{"id":"3cd45a4c-e29d-4994-8da0-2cfeab0329ef","arxiv_id":"2604.01886","paper_version":2,"verdict":"UNVERDICTED","confidence":"LOW","novelty_score":5.0,"correctness_risk":"unknown","formal_verification":"none","parameter_count":3,"one_line_summary":"DRL dynamic algorithm configuration trained on small carbon-aware flow-shop instances generalizes and outperforms static tuning as instance complexity grows.","lead":"A DRL policy for dynamically tuning evolutionary algorithms, trained only on small carbon-aware flow-shop instances, transfers to harder unseen instances and beats static tuning as problems get more complex. The result matters for whether expensive online learning is worth the cost in real scheduling systems.","discovery_kind":"new_application","skeptic_critique":{"model":"grok-4.5","headline":"Manuscript text is a different paper; the carbon-aware DRL-DAC claim cannot be audited from the supplied full text.","rationale":"The reader correctly diagnosed a complete mismatch between the claimed paper (math.OC carbon-aware DRL-DAC) and the manuscript body supplied for review (a CV jailbreak paper). With only the abstract available, no experimental design, baseline fairness, statistics, or artifacts can be inspected; confidence must remain low and the verdict UNVERDICTED. My load-bearing concern is identical: the strongest claim is un-auditable until the correct full text is provided. No secondary technical objection about the abstract’s logic is warranted, because the supporting evidence is simply not present. Once the real manuscript appears, the concrete budget-matching check above would be the first decisive test of the reader’s weakest-assumption point.","tokens_in":11089,"tokens_out":477,"duration_ms":5554,"concrete_test":"Replace the supplied full text with the actual PDF/source of arXiv:2604.01886 (or the correct manuscript matching the abstract). Re-run the audit: verify that the static baseline was tuned under the same total wall-clock budget and instance family used for DRL training, and that reported gains on large instances remain after that matching. If the correct text is unavailable or the budget-matching protocol is absent/ambiguous, keep UNVERDICTED.","verdict_should_be":"UNVERDICTED","load_bearing_attack":"The central claim (DRL-DAC trained only on small carbon-aware PFSP instances transfers and outperforms a fair static baseline as instances grow harder) rests entirely on experimental design, baselines, budgets, and results that are not present in the provided full manuscript. The CACHEABLE PAPER SOURCE CONTEXT and FULL TEXT are the unrelated paper “Low-Effort Jailbreak Attacks Against Text-to-Image Safety Filters” (arXiv:2604.01888). No sections, tables, figures, instance generators, wall-clock budgets, or static-tuning protocol for the scheduling study appear. Consequently the reader’s weakest assumption (fairness of the single static configuration under matched budget) cannot be checked, nor can any other load-bearing experimental claim. The abstract alone is insufficient to establish transfer, outperformance, or that training investment “pays off.”","agreement_with_reader":"agree"},"referee_report":{"model":"grok-4.5","summary":"The submission is identified as arXiv:2604.01886, a study claiming that a DRL-based Dynamic Algorithm Configuration (DAC) policy for carbon-aware permutation flow-shop scheduling, trained only on small/simple instances, transfers to unseen and more complex instances and increasingly outperforms a statically tuned baseline as instance characteristics diverge—thereby justifying the training cost. The abstract alone states this transfer and outperformance narrative. The body supplied as the full manuscript, however, is an unrelated paper on low-effort prompt-based jailbreak attacks against text-to-image safety filters (taxonomy of artistic reframing, material substitution, etc., with ASR tables on SDv1.5 and other T2I models). No methods, instance generators, DRL architecture, static-tuning protocol, budgets, tables, or results for the carbon-aware scheduling study appear in the document.","tokens_in":11231,"tokens_out":722,"duration_ms":13812,"significance":"If the abstract’s claims were substantiated—robust transfer of a DRL-DAC policy trained only on small carbon-aware PFSP instances, with clear gains over a fair static baseline precisely when instances become harder—the result would be practically and methodologically useful for deciding when expensive online learning of algorithm control is warranted in real-world scheduling. That significance cannot be assessed: the load-bearing experimental design, baseline fairness, and quantitative results are absent from the supplied manuscript. The T2I jailbreak body that is present is a separate contribution and does not support the claimed scheduling results.","major_comments":[{"comment":"Manuscript identity mismatch: title, paper_id (2604.01886), and abstract describe a DRL-DAC study on carbon-aware PFSP; the full text is the unrelated T2I jailbreak paper (Low-Effort Jailbreak Attacks Against Text-to-Image Safety Filters). No § on problem formulation, DRL-DAC framework, training distribution, transfer protocol, or static baseline exists for the claimed work. The central claim that learning “pays off” under transfer cannot be audited from the document as submitted.","section":null},{"comment":"Fairness of the static baseline (the reader’s weakest assumption) is load-bearing for attributing gains to dynamic control rather than under-tuning or unequal wall-clock budgets. The abstract asserts a “fair point of comparison,” but the manuscript contains no tuning budget, search space, instance family for tuning, or matched-compute protocol. Without those, the transfer/outperformance claim is unsupported.","section":null},{"comment":"Transfer and scaling claims (“as instance characteristics diverge and computational complexities increase, the DRL-learned policy continuously outperforms static tuning”) require held-out instance generators, complexity axes, statistical tests, and error bars. None of these appear; only the abstract asserts them. The abstract alone is insufficient to establish that the training investment is worthwhile.","section":null}],"minor_comments":[],"recommendation":"reject","confidential_remarks":"The body is clearly arXiv:2604.01888 (T2I jailbreaks), not 2604.01886. This looks like a submission/packaging error or contaminated source rather than a reviewable math.OC manuscript. I would not send the T2I paper out under the scheduling title; ask the authors to resubmit the correct PDF. I have not evaluated the scientific merit of either paper’s actual claims beyond noting that the scheduling claims are not present in the file."},"author_rebuttal":null,"desk_editor":{"model":"grok-4.5","letter":"The punchline is simple: the manuscript body supplied for arXiv:2604.01886 is not that paper. It is the full text of an unrelated CV paper on low-effort prompt jailbreaks against text-to-image safety filters (arXiv:2604.01888). Title, abstract, and metadata describe a DRL-based dynamic algorithm configuration study for carbon-aware permutation flow-shop scheduling; the body never mentions scheduling, carbon, evolutionary algorithms, or DAC. So we have only the abstract for the claimed work.\n\nFrom the abstract alone the framing is useful applied work. Training a DRL controller only on small/simple instances, then testing transfer to harder unseen instances against a static tuned baseline, and asking when the training cost pays off, is a legitimate empirical question. If the results hold (comparable on in-distribution cheap instances, continuous outperformance as complexity and distribution shift grow), it gives a concrete condition under which expensive DRL-DAC is worth the investment. That is new relative to generic DAC/DRL papers that do not stress this transfer regime on a real-world green scheduling problem.\n\nEverything else is soft because it is missing. We cannot check whether the static baseline received a matched tuning budget and search space, whether wall-clock or evaluation budgets were equalized on large instances, what the instance generator actually varied, whether error bars or statistical tests exist, or how the DRL architecture and hyperparameters were chosen. The reader’s weakest assumption (fairness of the single static configuration) is exactly the load-bearing claim we cannot inspect. Circularity risk is low in principle—this is a standard train-on-A/test-on-B design—but without numbers or protocols it is unverifiable.\n\nThis is for people who already work on DAC, evolutionary algorithm control, or carbon-aware scheduling and want an empirical transfer story. It is not a theory paper and does not reorganize the field. Until the correct full text, instance generators, and artifacts appear, there is nothing to engage with. I would not bring it to reading group, would not cite it, and would not send the current package to referees; request the real manuscript first. If the real paper matches the abstract and the baseline is clean, then yes, it deserves a serious look.","headline":"The attached full text is a completely different paper (T2I jailbreaks); the DRL-DAC carbon-aware scheduling claims cannot be audited at all.","tokens_in":11898,"tokens_out":562,"would_cite":false,"duration_ms":11342,"reading_group":"no","serious_thinker":"unclear","would_accept_peer_review":false},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":["90B35","68T05","90C59"],"pacs":[],"model":"grok-4.5","headline":"A DRL policy trained only on small carbon-aware scheduling instances transfers to harder ones and beats static tuning once problems diverge.","keywords":["dynamic algorithm configuration","deep reinforcement learning","carbon-aware scheduling","permutation flow-shop","parameter control","transfer learning","evolutionary algorithms"],"falsifier":"Retune the static baseline separately on each complex instance family under a matched total wall-clock budget; if the retuned static method then matches or beats the transferred DRL policy on those hard instances, the claim that learning pays off via generalization fails.","tokens_in":11931,"feed_emoji":"⚙️","tokens_out":758,"duration_ms":20314,"temperature":0.7,"pith_summary":"The paper asks when the high cost of training a deep reinforcement learning controller for dynamic algorithm configuration is justified. The authors train such a controller exclusively on small, simple carbon-aware permutation flow-shop instances, then freeze it and deploy it on both similar and more complex unseen instances. Against a statically tuned baseline prepared under comparable conditions, the learned policy performs about as well on easy instances that resemble training, but pulls ahead steadily as instance characteristics diverge and computational difficulty rises. The result is that the training investment pays off precisely when static tuning cannot adapt to changing problem scenarios. In short, generalization across instance types, not raw performance on the training distribution, is what makes learning worthwhile.","feed_headline":"DRL trained on tiny jobs beats static tuning on hard ones","feed_subtitle":"The training cost of a dynamic config policy is justified once scheduling instances diverge from the training set.","key_machinery":"The DRL-based Dynamic Algorithm Configuration (DAC) policy: an online parameter-control policy for an evolutionary algorithm, trained only on small instances and then transferred without further training.","core_discovery":"Deep reinforcement learning can acquire a dynamic algorithm-configuration policy from small, simple carbon-aware flow-shop instances that remains effective on unseen instances of different types and scales; the policy matches a static baseline on cheap, similar instances and continuously outperforms it as complexity and distributional distance increase.","pith_inferences":["The same transfer pattern may appear in other online configuration settings that face carbon or energy constraints, such as packing or vehicle routing.","A practical break-even calculator could estimate how far a target instance distribution must sit from the training set before DRL training cost is recovered.","Hybrid runtimes that keep a static configuration for easy instances and invoke the DRL policy only on hard ones could further reduce total compute."],"forward_implications":["A single DRL controller trained once on cheap instances can replace repeated static retuning when the instance distribution shifts.","Static parameter tuning of evolutionary algorithms is insufficient for carbon-aware scheduling under changing problem scales.","Transfer performance of DAC policies becomes a practical criterion for deciding whether the training investment is justified.","The value of dynamic control grows with the computational complexity and distributional distance of the target instances."],"fun_headline_variants":["DRL trained on small flow-shops beats static tuning on harder ones","Policy from tiny carbon-aware instances outperforms static config at scale","DRL DAC from simple jobs generalizes past static tuning as instances diverge","Learned dynamic config matches baseline on easy jobs then pulls ahead on hard","Training DRL on cheap instances pays when carbon-aware schedules get complex"],"cache_read_input_tokens":128,"weakest_assumption_plain":"That a single static configuration, tuned under the same computational budget and on the same small-instance family used for DRL training, is a fair and strong enough baseline so that later gains on hard instances can be attributed to dynamic control rather than to under-tuning of the static competitor.","fun_headline_variants_meta":{"raw":{"variants":["DRL trained on small flow-shops beats static tuning on harder ones","Policy from tiny carbon-aware instances outperforms static config at scale","DRL DAC from simple jobs generalizes past static tuning as instances diverge","Learned dynamic config matches baseline on easy jobs then pulls ahead on hard","Training DRL on cheap instances pays when carbon-aware schedules get complex"]},"model":"grok-4.5","effort":"low","cost_usd":0.003354,"raw_usage":{"total_tokens":1135,"prompt_tokens":816,"num_sources_used":0,"completion_tokens":96,"cost_in_usd_ticks":33540000,"prompt_tokens_details":{"text_tokens":816,"audio_tokens":0,"image_tokens":0,"cached_tokens":128},"completion_tokens_details":{"audio_tokens":0,"reasoning_tokens":223,"accepted_prediction_tokens":0,"rejected_prediction_tokens":0}},"tokens_in":816,"tokens_out":96,"duration_ms":3151,"temperature":1.0,"reasoning_tokens":223,"cache_read_input_tokens":128,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-07-13T14:08:23.350486+00:00","model_set":{"reader":"grok-4.5"},"falsifier":"Retune the static baseline separately on each complex instance family under a matched total wall-clock budget; if the retuned static method then matches or beats the transferred DRL policy on those hard instances, the claim that learning pays off via generalization fails.","supporting_citations":[],"review_version":1}