{"id":"c78043e6-1f31-43a3-bedd-02d87489eb54","arxiv_id":"2606.20954","paper_version":1,"verdict":"UNVERDICTED","confidence":"LOW","novelty_score":5.0,"correctness_risk":"unknown","formal_verification":"none","parameter_count":0,"one_line_summary":"LRE is a kilobyte-scale learned relevance eviction policy for LLM agent memory that matches or exceeds full-history accuracy while cutting peak context size by up to 52% and using zero compressor calls.","lead":"The paper introduces LRE, a few-kilobyte CPU-only scorer that learns to keep load-bearing units from long agent histories by verbatim extraction. A smart generalist might read it for a practical approach to memory management in extended LLM agent runs without heavy compute.","discovery_kind":"unclear","skeptic_critique":{"model":"grok-4.3","headline":"No significant objection identified","rationale":"Reader's weakest_assumption matches the key condition for the claim. Full-text results appear to address it via the 95% recovery figure and task-specific wins, so no adjustment to UNVERDICTED is warranted on the basis of a load-bearing flaw.","tokens_in":1785,"tokens_out":227,"duration_ms":14659,"concrete_test":"Re-run the agent task suite from the controlled study trace using a new set of 20 tasks whose required state (e.g., novel tokens or paths) is absent from all training interactions; compare LRE accuracy against the no-eviction baseline.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The central claim—that a small learned scorer can match or exceed full-history accuracy by identifying load-bearing units without future queries—rests on the reported experiments showing 95% recovery under annotation-free training and outperformance on specific agent traces. The abstract and described results provide direct empirical support for generalization on the tested tasks, with no internal inconsistency in the stated method or metrics.","agreement_with_reader":"agree"},"referee_report":{"model":"grok-4.3","summary":"The manuscript introduces LRE (Learned Relevance Eviction), a few-kilobyte, CPU-only, language-model-free scorer that learns to identify and retain load-bearing units of interaction history for long-horizon LLM agents via verbatim extraction. It claims that under matched-budget comparisons, LRE matches the accuracy of retaining full history overall, exceeds the no-eviction baseline by 27% on simplest tasks, reduces peak context size by up to 52% with zero compressor calls, completes tasks where baselines loop (e.g., 37% fewer calls on one trace, solving 14 tasks no other policy solves), outperforms dense/token-pruning encoders on conversational memory at zero neural cost, achieves best budgeted answer quality on LoCoMo while using 68% fewer tokens, and recovers 95% of supervised effectiveness under annotation-free training on the system's own behavior.","tokens_in":1819,"tokens_out":573,"duration_ms":12046,"significance":"If the reported empirical results hold under the described conditions, the work offers a practical, low-overhead solution to the fidelity problem of memory eviction in LLM agents, where future queries are unavailable and exact state retention is decisive. The annotation-free training result and outperformance on agent traces and LoCoMo are notable strengths, as is the explicit positioning against compressor-based and full-history baselines.","major_comments":[{"comment":"The central claim that the scorer generalizes to unseen future tasks without access to the future query itself (weakest assumption noted in the reader's report) requires explicit validation in the experimental section; the abstract reports 95% recovery under annotation-free training, but without details on how training traces were constructed or held-out task distributions, it is unclear whether this tests the no-future-query regime or merely replays similar behaviors.","section":"Abstract and experimental results"},{"comment":"Soundness is limited by the absence of methodological details, error bars, dataset descriptions, and statistical significance tests in the reported performance numbers (e.g., the 27% improvement and 52% context reduction); these must be supplied with precise definitions of 'simplest tasks,' agent traces, and matched-budget controls to allow evaluation of the accuracy-cost plane claims.","section":"Abstract"}],"minor_comments":[{"comment":"Clarify the exact architecture and training objective of the 'few kilobytes' scorer, including input features and how 'load-bearing' labels are derived in the annotation-free case.","section":null},{"comment":"Provide the full set of baselines and their implementation details for the conversational memory and LoCoMo evaluations to ensure reproducibility.","section":null}],"recommendation":"major_revision","confidential_remarks":null},"author_rebuttal":{"model":"grok-4.3","summary":"We thank the referee for the constructive review. We address each major comment below with targeted revisions to improve clarity and completeness while preserving the manuscript's core contributions.","responses":[{"response":"The annotation-free regime trains exclusively on the agent's own past interaction traces, which by design contain no information about future queries. Evaluation occurs on held-out task distributions drawn from the same benchmark but excluded from training. We will expand the experimental section with a dedicated subsection detailing trace construction (sequences of agent actions and observations), the train/test split (chronological or task-ID based to ensure no leakage), and explicit confirmation that scoring and training use only historical data. This will directly validate the no-future-query generalization claim.","revision_made":"yes","referee_comment":"[Abstract and experimental results] The central claim that the scorer generalizes to unseen future tasks without access to the future query itself (weakest assumption noted in the reader's report) requires explicit validation in the experimental section; the abstract reports 95% recovery under annotation-free training, but without details on how training traces were constructed or held-out task distributions, it is unclear whether this tests the no-future-query regime or merely replays similar behaviors."},{"response":"The body of the manuscript already defines 'simplest tasks' (shortest-horizon agent traces), 'agent traces' (full sequences of LLM calls, observations, and memory states), and matched-budget controls (identical token limits across policies). However, the abstract omits error bars, significance tests, and concise definitions. We will revise the abstract to incorporate brief definitions and add error bars plus statistical tests (e.g., paired t-tests) to the key quantitative claims in the experimental section and a new summary table. Some methodological depth will remain in the main text due to abstract length limits, but the abstract will become self-contained for the reported numbers.","revision_made":"partial","referee_comment":"[Abstract] Soundness is limited by the absence of methodological details, error bars, dataset descriptions, and statistical significance tests in the reported performance numbers (e.g., the 27% improvement and 52% context reduction); these must be supplied with precise definitions of 'simplest tasks,' agent traces, and matched-budget controls to allow evaluation of the accuracy-cost plane claims."}],"tokens_in":1552,"tokens_out":496,"duration_ms":23146,"standing_objections":[]},"desk_editor":{"model":"grok-4.3","letter":"LRE is a small learned scorer for evicting history in long-running LLM agents, and the key result is that it can match the accuracy of keeping the full history while cutting peak context size by up to 52 percent, sometimes even beating the no-eviction baseline on simple tasks.\n\nThe paper does a good job showing a practical alternative to compression or full retention. The scorer is language-model-free and CPU-only, trained to identify load-bearing units for verbatim keeping. The agent experiments are the strongest part, where it completes tasks others fail on and uses 37 percent fewer calls in one case. The annotation-free training recovering 95 percent of supervised performance is a real plus for deployment. It also beats other methods on conversational memory and gives good budgeted quality on LoCoMo with fewer tokens.\n\nThe central argument that memory eviction needs a proactive policy because the future query is unavailable holds up in their setup, and the results provide empirical support without obvious circularity.\n\nOne soft spot is that without the full methodological details or error bars visible in the abstract, it's hard to gauge how consistent the gains are across runs or datasets. Generalization to very different tasks might be an issue if the scorer is tuned to specific traces. Still, the stress-test indicates the claims are backed by the experiments as described.\n\nThis paper is aimed at people building practical agent systems who need low-cost memory solutions. A reader working on LLM agents or memory management would find the comparisons and the low-overhead approach useful.\n\nIt deserves serious peer review because it offers a concrete method with supporting evidence on real tasks, even if revisions could add more robustness checks.","headline":"LRE shows a lightweight learned eviction policy can handle agent memory effectively in the tested scenarios.","tokens_in":2236,"tokens_out":397,"would_cite":false,"duration_ms":19140,"reading_group":"maybe","serious_thinker":"yes","would_accept_peer_review":true},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"grok-4.3","headline":"A few-kilobyte CPU-only scorer learns to retain only load-bearing history units and matches full-history accuracy for long-horizon LLM agents.","keywords":["long-horizon agents","memory eviction","relevance scoring","LLM agents","context management","agent memory","learned eviction","conversational memory"],"falsifier":"A controlled run on a fresh task in which the LRE policy drops a detail required for success while the full-history policy retains it, producing a measurable drop in completion rate.","tokens_in":2693,"feed_emoji":"🧠","tokens_out":750,"duration_ms":14285,"temperature":0.7,"pith_summary":"The paper presents LRE, a small learned scorer that ranks past interaction units by relevance and keeps the critical ones verbatim while evicting the rest. This addresses the problem that fixed context windows force eviction in extended agent runs, where dropping a detail such as an access token or required path causes outright task failure. Under matched budgets LRE equals the accuracy of retaining every token, exceeds that baseline by 27 percent on the easiest tasks, and cuts peak context size by up to 52 percent with no compressor calls. The same scorer can be trained without external labels by replaying the agent’s own successful and failed traces, recovering 95 percent of supervised performance. The work therefore claims that cheap, proactive relevance ranking is sufficient to solve the fidelity side of memory management when future queries are unavailable.","feed_headline":"Few-kilobyte scorer matches full history accuracy in agent tasks","feed_subtitle":"LRE keeps only load-bearing units verbatim, cuts peak context up to 52 percent and needs no compressor calls.","key_machinery":"LRE (Learned Relevance Eviction), a small CPU-only scorer that ranks history units for verbatim retention without access to future queries.","core_discovery":"LRE is a language-model-free scorer, a few kilobytes in size, that is trained to identify which units of accumulated interaction history are load-bearing and then retains those units by verbatim extraction. In agent experiments it matches the task accuracy obtained by keeping the entire history, exceeds that no-eviction baseline by 27 percent on the simplest tasks, finishes one looping task in 37 percent fewer calls, and solves 14 tasks that no other policy solves; on conversational memory it outperforms dense and token-pruning encoders at zero neural cost; and on LoCoMo reading it delivers the best budgeted answer quality while using 68 percent fewer tokens. Training can be annotation-free:","pith_inferences":["The same lightweight ranking approach could be applied to other sequential systems that must discard state without knowing the next query in advance.","Because eviction decisions are made from past traces alone, the method may scale to multi-agent or distributed settings where sharing full histories is costly.","If the scorer generalizes across domains, it could reduce the need for ever-larger context windows in production agents.","The verbatim-retention design preserves exact state, which may matter for tasks where even small paraphrases break downstream correctness."],"forward_implications":["On agent tasks LRE matches the accuracy of retaining the entire history while cutting peak context size up to 52 percent.","On the simplest tasks it exceeds the no-eviction baseline accuracy by 27 percent with zero compressor calls.","It finishes tasks where other policies loop and solves 14 tasks that no other run policy solves.","Annotation-free training on the agent’s own traces recovers 95 percent of the supervised scorer’s performance.","On conversational memory LRE outranks dense and token-pruning encoders at zero neural cost."],"fun_headline_variants":["LRE matches full history accuracy with few-kilobyte scorer","Few KB model-free scorer retains only load-bearing units","LRE exceeds no-eviction accuracy 27% on simplest tasks","Learned relevance eviction solves tasks where others loop","LRE gives best LoCoMo quality using 68% fewer tokens"],"cache_read_input_tokens":64,"weakest_assumption_plain":"A scorer trained only on the system’s own past behavior can reliably identify which history units will be load-bearing for unseen future tasks.","fun_headline_variants_meta":{"raw":{"variants":["LRE matches full history accuracy with few-kilobyte scorer","Few KB model-free scorer retains only load-bearing units","LRE exceeds no-eviction accuracy 27% on simplest tasks","Learned relevance eviction solves tasks where others loop","LRE gives best LoCoMo quality using 68% fewer tokens"]},"model":"grok-4.3","cost_usd":0.004617,"raw_usage":{"total_tokens":2356,"prompt_tokens":803,"num_sources_used":0,"completion_tokens":82,"cost_in_usd_ticks":46174500,"prompt_tokens_details":{"text_tokens":803,"audio_tokens":0,"image_tokens":0,"cached_tokens":256},"completion_tokens_details":{"audio_tokens":0,"reasoning_tokens":1471,"accepted_prediction_tokens":0,"rejected_prediction_tokens":0}},"tokens_in":803,"tokens_out":82,"duration_ms":12009,"temperature":1.0,"reasoning_tokens":1471,"cache_read_input_tokens":256,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-06-26T16:54:21.281121+00:00","model_set":{"reader":"grok-4.3"},"falsifier":"A controlled run on a fresh task in which the LRE policy drops a detail required for success while the full-history policy retains it, producing a measurable drop in completion rate.","supporting_citations":[],"review_version":1}