique":{"model":"deepseek-v4-flash","headline":"Abstract's 'consistent gains' claim is contradicted by Table 1's own GPT-4o-mini multi-hop row (Ours 40.57 vs A-Mem 45.85 F1), so the central empirical claim is not supported by the reported data.","rationale":"The reader's weakest assumption focuses on the LLM salience evaluator being unvalidated. That is a legitimate concern, but the more immediate and decisive problem is that the central empirical claim is internally contradicted by the paper's own results. The abstract promises 'consistent gains' and the introduction repeats 'consistent improvements,' yet Table 1 shows SF-AMS losing to A-Mem on GPT-4o-mini multi-hop by over 5 F1 points and nearly 5 BLEU-1 points. This is not an external critique requiring new benchmarks; it is a direct inconsistency between claim and evidence. The paper's later phrasing 'consistent improvements across most reasoning types' suggests the authors recognized the non-uniformity, but the abstract and title-level claim are not so qualified. Because no error bars or repeated runs are reported, the one large positive result cited in the abstract could be within noise, especially given API-based evaluation variance. The formal proof issues raise additional doubts but are secondary to the empirical contradiction. My concern therefore reinforces the reader's REJECT verdict rather than changing it.","tokens_in":22878,"tokens_out":3668,"duration_ms":39046,"concrete_test":"Re-run the LoCoMo multi-hop condition under GPT-4o-mini with SF-AMS and A-Mem using identical retrieval and generation settings, at least 3 independent runs, and report mean ± 95% CI. If the SF-AMS mean remains below A-Mem or within the CI, the abstract's 'consistent gains' claim fails. As a cheaper analytical check, count all backbone × metric cells in Table 1 where 'Ours' is not the best; the multi-hop GPT-4o-mini loss on both F1 and BLEU-1 already falsifies the unqualified 'consistent gains over strong SOTA baselines' claim.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The strongest claim is that SF-AMS achieves 'consistent gains over strong SOTA baselines,' with the abstract highlighting up to +9.65 F1 in multi-hop reasoning. The paper's own Table 1 contradicts this: under GPT-4o-mini in multi-hop, SF-AMS scores 40.57 F1 and 31.87 BLEU-1, while A-Mem scores 45.85 F1 and 36.67 BLEU-1—a loss on both metrics in exactly the reasoning category advertised as a headline strength. The internal text retreats to 'consistent improvements across most reasoning types,' but the abstract and introduction claim consistency without that caveat. Additionally, the checklist explicitly states no error bars or statistical significance tests are provided ([No] on item 7), so the reader cannot determine whether the +9.65 gain on Qwen2.5-7B is signal or noise. This is a load-bearing problem independent of the unvalidated LLM salience evaluator: even if the evaluator were perfectly calibrated, the reported results would still fail to support the central claim of consistent gains.","agreement_with_reader":"partial"},"referee_report":{"model":"deepseek-v4-flash","summary":"The paper proposes SF-AMS, a memory-management framework for LLM agents that combines composite importance scoring (CIS), survival-potential dynamics with strategic forgetting, a four-level memory hierarchy, and hybrid retrieval. The authors claim that this dynamic utility-driven mechanism maintains a compact high-utility memory and yields consistent gains over strong baselines on LoCoMo and LongMemEval-s, with the largest improvements in multi-hop and temporal reasoning. The paper also presents Theorem 1 and Proposition 1 as theoretical support for retrieval priority and stability, and includes ablations, an efficiency analysis, and a memory-distribution study.","tokens_in":23250,"tokens_out":4638,"duration_ms":47322,"significance":"If the empirical and theoretical claims were well supported, the paper would make a useful contribution to agent memory management: the idea of replacing heuristic decay with a utility-based survival signal is plausible, and the framework integrates several signals (semantic, entity, usage, diversity, temporal) in one pipeline. The two benchmark evaluations and component ablations are appropriate. However, the current evidence is not sufficient. The headline claim of 'consistent gains' is contradicted by the paper's own Table 1, the formal proofs are restatements of assumptions rather than proofs about the actual system, the core LLM salience evaluator is unvalidated, and the experiments report no variance or significance information. These are load-bearing issues for the paper's central claims.","major_comments":[{"comment":"The abstract and introduction claim 'consistent gains' over strong baselines and highlight multi-hop reasoning as the largest improvement. Table 1 directly contradicts this: under GPT-4o-mini, SF-AMS scores 40.57 F1 and 31.87 BLEU-1 on Multi-Hop, while A-Mem scores 45.85 F1 and 36.67 BLEU-1 — a loss on both metrics. The text later retreats to 'most reasoning types,' but the abstract and conclusion do not include this caveat. As stated, the central empirical claim is not supported by the reported data.","section":"Abstract, §1, §4.3, Table 1"},{"comment":"The proof of Theorem 1 uses a scoring function R(m,q)=ω(L)·sim(v_m,v_q) with a layer weight ω(L). This function does not appear in the main text; Eq. (5) defines session relevance as S_session = sim_sem + α_kw·sim_kw, with no layer weight. The theorem's condition γ>1+δ and the 'noise-bounded similarity ratio' are assumed rather than derived from the actual retrieval formula. Consequently, Theorem 1 is a restatement of its assumptions, not a guarantee about the deployed ranker in Eq. (5).","section":"Appendix A.1, Theorem 1, Eq. (5)"},{"comment":"The proof of Proposition 1 does not establish a uniform bound on aggregate survival potential. The per-step bound Φ^{t+1} ≤ Φ^t + R_max allows unbounded growth over time when a unit's expected reinforcement exceeds λ, and the capacity constraint only bounds the number of units, not the sum of their potentials. The 'continual competition' and 'finite precision' remarks are informal; the claimed Lyapunov boundedness is not proven. The proposition as stated remains unsupported.","section":"Appendix A.2, Proposition 1"},{"comment":"No error bars, confidence intervals, or statistical significance tests are reported for any experiment. Many of the claimed gains are small (e.g., +3.32 ACC in Table 3), and the headline +9.65 F1 is a single number from a single run. Without variance information, the reader cannot determine whether any of these differences are signal or noise. Since the paper's central claim is empirical, this missing evidence is load-bearing.","section":"Checklist item 7, §4.3, Tables 1 and 3"},{"comment":"The LLM salience evaluator in Appendix C.3 is the core signal feeding CIS and survival-potential dynamics, but its output is never validated against human judgments, no ablation of the fixed weight distribution (30/25/20/15/10) is reported, and no score stability across calls is given. If the evaluator is biased or noisy, the importance ordering, hierarchy, and retrieval all inherit that error. The paper needs at least a sensitivity analysis of this component to support the mechanism's reliability.","section":"Appendix C.3, Eq. (1), Eq. (3)"},{"comment":"The implementation details leave many free parameters unspecified: α in Eq. (1), the entity-type priors w_k, the decay coefficient λ, α_div, β, τ, α_kw, and the mapping from the 'Ratio' in Table 5 to λ. The checklist claims full disclosure, but without these values the experiments cannot be reproduced. Code is also withheld (checklist item 5 [No]), further limiting reproducibility.","section":"§4.2, Checklist item 4"}],"minor_comments":[{"comment":"The text refers to 'the Foster-Lyapunov drift condition outlined in Proposition 2,' but no Proposition 2 appears anywhere in the paper. Either add the proposition and proof or remove the reference.","section":"Appendix B.1"},{"comment":"The paper has no limitations section, and the checklist explicitly answers [No] to item 2. Given the unvalidated salience evaluator and the contradictory results in Table 1, a limitations discussion is needed.","section":"Checklist item 2"},{"comment":"The checklist answers [N/A] to LLM usage, stating that LLMs are only backbone models. This is inconsistent with Appendix C.3, where an LLM salience evaluator is an integral part of the proposed method. This should be declared.","section":"Checklist item 16"},{"comment":"The figure contains typographical errors: 'Forgetting Radio' should be 'Forgetting Ratio,' and 'Oraginal Memory' should be 'Original Memory.'","section":"Figure 3"},{"comment":"The text says the 'largest gain is observed in Temporal reasoning' under GPT-4o-mini (+6.91 F1), but Table 1 also shows a large Open-Domain gain (+? vs MemO 48.62 to 49.10, +0.48 only; actually temporal is largest for GPT-4o-mini). The narrative is mostly accurate, but the abstract's emphasis on multi-hop as the headline is not representative of the full table.","section":"§4.3"}],"recommendation":"reject","confidential_remarks":"The paper's own evidence works against its central claim: the abstract promises 'consistent gains' and highlights multi-hop reasoning, yet Table 1 shows SF-AMS loses to A-Mem in the GPT-4o-mini multi-hop condition. The theoretical results are similarly problematic: Theorem 1's proof relies on a scoring function not used in the main text, and Proposition 1's proof does not actually bound the aggregate potential. The missing error bars and unvalidated salience evaluator are additional load-bearing gaps. These are not local presentation issues; they affect the main contributions, so I cannot recommend revision within the current scope."},"author_rebuttal":null,"desk_editor":{"model":"deepseek-v4-flash","letter":"Two things you should know about this paper. The core mechanism—composite importance scoring plus survival-potential decay and eviction—is a reasonable integration of existing memory ideas, and on Qwen2.5-7B it shows real-looking gains in multi-hop and temporal reasoning. But the headline claim of 'consistent gains' is contradicted by their own Table 1: under GPT-4o-mini, multi-hop is 40.57 F1 against A-Mem's 45.85, and BLEU-1 is also lower. That's not a footnote; it's the abstract's central promise.\n\nWhat's genuinely new is the specific combination: CIS merges an LLM salience score with entity-type priors, survival potential Φ(m_i) is updated with usage feedback and redundancy modulation, and memory is partitioned into a four-layer hierarchy with entity anchoring. I haven't seen that exact integration in the cited prior work. The LongMemEval-s result (68.52% ACC vs 65.20% for A-MEM) and the LLM-call reduction are plausible efficiency wins, and the ablations show the components matter, even if the effect sizes look too clean.\n\nThe soft spots are real and load-bearing. Theorem 1's appendix proof uses a scoring function R(m,q)=ω(L)·sim that never appears in the main-text Eq. 5, and the condition γ>1+δ is assumed, not derived. Proposition 1 basically restates bounded reinforcement and capacity constraints. The checklist admits no error bars or significance tests, and the code is withheld. Appendix B.1 claims a 10× speedup 'in our main results' that Table 3 doesn't actually show—it reports runtime and LLM calls, no 10× anywhere. Figure 2 and Table 2 disagree on component names (LLM-based vs Semantic Salience). And the whole framework rides on an LLM salience evaluator (Appendix C.3) whose scores are never validated.\n\nThe stress-test note is accurate; I don't see a reading of the abstract that survives Table 1. That said, the idea is reasonable and externally testable. I'd send this to referees with a clear expectation of major revision—not a desk reject—because the flaws are fixable: add variance, reconcile the headline with the table, either fix the theory or cut it, release code. As submitted, though, the central empirical claim is not supported.\n\nFor someone designing agent memory, the mechanism is worth a look as a design idea. For someone wanting to build on the empirical results, hold off.","headline":"Reasonable integration of memory ideas, but the paper's own Table 1 contradicts the abstract's 'consistent gains,' and the theory is mostly restated assumptions.","tokens_in":23726,"tokens_out":3974,"would_cite":false,"duration_ms":39140,"reading_group":"maybe","serious_thinker":"yes","would_accept_peer_review":true},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"deepseek-v4-flash","headline":"SF-AMS claims that managing LLM-agent memory by a dynamic survival potential—reinforced by use, decayed by time, and evicted when redundant—yields consistent reasoning gains over static retrieval and heuristic decay.","keywords":["strategic forgetting","LLM agent memory","survival potential","composite importance scoring","hierarchical memory","long-context reasoning","memory utility","entity anchoring"],"falsifier":"Replace the LLM salience evaluator with random scores in [0,1] and rerun the LoCoMo multi-hop and temporal experiments, keeping everything else fixed. If the reported F1 gains over baselines persist, the composite importance signal is not the driver; if they collapse, the claim is supported. A second check: run each configuration several times and test whether the +9.65 F1 margin exceeds run-to-run variance, since no error bars are reported.","tokens_in":22709,"feed_emoji":"🧠","tokens_out":6056,"duration_ms":60145,"temperature":0.7,"pith_summary":"The paper argues that the bottleneck in long-horizon LLM-agent reasoning is not storage capacity but the signal-to-noise ratio of memory. It proposes treating each memory unit as having a survival potential that rises with importance, usage, and novelty and falls with redundancy and time, so that low-value memories are strategically forgotten rather than accumulated. A composite importance score, combining an LLM-assigned salience rating with entity-type signals, drives this survival dynamic and a four-tier hierarchy. On LoCoMo and LongMemEval-s the method reports consistent gains over strong baselines, with the largest jump in multi-hop reasoning under Qwen2.5-7B (+9.65 F1) and temporal reasoning under GPT-4o-mini (+6.91 F1). A sympathetic reader would care because the claim, if true, means agents can reason better over long interactions without unbounded memory growth.","feed_headline":"Forgetting on purpose lifts LLM memory reasoning by 9.65 F1","feed_subtitle":"A survival-potential score keeps a compact, high-signal memory that beats static retrieval on long-context tasks.","key_machinery":"The load-bearing object is the Composite Importance Score (CIS): a normalized blend of an LLM-assigned salience score S_LLM(m) and entity-type priors (Eq. 1). Eq. 3 translates that score into survival-potential dynamics with a global decay lambda, usage gating, and diversity modulation, and the resulting four-tier hierarchy (Core, Important, Secondary, Irrelevant) couples retrieval with forgetting. A structural-gap condition (gamma > 1 + delta) is stated as a guarantee that core facts stay ranked above distractors in Top-K retrieval, while a boundedness proposition asserts that the aggregate survival potential does not diverge despite repeated reinforcement.","core_discovery":"The central claim is that memory importance is not a static property fixed at write time but a dynamic utility signal that must be updated as the agent interacts. SF-AMS models each memory unit's survival potential Phi, updated by the Composite Importance Score I(m), usage feedback, a diversity modulation, and a global decay lambda. Reinforcement is gated so that frequently accessed, non-redundant, high-importance units persist in a Core layer, while redundant or stale units drift to lower layers and are eventually evicted; retrieval then ranks by survival potential plus semantic and keyword relevance. The paper reports up to +9.65 F1 in multi-hop reasoning, +6.91 F1 in temporal reasoning, a","pith_inferences":["The paper does not validate the LLM salience evaluator's fixed weights (stability 30%, identity 25%, usage 20%, timeliness 15%, emotion 10%) against human ratings or alternative prompts; a direct test would be to ablate those weights and compare against a cheap proxy such as entity frequency plus recency.","The paper's own appended self-review notes no limitations section, no error bars, and no code release at submission; that leaves the headline deltas as unverified point estimates, so a multi-seed replication with variance reporting would be the natural next step.","The appended self-review says LLMs are not part of the proposed method, yet Appendix C.3 uses an LLM prompt as the salience evaluator feeding Eq. 1; a reader should reconcile this contradiction before relying on the component attribution.","If the survival-potential mechanism is truly the cause of the gains, it suggests a general design principle: treat agent memory as a filter that keeps survival space for novel, high-entropy information rather than as an accumulator of all past interactions—a perspective that could transfer to continual learning and online summarization."],"forward_implications":["If memory importance is dynamic, then retrieval and eviction should share one utility signal rather than separate heuristics; the paper attributes its gains to exactly this coupling.","Compact, strategically pruned memory can improve multi-hop and temporal reasoning without increasing context length, even under a tight capacity limit of 300 units.","Tiered forgetting—preserving Core memories while aggressively removing Irrelevant ones—is a concrete mechanism for maintaining a high signal-to-noise ratio over long horizons.","Utility-aware memory regulation can cut LLM interaction overhead: the paper reports 46.7% fewer LLM calls than LangMem and 73.3% fewer than A-MEM on LongMemEval-s while achieving higher accuracy.","The observed cross-backbone gains on both GPT-4o-mini and Qwen2.5-7B suggest the mechanism is not tied to one model family, though the size of the gain varies by task and backbone."],"fun_headline_variants":["Dynamic memory importance boosts LLM agents by 9.65 F1","Strategic forgetting: memory utility update lifts reasoning","Forget strategically, reason better: SF-AMS gains up to 9.65 F1","Survival-based memory pruning sharpens LLM long-context","Utility-driven forgetting beats static retrieval in agents"],"cache_read_input_tokens":2304,"weakest_assumption_plain":"The load-bearing premise is that the LLM salience evaluator in Appendix C.3 returns trustworthy importance scores from a fixed prompt, and the paper does not validate those scores against human judgment, ablate the prompt, or report run-to-run variance—so if that scoring signal is noisy or biased, the survival potential, hierarchy, and retrieval all inherit the noise.","fun_headline_variants_meta":{"raw":{"variants":["Dynamic memory importance boosts LLM agents by 9.65 F1","Strategic forgetting: memory utility update lifts reasoning","Forget strategically, reason better: SF-AMS gains up to 9.65 F1","Survival-based memory pruning sharpens LLM long-context","Utility-driven forgetting beats static retrieval in agents"]},"model":"deepseek-v4-flash","effort":"low","cost_usd":0.000944,"raw_usage":{"total_tokens":3871,"prompt_tokens":749,"completion_tokens":3122,"prompt_tokens_details":{"cached_tokens":256},"prompt_cache_hit_tokens":256,"prompt_cache_miss_tokens":493,"completion_tokens_details":{"reasoning_tokens":3045}},"tokens_in":493,"tokens_out":3122,"duration_ms":22218,"temperature":1.0,"reasoning_tokens":3045,"cache_read_input_tokens":256,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-08-02T12:44:58.997871+00:00","model_set":{"reader":"deepseek-v4-flash"},"falsifier":"Replace the LLM salience evaluator with random scores in [0,1] and rerun the LoCoMo multi-hop and temporal experiments, keeping everything else fixed. If the reported F1 gains over baselines persist, the composite importance signal is not the driver; if they collapse, the claim is supported. A second check: run each configuration several times and test whether the +9.65 F1 margin exceeds run-to-run variance, since no error bars are reported.","supporting_citations":[],"review_version":1}