{"id":"b80111e2-ec0c-459d-a67e-bc9044ba58d4","arxiv_id":"2608.01234","paper_version":1,"verdict":"CONDITIONAL","confidence":"MODERATE","novelty_score":5.0,"correctness_risk":"medium","formal_verification":"none","parameter_count":8,"one_line_summary":"COVE coordinates memory-based and parameter-based LLM self-evolution by routing tasks, scheduling training on plateau or cold-start signals, and keeping volatile API knowledge out of model weights via an anti-recitation penalty.","lead":"The paper introduces COVE, a framework that lets LLM agents decide whether to store new experience in an external memory or bake it into model weights, and when to switch between the two. Its experiments suggest this coordination keeps performance comparable to full retraining while using far fewer training tokens.","discovery_kind":"new_method","skeptic_critique":{"model":"deepseek-v4-flash","headline":"Router reliability is untested: only cross-backbone consistency is reported, and the Hybrid evidence is measured on router-selected instances.","rationale":"The reader's weakest_assumption identifies the router reliability as the load-bearing premise. I agree: the paper provides no direct evidence of router accuracy, only cross-backbone consistency, which does not measure correctness. The Hybrid column in Table 2 is doubly dependent on the router—both for selecting the subset and for assigning training channels—making it an unconvincing demonstration of coordination value. A randomized router achieves nearly the same overall performance as COVE in Table 3, reinforcing that the router's specific decisions may not be essential. This does not invalidate the paper but makes the central coordination claim conditional on a missing validation. The reader's CONDITIONAL verdict is appropriate; my concern reinforces it rather than changing it. I also considered the absence of error bars on the small performance margins, but that is a reporting issue that would affect all methods equally; the router concern is more specific to the paper's core contribution.","tokens_in":14093,"tokens_out":9394,"duration_ms":95411,"concrete_test":"Create a labeled routing validation set (e.g., 50 instances per task from APPS, MATH, TableQA, HotpotQA) with oracle channel assignments determined by human annotation of knowledge volatility or by comparing Parametric-only and Harness-only performance on held-out splits. Compute the router's accuracy and confusion matrix. Then re-run the Hybrid-subset evaluation using an oracle router instead of the learned router; if the COVE advantage over Parametric-only (24.1 vs 21.3) disappears or reverses, the router's reliability is the load-bearing factor.","verdict_should_be":"UNCHANGED","load_bearing_attack":"COVE's central claim hinges on the Task-aware Router making correct channel assignments (Section 4.2). The only validation offered is a cross-backbone consistency check (footnote 1, Section 5.3): >95% agreement across Qwen3-8B, GPT-4o-mini, and DeepSeek-v3.2. Consistency is not accuracy; two strong LLMs can share the same systematic biases. No labeled routing validation set is reported, so misrouting rates are unknown. Moreover, the strongest evidence for coordination—the Hybrid column in Table 2 (24.1% vs 21.3% for Parametric-only)—is computed on the subset the router itself selects. If the router systematically misroutes volatile interface knowledge into the parametric channel or stable knowledge into the harness, the claimed performance and efficiency advantages (Table 2, Table 3) would shrink or disappear. The ablation in Table 3 shows Random-Route performs within 2.3 points of Ours (62.7 vs 65.0), suggesting that routing quality may have a minor effect. Without an accuracy measure, the router's contribution to the stated gains is not established.","agreement_with_reader":"agree"},"referee_report":{"model":"deepseek-v4-flash","summary":"The paper proposes COVE, a framework for LLM-agent self-evolution that coordinates an external memory/harness channel with a parametric fine-tuning channel. COVE uses a Task-aware Router to assign tasks to harness-only, parametric-candidate, or hybrid processing; a Stage-aware Scheduler to decide when to trigger parametric updates based on plateau, data sufficiency, and cold-start conditions; and KnowledgePO, a dual-modal knowledge-optimization scheme that labels knowledge as volatile/stable/strategic, applies an anti-recitation reward to prevent volatile interface knowledge from being memorized, and releases stable memories from online retrieval after internalization. The empirical evaluation compares COVE against Evo-Memory, Self-Challenging, and channel-only ablations on five benchmarks (Lean4, APPS, TableQA, HotpotQA, MATH), reporting success rates and amortized per-instance training-token costs. The main claimed result is that COVE outperforms single-channel strategies and achieves lower parametric-training cost, with the strongest coordination evidence on a router-selected hybrid subset (24.1% vs. 21.3% for Parametric-only).","tokens_in":14375,"tokens_out":3243,"duration_ms":37164,"significance":"The coordination question is timely and the proposed design is well motivated. The diagnostic analyses in Section 3, particularly the API-renaming result in Table 1 and the Lean error-type analysis in Figure 3, are useful and give concrete evidence for the volatility/complementarity story. The anti-recitation reward is a plausible mechanism for preventing parametric internalization of unstable interface knowledge, and the memory-release idea addresses a real weakness of ever-growing memory systems. If the central empirical claims were supported by stronger statistical evidence and by a validated router, the paper would constitute a meaningful step toward principled hybrid self-evolution. The paper also provides a token-accounting protocol (Appendix A), which is commendable transparency even though the protocol itself raises questions.","major_comments":[{"comment":"The abstract and Section 5.2 state that COVE 'outperforms single-channel evolution strategies,' but the reported differences are small on four of five tasks: Lean4 7.0 vs. 6.2, APPS 33.4 vs. 33.1, HotpotQA 69.6 vs. 69.4, MATH 91.7 vs. 91.6. No error bars, confidence intervals, or significance tests are reported anywhere in the paper. Without repeated runs or statistical testing, the 'outperforms' claim is not established for these margins. Please provide variance estimates or significance tests, or soften the claim to 'competitive with occasional gains.'","section":"§5.2, Table 2"},{"comment":"The router is load-bearing for both the coordination claim and the efficiency claim, yet its accuracy is never measured. Footnote 1 reports only cross-backbone consistency (>95% agreement among Qwen3-8B, GPT-4o-mini, DeepSeek-v3.2), which does not establish correctness; consistent judges can share systematic biases. No labeled routing-validation set or confusion-matrix analysis is reported. In addition, Table 3 shows Random-Route reaches 62.7 vs. Ours 65.0, a 2.3-point gap, which suggests that routing decisions have only a modest effect on average performance in this ablation. Provide a labeled evaluation of router decisions (e.g., against human/rule-based labels on a held-out sample) and report routing accuracy per channel.","section":"§5.3, Table 3 and footnote 1"},{"comment":"The strongest evidence for the benefit of coordination is the Hybrid column in Table 2: COVE's 24.1% vs. Parametric-only's 21.3%. This subset is constructed by taking instances 'that the router classifies as hybrid.' Measuring a method on a subset selected by its own router confounds routing quality with downstream performance; the 24.1% figure is not an independent test of the coordination hypothesis. Please evaluate the hybrid mechanism on a pre-defined held-out set of tasks that are known to require both memory and parametric competence, or report the full-task success and the router's precision/recall for hybrid assignment.","section":"§5.2, Hybrid column"},{"comment":"The headline efficiency claim (86% fewer training tokens) rests on an accounting model in which train_tokens = n_param × 14100 and train_steps = n_param × 0.125 for the routing analysis. These constants are presented without derivation, measurement, or sensitivity analysis. Since Self-Challenging is charged a flat 28.3K tokens per task and the harness channel is free, the reported token savings depend on assumptions that favor the selective router. Please provide the measured or derived basis for the 14100-token and 0.125-step constants, report the variance in completion lengths, and run a sensitivity analysis over the constants to show that the qualitative efficiency conclusion is robust.","section":"Appendix A and Table 2/Table 3"},{"comment":"Memory release is a stated component of KnowledgePO, but no experiment evaluates it: there is no measurement of how many memories were released, the A/B threshold used, or the effect on downstream task performance or context cost. If the release mechanism is intended as a contribution, it needs direct evaluation; otherwise it should be framed as a design choice rather than a validated component.","section":"§4.4.2"}],"minor_comments":[{"comment":"The figure contains the typo 'Anti-Recitation Rward' (should be 'Reward'). Also, the figure is dense; consider enlarging the router/scheduler text or providing a separate detailed diagram.","section":"Figure 5"},{"comment":"The channel labels 'harness_only', 'parametric_candidate', and 'hybrid' are sometimes rendered with underscores and sometimes in prose; unify the notation.","section":"§4.2"},{"comment":"The table header for 'Hybrid' is formatted as 'Hybrid Avg. Tok.' and the Avg. Tok. column appears to be merged with the last numeric column. Separating the 'Hybrid Succ.' column from 'Avg. Tok.' would improve readability.","section":"Table 2"},{"comment":"The phrase 'training-set-independent manner' is ambiguous: the model uses a per-task constant anchored to 'measured GRPO completion length' but the exact measurement is not described. Clarify which runs produced the 14100-token constant.","section":"Appendix A"},{"comment":"Several preprints are dated 2026 (e.g., [2], [28]) and the arXiv IDs are not provided for all; please verify the citations and add arXiv/DOI identifiers where available.","section":"References"},{"comment":"The paper says 'we also include two ablation variants' but then lists Harness-only and Parametric-only as baselines in Table 2; clarify which comparisons are baselines versus ablations.","section":"§5.1"}],"recommendation":"major_revision","confidential_remarks":"The paper is technically interesting and the central coordination idea is worth pursuing, but the empirical support for the strongest claims is thinner than the abstract suggests. The two load-bearing issues are (1) lack of statistical grounding for small performance differences in Table 2, and (2) lack of router-accuracy validation combined with a Hybrid subset selected by the router itself. These are addressable in a revision and do not require rejecting the manuscript's scope. I would also check the token-accounting assumptions carefully before publication, since the efficiency claim may be the most cited part of the paper."},"author_rebuttal":null,"desk_editor":{"model":"deepseek-v4-flash","letter":"The short version: this is a real framework paper, not a toy. COVE combines external memory and parameter updates through a router, a stage-aware scheduler, and volatility-aware knowledge optimization. The anti-recitation reward is the standout idea — training the model to avoid memorizing interface names by penalizing stale API calls, and showing it stays robust to API renaming. That held up in the TableQA experiment, and it's a genuinely useful trick for deployed agents.\n\nThe paper also motivates the problem well. The Lean diagnostics (harness memory saturating with little success gain) and the API-renaming drop (96.5% to 54% API correctness) are clear demonstrations of the two failure modes. The framework is coherent, and the token accounting is transparent even if its assumptions are debatable.\n\nThe soft spots are mostly about evidence, not architecture.\n\nFirst, the headline margins over Parametric-only are small: 0.3 to 3.0 points across tasks, with no error bars or significance tests. On HotpotQA and MATH the gaps are 0.1 and 0.3 points, which could easily be noise. \"Outperforms\" is doing too much work.\n\nSecond, the 86% token savings come from a modeled cost: 14,100 tokens per routed instance and 0.125 steps per instance, with the harness channel free. That's a reasonable accounting unit for comparing methods, but it's not an end-to-end measured cost. Real savings could be smaller once you count evaluation, retrieval, and router overhead.\n\nThird, the strongest evidence for coordination is the Hybrid column, but that subset is selected by the router itself, not an external test distribution. The paper is transparent about this, but it means the 24.1% vs 21.3% gap is on a self-selected sample and should be read cautiously.\n\nFourth, the router's accuracy is never measured. The 95% cross-backbone consistency check shows decisions are reproducible, not correct. The routing ablation partially mitigates this: Ours beats Random-Route by 2.3 points in success rate (65.0 vs 62.7) with about a third of the token cost, so the router's main demonstrated benefit is efficiency, not raw performance. Also, an uncoordinated Always-Both baseline does exist in Table 3 — the reader report was wrong to say it's missing; it just doesn't appear in the main table.\n\nMinor: the manuscript still has bibliographic placeholders (\"Conference acronym ’XX, Woodstock, NY\"), which suggests a hasty assembly.\n\nIf you work on continual learning for LLM agents, this is worth reading: it gives a structured template for deciding what to remember versus what to internalize. The ideas are solid and the anti-recitation mechanism is a keeper, but the limitations are fixable with better reporting — error bars, a labeled routing validation set, and an end-to-end token measurement. I'd send it to peer review with the expectation of major revisions.","headline":"Coherent framework and a genuine anti-recitation idea, but the empirical case is thinner than the abstract implies—worth reviewing, not accepting as-is.","tokens_in":14876,"tokens_out":4490,"would_cite":false,"duration_ms":42138,"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":"An LLM agent that routes each piece of feedback to either its external memory or its model weights, based on how volatile the knowledge is, improves more robustly and 86% more cheaply than using either channel alone.","keywords":["LLM agents","self-evolution","memory-parameter coordination","knowledge volatility","task-aware routing","anti-recitation","stage-aware scheduling"],"falsifier":"Measure the router's decisions against tasks with known optimal channels (e.g., synthetic tasks with injected volatility), or replace router choices with random channel assignment and check whether the hybrid-subset advantage and the 86% token savings survive.","tokens_in":13972,"feed_emoji":"🧠","tokens_out":6086,"duration_ms":55054,"temperature":0.7,"pith_summary":"LLM agents in the wild face changing APIs and user demands, and they can improve from feedback two ways: by editing external memories (the harness) or by updating their own weights. This paper argues those channels are complementary and should be coordinated rather than chosen. It presents COVE, which routes each task to the channel that matches its knowledge structure, switches channels when performance plateaus or cold-starts, and uses volatility labels plus an anti-recitation penalty to keep fragile interface names out of the weights. On coding, theorem proving, QA, and math benchmarks, COVE beats both single-channel variants and the two baseline paradigms, and it does so with 86% fewer parametric training tokens. The strongest gains appear on tasks the router marks 'hybrid', where both channels are active.","feed_headline":"86% fewer tokens: coordinated self-evolution beats single-channel","feed_subtitle":"COVE routes each task to memory or weights by knowledge volatility, beating both single-channel paradigms at lower cost.","key_machinery":"Knowledge volatility as the organizing criterion. The paper defines three labels — volatile (API names, schemas), stable (proof tactics, reasoning patterns), strategic (debugging plans) — and makes them the basis of the router's channel choice and of an anti-recitation reward that subtracts a penalty when the model calls a volatile name absent from the current context. This reward is what keeps volatile knowledge in the harness.","core_discovery":"The central claim is that an agent's self-evolution should be driven by the volatility and reuse structure of the knowledge in each piece of feedback. COVE instantiates this as three mechanisms: a Task-aware Router that decides, from the task description plus execution feedback, whether a task should be learned through the harness (memory), through parameters, or both; a Stage-aware Scheduler that starts harness exploration and triggers parametric training only on plateau, enough data, or cold-start failure; and KnowledgePO, which labels memories as volatile/stable/strategic, wraps volatile entries so the model reads current interfaces instead of reciting old ones, and releases internalized","pith_inferences":["The router is evaluated only on router-selected instances; an editorial inference is that the hybrid gain might shrink if hybrid routing errors are measured against a labeled channel-assignment ground truth.","The volatility labels currently come from a prompted judge; one could extend COVE by predicting volatility from historical revision statistics alone, which the paper partially does by promoting repeatedly revised entries.","The same coordination could apply to knowledge stored in code libraries and documentation, where version-sensitive surfaces are automatically volatile.","A natural test is scaling: as the base model gets stronger, the share of tasks routed to the parametric channel might drop, suggesting the framework also serves as a diagnostic of model competence."],"forward_implications":["COVE's hybrid subset (24.1% vs 21.3% parametric-only) suggests tasks that engage both channels are where coordination pays.","The 86% training-token reduction implies selective routing can make self-evolution much cheaper while improving robustness.","The anti-recitation treatment preserves API-call correctness after renaming (92.5% vs 54.0%), implying volatile interface knowledge should never enter weights.","Stage-aware scheduling (plateau, data sufficiency, cold-start) replaces fixed training schedules and can be applied to other agent-training pipelines.","Treating feedback as heterogeneous knowledge rather than uniform training data is a general design principle for self-evolving agents."],"supporting_citations":[{"why":"Evo-Memory: the harness-based self-evolution baseline COVE must beat, whose memory pipeline COVE extends.","marker":"[18]"},{"why":"Self-Challenging: the parametric-only self-evolution baseline; COVE compares against its reproduced training protocol.","marker":"[28]"},{"why":"MiniF2F: source of Lean theorem-proving trajectories for the task-dimension analysis showing harness evolution's limits.","marker":"[27]"},{"why":"WikiTableQuestions: source of the table-QA trajectories for the API-renaming analysis showing parametric brittleness.","marker":"[12]"},{"why":"Qwen3: the 8B base model used for all experiments and backbone for router consistency checks.","marker":"[20]"},{"why":"APPS: coding benchmark used in main results and routing analysis.","marker":"[5]"},{"why":"MATH: math benchmark used in main results and routing analysis.","marker":"[6]"},{"why":"HotpotQA: multi-hop QA benchmark used in main results and routing analysis.","marker":"[21]"}],"fun_headline_variants":["COVE: route tasks to memory or weights by knowledge volatility","Volatility-driven self-evolution: coordinate memory and parameters","Adaptive memory-parameter coordination beats single-channel evolution","Know what to remember, what to internalize: COVE's routing"],"cache_read_input_tokens":2816,"weakest_assumption_plain":"The Task-aware Router's channel assignments are reliable, but the paper does not report router accuracy on a labeled validation set; if the judge frequently sends volatile tasks to the parametric channel or stable tasks to the harness, coordination gains and token savings shrink.","fun_headline_variants_meta":{"raw":{"variants":["COVE: route tasks to memory or weights by knowledge volatility","Volatility-driven self-evolution: coordinate memory and parameters","Adaptive memory-parameter coordination beats single-channel evolution","Know what to remember, what to internalize: COVE's routing"]},"model":"deepseek-v4-flash","effort":"low","cost_usd":0.000178,"raw_usage":{"total_tokens":1105,"prompt_tokens":691,"completion_tokens":414,"prompt_tokens_details":{"cached_tokens":256},"prompt_cache_hit_tokens":256,"prompt_cache_miss_tokens":435,"completion_tokens_details":{"reasoning_tokens":344}},"tokens_in":435,"tokens_out":414,"duration_ms":4728,"temperature":1.0,"reasoning_tokens":344,"cache_read_input_tokens":256,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-08-06T00:26:09.508056+00:00","model_set":{"reader":"deepseek-v4-flash"},"falsifier":"Measure the router's decisions against tasks with known optimal channels (e.g., synthetic tasks with injected volatility), or replace router choices with random channel assignment and check whether the hybrid-subset advantage and the 86% token savings survive.","supporting_citations":[{"cited_title":null,"cited_arxiv_id":null,"evidence_quote":"WikiTableQuestions: source of the table-QA trajectories for the API-renaming analysis showing parametric brittleness."}],"review_version":1}