{"id":"c15e7786-c5f7-402b-a87f-6927049fd6d3","arxiv_id":"2603.16250","paper_version":2,"verdict":"UNVERDICTED","confidence":"LOW","novelty_score":5.0,"correctness_risk":"high","formal_verification":"none","parameter_count":0,"one_line_summary":"SEVEX explores abstract visual-prompt ideas with novelty-guided agent experiments and reports large gains on BlindTest and BLINK over baselines.","lead":"The paper claims an automated agent framework (SEVEX) can discover reusable visual prompts that fix LVLM perception failures better than prior tool-selection methods. A smart generalist might care because it targets a practical bottleneck: making multimodal models see reliably without per-image human hacking.","discovery_kind":"new_method","skeptic_critique":{"model":"grok-4.5","headline":"Manuscript mismatch blocks any audit of SEVEX claims; central claim remains unverifiable.","rationale":"The Reader correctly diagnosed that the full manuscript text does not belong to 2603.16250 and therefore treated the review as abstract-only, returning UNVERDICTED with low confidence. That diagnosis is load-bearing and still decisive: without the real methods, experiments, and figures, neither the performance claims nor the premise that abstract-idea-space agent search yields transferable task-wise prompts can be validated or falsified. No secondary methodological critique of SEVEX is possible or useful until the correct manuscript is present. Verdict remains UNVERDICTED; no adjustment is warranted.","tokens_in":10696,"tokens_out":475,"duration_ms":17724,"concrete_test":"Fetch the actual arXiv:2603.16250 PDF (or camera-ready source) and confirm (1) it contains SEVEX, BlindTest/BLINK results, and visual-prompt examples matching the abstract, and (2) the reported accuracy/efficiency gains vs. named baselines hold under the paper’s own evaluation protocol; if the PDF is the Nefertiti cosmology paper or lacks those results, the central claim stays unsubstantiated.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The central claim (SEVEX discovers task-wise visual prompts that significantly beat baselines on BlindTest/BLINK in accuracy, inference efficiency, exploration efficiency/stability, including counter-intuitive strategies) cannot be checked at all from the supplied source. The CACHEABLE full manuscript is a different paper entirely—cosmological hydrodynamical simulations of clustering dark energy with Nefertiti (arXiv:2603.16248-class content: fluid EoS, power spectra, halo profiles, no LVLMs, no visual prompts, no BlindTest/BLINK). There are no SEVEX algorithm details, no abstract-idea-space search procedure, no novelty-guided selection results, no tables/figures of task accuracy or exploration metrics, and no discovered prompts. The abstract’s framing that empirical black-box search in idea space suffices to fix root LVLM perception failures is therefore unsupported by any evidence in the provided text. This is more fundamental than any methodological assumption: the claimed paper’s body is absent.","agreement_with_reader":"agree"},"referee_report":{"model":"grok-4.5","summary":"The submission is titled and abstracted as a computer-vision paper introducing SEVEX, an automated semantic-exploration framework that searches an abstract idea space (with novelty-guided selection and semantic feedback) to discover task-wise visual prompts for large vision-language models, claiming significant gains over baselines on BlindTest and BLINK in accuracy, inference efficiency, exploration efficiency, and stability, including counter-intuitive strategies beyond tool selection. The body of the manuscript actually supplied, however, is an unrelated cosmology paper presenting the first Nefertiti hydrodynamical simulations of clustering dark energy treated as an effective fluid (EoS with w and cs, continuity/Euler/Poisson system, finite-volume MUSCL-Hancock integration, power spectra, halo density profiles, and comparison to k-evolution). No SEVEX algorithm, idea-space search, visual-prompt results, or LVLM benchmarks appear in the full text.","tokens_in":10930,"tokens_out":798,"duration_ms":12459,"significance":"If the abstract’s claims were substantiated by a matching manuscript, automated discovery of transferable task-wise visual prompts that mitigate root LVLM perception failures would be a useful contribution to multimodal robustness and would reduce reliance on manual trial-and-error. The supplied body instead reports a different, potentially valuable result in cosmology (stable nonlinear clustering-DE simulations without the instabilities reported for EFT scalar formulations, ~10% DE contribution inside massive halos). Because the two documents do not describe the same work, neither contribution can be properly assessed or credited under the stated title and abstract.","major_comments":[{"comment":"Title/abstract vs. full text: the manuscript body is the Nefertiti clustering-dark-energy paper (fluid EoS Eq. (1), continuity/Euler Eqs. (2)–(3), Poisson Eq. (4), power spectra Figs. 1–3, halo profiles Fig. 4, slices Fig. 5, Appendices A–B). It contains no definition of SEVEX, no abstract idea space, no novelty-guided selection, no semantic feedback loop, no BlindTest/BLINK evaluation, and no visual prompts. The central claims of the abstract are therefore unsupported by any evidence in the submitted full text and cannot be refereed.","section":null},{"comment":"Because the body is a different paper, load-bearing elements required for the CV claims—algorithm pseudocode, search-space construction, selection/ideation criteria, baselines, ablations, statistical significance of accuracy/efficiency gains, and examples of discovered counter-intuitive prompts—are entirely absent. No revision of the cosmology text can repair this; a correct matching manuscript would be required.","section":null},{"comment":"Even treating the cosmology body on its own terms (if the packaging error were ignored), the abstract and title still advertise SEVEX/LVLM results, so the submission as a whole is incoherent and fails basic integrity checks for peer review.","section":null}],"minor_comments":[{"comment":"The cosmology manuscript itself has ordinary presentation issues (e.g., ‘predictoin’ typo in Fig. 2 caption; occasional OCR/encoding artifacts in equations) that would be minor if that paper were under review under its own title.","section":null}],"recommendation":"reject","confidential_remarks":"This appears to be a severe manuscript-packaging or arXiv-ID mismatch (abstract 2603.16250 cs.CV vs. body content of the Nefertiti cosmology paper, ~2603.16248). I cannot evaluate SEVEX at all. Recommend desk rejection or return to authors for the correct PDF; do not send the cosmology body out under the CV title. No evidence of author misconduct beyond the mismatch is visible from the files alone."},"author_rebuttal":null,"desk_editor":{"model":"grok-4.5","letter":"The one thing you need to know: the cacheable “full manuscript” is not Visual Prompt Discovery via Semantic Exploration. It is a cosmology paper on clustering dark energy with Nefertiti (fluid EoS, power spectra, halo profiles). So for 2603.16250 we effectively have an abstract only. I cannot check SEVEX, the idea-space search, novelty-guided selection, semantic feedback, or any BlindTest/BLINK numbers.\n\nWhat the abstract claims is clear and, if true, useful in multimodal practice. Prior visual-prompt work is cast as tool selection or manual trial-and-error; they want task-wise (not per-sample) automated discovery via an abstract idea space plus agent-driven empirical trials, and they report gains on accuracy, inference cost, exploration efficiency, and stability, including counter-intuitive strategies. That framing is a legitimate methodological step if the body delivers it. Novelty looks incremental rather than foundational; significance is real for people who ship LVLM perception fixes, not a rewrite of learning theory.\n\nSoft spots are not subtle method flaws—they are absence of evidence in the supplied source. No algorithm details, no ablations, no tables, no discovered prompts, no code. The load-bearing premise (black-box search in idea space can fix root perception failures under model opacity and transfer task-wise) is stated, not shown. Mild train/test leakage risk for any agent scored on the same benchmarks is normal for this genre and uncheckable here. Circularity is not structural from the abstract alone.\n\nWho this is for: people working on visual prompting and LVLM evaluation. Right now it is not for a reading group or for citation, because the body we were given is the wrong paper. A serious editor would not send this package to referees until the correct manuscript, results, and preferably code are attached. If the real SEVEX paper matches the abstract’s claims with solid experiments, it would deserve a full review; on the present materials, it does not.\n\nRecommendation: do not engage further until the correct full text for 2603.16250 is available. Treat the cosmology manuscript as a separate, unrelated work.","headline":"We only have the SEVEX abstract; the attached full text is a different cosmology paper, so none of the BlindTest/BLINK claims can be audited.","tokens_in":11519,"tokens_out":545,"would_cite":false,"duration_ms":11408,"reading_group":"no","serious_thinker":"unclear","would_accept_peer_review":false},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"grok-4.5","headline":"An automated semantic explorer finds task-wise visual prompts that fix LVLM perception failures without per-sample manual trial-and-error.","keywords":["visual prompts","large vision-language models","semantic exploration","SEVEX","perception failures","task-wise prompting","agent-driven experiments","image manipulation"],"falsifier":"Run the same exploration budget on BlindTest and BLINK with SEVEX versus strong tool-selection and per-sample baselines: if SEVEX does not raise task accuracy while also improving exploration efficiency and stability, or if the discovered prompts fail to transfer task-wise, the central claim fails.","tokens_in":11604,"feed_emoji":"👁️","tokens_out":823,"duration_ms":14116,"temperature":0.7,"pith_summary":"Large vision-language models still fail on basic image understanding and visual reasoning. Visual prompts—code that manipulates the image before the model sees it—can reduce those failures, but prior work mostly picks among fixed tools and still depends on human trial-and-error because the models are opaque. This paper argues that the right unit of search is not low-level code or a one-off fix per image, but reusable, task-level visual strategies discovered by automated experiments. It introduces SEVEX, which searches an abstract idea space, selects candidates by novelty, and refines ideas from semantic feedback on empirical outcomes. On perception-focused benchmarks, the discovered prompts improve accuracy while cutting inference and exploration cost and raising stability, and they include counter-intuitive strategies that go beyond ordinary tool use.","feed_headline":"AI finds image tricks that fix vision-model blind spots","feed_subtitle":"SEVEX searches abstract visual ideas and beats manual tool picking on perception tests","key_machinery":"SEVEX: a semantic exploration loop that searches an abstract idea space (instead of raw low-level image-manipulation code), selects candidates with a novelty-guided rule, and ideates new prompts from semantic feedback on empirical results, yielding reusable task-wise visual prompts.","core_discovery":"SEVEX can automatically discover task-wise visual prompts that meaningfully reduce LVLM perception failures. By treating abstract visual ideas as the search space and driving exploration with novelty selection plus semantic feedback from real model outcomes, the method outperforms baselines on BlindTest and BLINK in accuracy, inference efficiency, exploration efficiency, and stability, and surfaces sophisticated strategies that conventional tool-selection approaches miss.","pith_inferences":["The same idea-space loop could be applied to other black-box multimodal failures (e.g., OCR, spatial counting, or chart reading) where code-based input transforms are available.","If abstract-idea search is the right abstraction, hybrid systems that mix a few hand-written visual strategies with SEVEX-style expansion may outperform pure tool routers.","Persistent failure modes after SEVEX would point to perception errors that no pre-model image transform can fix, clarifying the boundary between prompt engineering and model redesign."],"forward_implications":["Task-level visual prompt libraries can be built once per perception task instead of regenerating prompts per image.","Agent-driven empirical search can replace much of the human trial-and-error currently used to design visual prompts.","Counter-intuitive image manipulations become usable assets for LVLM perception once discovery is automated.","Evaluation of visual prompting can shift from tool catalogs toward measured exploration efficiency and stability on perception benchmarks."],"fun_headline_variants":["SEVEX auto-discovers visual prompts fixing LVLM perception failures","Semantic search finds task-wise image prompts that cut vision blind spots","Abstract-idea exploration yields sophisticated prompts for LVLMs","Novelty-guided SEVEX uncovers non-obvious visual strategies for models","Agent-driven trials surface visual prompts beating tool-selection baselines"],"cache_read_input_tokens":128,"weakest_assumption_plain":"That black-box trials over an abstract idea space are enough to find visual prompts that transfer across a whole task and actually address root perception failures, without needing internal diagnosis of the model.","fun_headline_variants_meta":{"raw":{"variants":["SEVEX auto-discovers visual prompts fixing LVLM perception failures","Semantic search finds task-wise image prompts that cut vision blind spots","Abstract-idea exploration yields sophisticated prompts for LVLMs","Novelty-guided SEVEX uncovers non-obvious visual strategies for models","Agent-driven trials surface visual prompts beating tool-selection baselines"]},"model":"grok-4.5","effort":"low","cost_usd":0.003552,"raw_usage":{"total_tokens":1176,"prompt_tokens":828,"num_sources_used":0,"completion_tokens":74,"cost_in_usd_ticks":35520000,"prompt_tokens_details":{"text_tokens":828,"audio_tokens":0,"image_tokens":0,"cached_tokens":128},"completion_tokens_details":{"audio_tokens":0,"reasoning_tokens":274,"accepted_prediction_tokens":0,"rejected_prediction_tokens":0}},"tokens_in":828,"tokens_out":74,"duration_ms":3122,"temperature":1.0,"reasoning_tokens":274,"cache_read_input_tokens":128,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-07-13T23:52:45.394006+00:00","model_set":{"reader":"grok-4.5"},"falsifier":"Run the same exploration budget on BlindTest and BLINK with SEVEX versus strong tool-selection and per-sample baselines: if SEVEX does not raise task accuracy while also improving exploration efficiency and stability, or if the discovered prompts fail to transfer task-wise, the central claim fails.","supporting_citations":[],"review_version":1}