{"id":"47d9c48f-db76-4afa-9256-0c66f02057e7","arxiv_id":"2607.16621","paper_version":1,"verdict":"CONDITIONAL","confidence":"MODERATE","novelty_score":6.0,"correctness_risk":"medium","formal_verification":"none","parameter_count":8,"one_line_summary":"MSCE, a training-free memory–skill co-evolution framework, converts grounded traces into callable skills through reflection-weighted value backfilling and outperforms prior agent memory/skill baselines on EvoAgentBench and LoCoMo.","lead":"This paper introduces MSCE, a system that lets AI agents turn past interactions into reusable skills, guided by an organized memory hierarchy and feedback. Agents using MSCE score higher on two long-horizon agent benchmarks than several existing memory and skill-augmented methods.","discovery_kind":"new_method","skeptic_critique":{"model":"deepseek-v4-flash","headline":"The headline 'significantly outperforms' is unsupported by any reported variance or significance testing; several key margins are small and could plausibly be within task-level noise.","rationale":"The reader's weakest_assumption focused on the faithfulness of the LLM-based reflection weights α and reward quantification R_i. That is a real concern, and the paper's Limitations section acknowledges it. However, I see an even more load-bearing issue: even if α and R_i are perfect, the central claim of 'significantly outperforming' baselines is only supported by point estimates without variance or significance tests. Several margins are small, and the benchmark/LLM-judge settings inject nontrivial noise. If the differences are within noise, the central claim does not land regardless of how well the internal value signal is calibrated. The reader did mention the absence of error bars and significance tests in the rationale, but did not make it the weakest assumption, so my agreement is partial. My concrete test—rerunning with multiple seeds or bootstrapping tasks and performing paired permutation tests—would directly settle whether the headline empirical claim is statistically robust. This does not change the reader's conditional verdict; it reinforces the need for conditional acceptance pending stronger empirical evidence.","tokens_in":22433,"tokens_out":7283,"duration_ms":84790,"concrete_test":"Using the released repository, rerun EvoAgentBench with at least 5 random seeds (or bootstrap across the N tasks) for MSCE and the strongest non-MSCE baseline in each domain; compute 95% confidence intervals and a paired permutation test on Pass@1. For LoCoMo, repeat the GPT-4o judge scoring 3 times with different answer orders or temperatures and report the spread. If any claimed improvement's confidence interval includes 0 or the permutation p-value exceeds 0.05, the word 'significantly' in the abstract should be retracted or qualified.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The paper's central claim is that MSCE 'significantly outperforms state-of-the-art skill-augmented and memory-driven agent baselines.' The supporting evidence is point estimates in Tables 1 and 2, with no standard errors, confidence intervals, number of seeds, or significance tests. Appendix D.1 states that standard error is computed across tasks, but none are reported. This is load-bearing because several headline margins are modest: LoCoMo overall judge score improves by only +2.01 over SkillFlow-Evolve and F1 by +1.18; on EvoAgentBench, Math improves by +4.00, IR by +4.61, and Code is a tie with EvoSkill at 61.54. Given that the LoCoMo evaluation uses a GPT-4o judge and that the paper's own Limitations section concedes the prompted operators 'may still be noisy or sensitive to model and prompt choices,' the absence of uncertainty quantification leaves open the possibility that the observed differences are within chance. Without variance estimates, the empirical superiority that anchors the paper's main contribution is not yet established.","agreement_with_reader":"partial"},"referee_report":{"model":"deepseek-v4-flash","summary":"The paper proposes MSCE, a training-free memory-skill co-evolution framework for long-horizon LLM agents. It maintains a three-level memory (L1 traces, L2 procedural policies, L3 environmental cognition) and promotes evidence-backed L2 policies with positive estimated gain into callable, verifiable skills. A reflection-weighted value backfilling rule (Eq. 2) propagates sparse terminal feedback through step-level self-reflections, producing trace values that govern retrieval, policy induction, skill crystallization, and skill lifecycle. Experiments on EvoAgentBench and LoCoMo compare MSCE against memory-based and trajectory-to-skill baselines, with ablations, cross-domain transfer, and lifelong-evolution analyses. The paper claims that MSCE \"significantly outperforms\" state-of-the-art baselines and releases code at a public repository.","tokens_in":22758,"tokens_out":4359,"duration_ms":48743,"significance":"If the empirical claims hold, MSCE is a useful contribution: it cleanly separates evidence, procedure, and environmental knowledge; it offers a governed skill-promotion mechanism with deterministic verification; and it is training-free, making it easy to adopt. Strengths include a clearly specified pipeline, detailed prompt templates, a component-level ablation design, and a public code link. The cross-domain and lifelong-evolution results are interesting and go beyond a single benchmark. However, the central empirical claim of \"significantly outperforms\" is not yet supported because no uncertainty quantification is reported, and several headline margins are small. The prompted operators that supply alpha and R are also unvalidated and share a model family with the LoCoMo judge. With added variance reporting and operator validation, this could be a solid system paper.","major_comments":[{"comment":"The abstract and conclusion say MSCE \"significantly outperforms\" baselines, but Tables 1 and 2 report only point estimates of Pass@1, judge score, and cost. Appendix D.1 states that standard error is computed across tasks, yet no standard error, confidence interval, number of trials/seeds, or significance test appears anywhere. This matters because the margins are modest: on LoCoMo the overall judge score improves by +2.01 and F1 by +1.18 over SkillFlow-Evolve, and on EvoAgentBench Code is a tie (61.54) with EvoSkill. Without variance estimates, the observed differences could be within task-level noise. Please report per-task SEs/CIs, trial counts, seeds, and significance tests, or explicitly soften the \"significant\" claim.","section":"§5.2, Tables 1–2, Appendix D.1"},{"comment":"The value backfill and policy gain depend entirely on alpha_t (reflection scorer) and R_i (reward quantification), both produced by prompted GPT-4o operators. The LoCoMo evaluation also uses a GPT-4o judge, so the same model family generates the training signal and scores the result. The Limitations section concedes that these operators \"may still be noisy or sensitive to model and prompt choices.\" If alpha or R are systematically biased, the governance signal degrades and the reported gains may not reproduce. I ask for validation: compare alpha against human labels or an oracle, compare R_i against verifier rewards on domains where they exist, and ablate the judge model family on LoCoMo. Without this, the mechanism's causal story is not established.","section":"§4.4, Eq. (2), Appendices B.3 and E.1, Limitations"},{"comment":"The ablation results are single point estimates with no variance or repeated trials. The claim that \"all components contribute\" rests on differences of a few points, e.g., w/o L3 vs full MSCE on Code (58.97 vs 61.54), w/o Value Calibration on KW (50.00 vs 53.45), and w/o Reflection Weighting on IR (21.54 vs 26.15). Given the small margins and absence of uncertainty, the component-level conclusions are not yet robust. Please provide confidence intervals or at least multiple seeds for the ablations.","section":"§5.4, Table 3"},{"comment":"The stated training objective is cumulative reward sum R_i, but the main evaluation metric is Pass@1, a thresholded success indicator. Since R_i itself is produced by an LLM reward quantifier (Appendix B.3), any systematic correlation between the quantifier and the task success criterion can inflate reported gains without reflecting actual task success. The paper should report the correlation between R_i and the binarized verifier reward, or evaluate a version that uses only verifier rewards on domains where they exist (IR, Math, SE, Code). This would strengthen the link between the training signal and the evaluation metric.","section":"§3 and §5.1"}],"minor_comments":[{"comment":"The affiliation contains a typo: \"MemT ensor\" should be \"MemTensor\". Several \"T able\" instances in the text should be \"Table\".","section":"Author affiliation"},{"comment":"Memento is listed as a baseline but is not described in Related Work; a one-sentence description and explicit reference would help readers understand what it does.","section":"§5.1 Baselines"},{"comment":"The right y-axis (Delta Cost %) is not clearly associated with the bar/line legend. Please state explicitly that bars use the left axis and the line uses the right axis.","section":"Figure 2"},{"comment":"Many hyperparameters (theta_G, theta_sim, theta_edge, reliability thresholds) are listed without sensitivity analysis. Even a brief statement of which are most influential would help assess robustness.","section":"Table 6"},{"comment":"The statement \"standard error computed across tasks\" should be accompanied by the actual values in the main tables, or at least referenced in the main text when reporting results.","section":"Appendix D.1"}],"recommendation":"major_revision","confidential_remarks":"The framework itself is coherent and the paper is well-structured, but the empirical support currently falls short of the paper's stated claims. The absence of error bars and significance tests is the main blocker; the authors likely have per-task results and can compute SEs cheaply. The operator validation and judge-model overlap should also be addressed in the revision. I do not see a fundamental flaw in the method, only a need to substantiate the empirical claims before acceptance."},"author_rebuttal":null,"desk_editor":{"model":"deepseek-v4-flash","letter":"Colleague,\n\nMSCE is worth your time. The framework — three-level memory (traces, policies, environmental cognition) with evidence-linked skill crystallization and reflection-weighted value backfilling — is a sensible and reasonably novel synthesis. It treats skill creation as a governed promotion from memory rather than direct distillation from noisy trajectories, and the ablations in Table 3 suggest each component earns its place. The deterministic verification of drafted skills against evidence anchors is also a nice touch. Credit: the authors ship code, spell out prompts, and they state plainly in the Limitations that their value estimates are heuristic and their prompted operators may be noisy.\n\nThe soft spot is exactly where the reader put it: the empirical core. Tables 1–2 report point estimates only. No error bars, no confidence intervals, no seeds, no significance tests. Several headline margins are small — LoCoMo overall +2.01, F1 +1.18, Math +4.00, and a tie on Code. With task-level variance unknown, 'significantly outperforms' in the abstract is simply not established. The stress-test note is right: the appendix says standard error was computed across tasks, but none appears.\n\nOne additional wrinkle: the same model family (GPT-4o) is used both as an auxiliary operator (reflection scoring, reward quantification) and as the judge on LoCoMo. That's not fatal — the F1 and pass@1-style scores are more objective — but it does muddy the interpretation of the judge-based numbers. The reward quantification prompt is the linchpin for α and R_i in Eq. 2, and its reliability is not independently validated.\n\nNone of this kills the architecture. The ablations show large drops when you flatten memory or remove skill crystallization, and the cross-domain transfer results are consistent. This is a solid system contribution that needs better empirical hygiene. For a serious referee: yes, engage. But the authors need to add variance estimates, significance tests, and ideally an independent judge or at least a sensitivity analysis before the 'significantly outperforms' claim can stand.","headline":"The MSCE framework is a genuinely useful synthesis — but the paper's 'significantly outperforms' is not backed by the reported numbers.","tokens_in":23266,"tokens_out":1970,"would_cite":true,"duration_ms":23131,"reading_group":"yes","serious_thinker":"yes","would_accept_peer_review":true},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"deepseek-v4-flash","headline":"MSCE converts agent experience into evidence-backed callable skills and outperforms prior memory and skill agents on long-horizon benchmarks.","keywords":["memory-skill co-evolution","long-horizon LLM agents","training-free","skill crystallization","reflection-weighted value backfilling","evidence grounding","EvoAgentBench","lifelong learning"],"falsifier":"Run MSCE on EvoAgentBench with reflection weights α_t replaced by random values in [0,1] (or by a constant 0.5) while keeping all other components unchanged. If Pass@1 stays at the reported level, the reflection-weighting mechanism is not doing the claimed causal work; if it drops substantially, the mechanism is load-bearing.","tokens_in":22296,"feed_emoji":"🧠","tokens_out":3509,"duration_ms":36369,"temperature":0.7,"pith_summary":"The paper argues that long-horizon LLM agents improve most when their accumulated experience is governed as a three-level memory hierarchy and promoted into callable skills only after it earns positive, evidence-weighted value. It introduces reflection-weighted value backfilling, which spreads sparse terminal feedback across individual steps according to how trustworthy each step's self-reflection is. The resulting trace values drive retrieval, policy induction, environmental abstraction, and skill crystallization without retraining the model. If correct, this gives a training-free recipe for turning raw interaction logs into reusable procedures, cutting redundant exploration while improving success rates on the tested benchmarks.","feed_headline":"MSCE beats prior agents on all five EvoAgentBench domains","feed_subtitle":"Training-free co-evolution of memory and skills lifts Pass@1 in every tested domain, with lower cost in most.","key_machinery":"The central mechanism is reflection-weighted value backfilling, a recurrence that distributes a single episode reward R_i backwards through the trace: V_t = α_t R_i + (1−α_t)γ V_{t+1}. The weight α_t is produced by a prompted reflection scorer, and this valued trace is the universal currency that decides which traces become L2 policies, which policies become L3 environmental cognition, and which policies are promoted into verified, callable skills with reliability estimates and evidence anchors.","core_discovery":"MSCE establishes that memory and skills can co-evolve through a single value signal: each step trace receives V_t = α_t R_i + (1−α_t)γ V_{t+1}, where α_t is estimated from the step's self-reflection and R_i is the episode-level terminal feedback. This couples dense, possibly noisy reflections with sparse, reliable rewards, so only steps that are both globally rewarded and locally interpretable get high value. High-valued, cross-episode policies are then crystallized into skills only if they retain supporting evidence, show positive gain, and remain stable. Across EvoAgentBench's five domains and LoCoMo, the method reports the best or tied-best Pass@1 in every domain, with cost reductions in","pith_inferences":["The design implies that the quality of the reflection scorer is the real bottleneck: if α_t is systematically biased, the whole value backfill is biased, so gains could degrade on backbones whose self-reflections are less faithful. A natural test is to replace α_t with random weights and measure Pass@1.","The same value-backfill recurrence could serve other credit-assignment settings where terminal rewards are sparse, such as robotics trajectory learning or multi-turn dialogue optimization, since it only requires per-step reflections and final feedback.","The paper's reliance on prompted operators (reflection scoring, reward quantification, policy induction, L3 abstraction, skill drafting) means reproducibility depends on prompt stability; the authors' own Limitations section concedes these outputs may still be noisy or sensitive to model and prompt choices.","If the framework generalizes, the distinction between memory and skill dissolves into a promotion ladder: memory is evidence, skills are deployed policies, and governance is value. That reframing could shape future agent architectures that treat experience as a continuously auditable resource."],"forward_implications":["If MSCE's central claim holds, long-horizon agents can improve across sessions without gradient updates, simply by maintaining governed memory and promoting only evidence-backed, positive-gain procedures into skills.","Repeated failure patterns become reusable anti-patterns: the decision-guidance mechanism converts contrastive evidence into prefer/avoid guidance, so later tasks avoid known pitfalls rather than re-discovering them.","Cross-domain transfer results imply that procedures learned in one environment carry over to other domains, reducing the need for per-domain experience from scratch.","Lifelong evolution results suggest that as accumulated experience grows, success rates keep rising while cost per task eventually falls, supporting a learning-by-using effect.","The separation of trace evidence from promoted skills makes each skill auditable: a skill stays linked to the specific traces that support it, so verification and retirement can be grounded in evidence rather than trust."],"fun_headline_variants":["Training-free memory-skill co-evolution beats all baselines","MSCE co-evolves memory and skills to top every domain","One value signal couples memory and skills for wins","Zero-training co-evolution lifts Pass@1 in all five domains","Co-evolving memory and skills: best Pass@1 across domains"],"cache_read_input_tokens":2304,"weakest_assumption_plain":"The framework assumes the LLM-based reflection scorer and reward quantifier produce trustworthy α_t and R_i; if those prompted numbers are systematically wrong, the value backfill that drives all memory and skill decisions collapses.","fun_headline_variants_meta":{"raw":{"variants":["Training-free memory-skill co-evolution beats all baselines","MSCE co-evolves memory and skills to top every domain","One value signal couples memory and skills for wins","Zero-training co-evolution lifts Pass@1 in all five domains","Co-evolving memory and skills: best Pass@1 across domains"]},"model":"deepseek-v4-flash","effort":"low","cost_usd":0.000205,"raw_usage":{"total_tokens":1210,"prompt_tokens":703,"completion_tokens":507,"prompt_tokens_details":{"cached_tokens":256},"prompt_cache_hit_tokens":256,"prompt_cache_miss_tokens":447,"completion_tokens_details":{"reasoning_tokens":431}},"tokens_in":447,"tokens_out":507,"duration_ms":5372,"temperature":1.0,"reasoning_tokens":431,"cache_read_input_tokens":256,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-08-01T20:23:26.973429+00:00","model_set":{"reader":"deepseek-v4-flash"},"falsifier":"Run MSCE on EvoAgentBench with reflection weights α_t replaced by random values in [0,1] (or by a constant 0.5) while keeping all other components unchanged. If Pass@1 stays at the reported level, the reflection-weighting mechanism is not doing the claimed causal work; if it drops substantially, the mechanism is load-bearing.","supporting_citations":[],"review_version":1}