{"id":"ed14776c-69c1-4d42-a04e-2e1f7dba7e98","arxiv_id":"2607.10522","paper_version":1,"verdict":"CONDITIONAL","confidence":"MODERATE","novelty_score":6.0,"correctness_risk":"medium","formal_verification":"none","parameter_count":4,"one_line_summary":"AMID, a verification-guided multi-agent MLE system for medical imaging, outperforms general MLE agents on 20 ReX-MLE challenges and approaches human challenge solutions on several tasks.","lead":"AMID is a multi-agent system that builds medical imaging models end-to-end from task data, using data-conditioned method planning and strict artifact verification. It beats general MLE agents on 20 challenge tasks and nears human challenge scores on several of them.","discovery_kind":"new_method","skeptic_critique":{"model":"grok-4.5","headline":"The 19/20 superiority claim is load-bearing on accepted-artifact validity, but full-suite baselines may fail mainly from protocol/artifact collapse rather than weaker medical search.","rationale":"The paper is a preliminary system report with clear empirical deltas and honest limitations. The strongest claim is system-level superiority under accepted artifacts, not clinical validation or a theorem. The reader correctly flags incomplete mechanism attribution: Table 2 helps on backend strength for three tasks, but full-suite gains still mix medical method planning with the more basic ability to produce grader-valid submissions where baselines often FAIL. That is the single most load-bearing soft spot for the “via DCMP + verification-guided optimization” causal story. It does not invalidate the benchmark numbers or the usefulness of an auditable medical MLE workflow; it keeps the verdict CONDITIONAL until accepted-only, same-backend ablations separate validity rescue from search quality. I agree with the reader’s weakest assumption and do not move the verdict.","tokens_in":22180,"tokens_out":626,"duration_ms":6427,"concrete_test":"On the full 20-task suite, re-run the strongest baseline (or a stripped AMID with DCMP/reviewer gates disabled) under the same Codex+GPT-5.5 backend, wall-clock, and GPU budget, scoring only attempts that pass the paper’s own reviewer checks (valid split, metric direction, complete prediction schema). If baseline accepted scores rise to near-AMID levels once invalid artifacts are repaired/filtered, the design-attribution claim weakens; if the 19/20 gap remains on accepted-only scores, the claim holds.","verdict_should_be":"CONDITIONAL","load_bearing_attack":"The central claim is that AMID’s DCMP + Verification-Guided Two-Stage Optimization produces superior accepted medical-imaging models on ReX-MLE (Table 1: valid on 20/20; better than strongest listed baseline on 19/20). The paper’s own framing (Sec. 1–2.4, Discussion) treats verification of splits, metrics, and submission artifacts as the control layer that makes scores trustworthy. Yet the full-suite comparison uses ReX-MLE-reported baseline scores under original challenge contracts, while many baseline entries are FAIL/near-zero on segmentation and detection (e.g., PUMA tissue seg, ISLES’22, SEG.A). That pattern is consistent with artifact/protocol collapse as much as with weaker method search. Table 2 only partially controls the model backend on three tasks and still does not ablate DCMP, two-stage promotion, or reviewer gates. Without those ablations, the headline deltas can be explained by “AMID is the first system that reliably ships valid medical submissions under a 24h/A6000 budget,” not by the claimed planning/verification design being the primary performance driver. Human references remain approximate (original test sets vs agent re-split public train data; Sec. 3.1–3.2).","agreement_with_reader":"agree"},"referee_report":{"model":"grok-4.5","summary":"This preliminary technical report introduces AMID, a multi-agent system for autonomous medical imaging model development. Given a task definition and dataset, AMID first runs Data-Conditioned Method Planning (DCMP) to convert task/data profiles and runnable medical resources into parallel method lanes, then applies Verification-Guided Two-Stage Optimization: broad, reviewer-gated exploration of lanes followed by selective exploitation of promoted candidates, with independent checks on validation protocols, metrics, and prediction/submission artifacts. On the 20-task ReX-MLE suite (segmentation, detection, classification, IQA/enhancement; 24h wall-clock and one A6000 GPU per task; Codex+GPT-5.5 main backend), AMID reports valid accepted artifacts on all tasks, improves over the strongest listed general-purpose MLE baseline (AIDE, ML-Master, R&D-Agent) on 19/20 tasks, and approaches or matches original-competition human references on several challenges. Case studies attribute strong runs to domain-matched pipelines (e.g., YOLO+anatomical priors on DENTEX; pathology foundation models on PUMA) and weaker runs to long-horizon enhancement and topology-dependent classification.","tokens_in":22590,"tokens_out":1495,"duration_ms":24864,"significance":"If the empirical superiority and auditability claims hold under stronger controls, this is a meaningful systems contribution: it is among the first end-to-end agentic MLE frameworks specialized to heterogeneous medical imaging, with an explicit artifact contract and independent reviewer gate rather than score-only search. The 20-task ReX-MLE evaluation with accepted-artifact criteria, the focused GPT-5.5-unified backend comparison on three tasks (Table 2), public solution release, and the framing of medical MLE as executable, verifiable search are concrete strengths. The work would matter for reducing bespoke challenge engineering and for making agent-built medical models inspectable. Significance is currently tempered by missing component ablations and by the possibility that large deltas partly reflect reliable valid-submission production under medical contracts rather than uniquely superior method search.","major_comments":[{"comment":"Table 1 / §3.2: The headline 19/20 superiority claim is load-bearing for the paper’s design narrative, but many baseline cells are FAIL or near-zero on segmentation/detection (e.g., PUMA tissue seg, ISLES’22, SEG.A). That pattern is consistent with protocol/artifact collapse as much as with weaker medical method search. Please quantify, for each baseline and task, whether failure was invalid artifacts/metric/split vs. a valid but weak model, and report a secondary comparison restricted to tasks where all systems produced accepted artifacts. Without this, the large deltas can be read as “AMID ships valid medical submissions” rather than as evidence that DCMP + two-stage optimization primarily improve modeling quality.","section":null},{"comment":"§2.3–2.4 and Discussion: DCMP and Verification-Guided Two-Stage Optimization are the claimed technical contributions, yet the manuscript provides no full-suite (or even multi-task) ablations that remove or disable (i) data-conditioned lane construction, (ii) the explore→promote→exploit schedule, or (iii) the independent reviewer gate. Table 2 only unifies the model backend on three tasks and still does not isolate these modules. For a systems paper whose central claim is that these mechanisms drive reliable high-performing medical MLE, at least a focused multi-task ablation (e.g., generic planning vs DCMP; single-stage score-driven search vs two-stage; no reviewer vs reviewer) is needed before the design—not just the full stack—can be credited.","section":null},{"comment":"§3.1–3.2 and Figure 3: Human-normalized comparisons use original-competition test-set references while agents train on re-split public training data with less data. The text acknowledges this, but the abstract, Figure 1, and Figure 3 still invite a “near human / human level” reading. Please demote human bars to an explicitly approximate scale in all main displays, avoid “matched” language, and report absolute accepted metrics as the primary claim; otherwise the “approached or matched strong human-designed solutions” statement overreaches the experimental design.","section":null},{"comment":"§2.4 Stage-1 promotion gate and free parameters: Lane coverage, plateau, and Top-K promotion criteria are described qualitatively but not specified with thresholds, budgets per stage, or sensitivity analysis. Given a fixed 24h/A6000 budget, promotion timing can dominate which families receive exploitation compute. Please state the operational promotion rules used for the main suite and show that headline rankings are stable under reasonable gate settings (or report variance across seeds/runs where feasible).","section":null}],"minor_comments":[{"comment":"Abstract/full-text opening: “Large anguage model” is a typo for “Large language model.”","section":null},{"comment":"Figure 1 / Table 1: Clarify whether “Human level” and Human columns are original leaderboard scores or ReX-MLE re-split scores in every caption; currently the distinction is only fully clear in §3.2 prose.","section":null},{"comment":"§2.5 Shared memory / skills: The skill mechanism is interesting but lightly evidenced; a short example of a skill created mid-run and reused would help readers separate design description from demonstrated behavior.","section":null},{"comment":"Related Work §5: Several 2025–2026 arXiv systems are cited appropriately; ensure citation completeness for ReX-MLE evaluation protocol details so readers can reproduce the accepted-artifact criterion without external ambiguity.","section":null},{"comment":"Note that this is an “ongoing preliminary technical report” is appropriate; please keep that framing consistent in the abstract and conclusion so readers do not treat deferred ablations as optional polish.","section":null},{"comment":"USenhance and TopCoW-Cls failure analyses (§3.3) are useful; consider a compact failure taxonomy table (budget, topology coupling, long-horizon training instability) for the full suite.","section":null}],"recommendation":"major_revision","confidential_remarks":"Fit is reasonable for a methods/systems venue in medical imaging or agentic ML, but the manuscript is still closer to a strong technical report than a finished journal article: the empirical stack result is impressive, yet causal credit to DCMP and verification is not yet isolated. I would not reject on novelty grounds—the medical artifact-contract framing is timely—but I would require the baseline validity breakdown and at least focused ablations before acceptance. If the journal prioritizes complete causal analysis over first-of-kind systems demos, major revision is the right bar; if it accepts systems papers with deferred ablations when limitations are explicit, minor revision after the validity breakdown might suffice—I lean major because the design is the claimed contribution."},"author_rebuttal":null,"desk_editor":{"model":"grok-4.5","letter":"The useful takeaway is simple: AMID is a working multi-agent MLE stack for medical imaging that actually produces accepted artifacts on all 20 ReX-MLE tasks and beats the listed general-purpose agents on 19 of them. That is not a small engineering result. Most prior MLE agents collapse on medical submission contracts; this one does not.\n\nWhat is new is the packaging, not the individual ingredients. Data-Conditioned Method Planning turns modality, geometry, labels, and runnable resources (nnU-Net, MONAI, MedSAM, pathology FMs, etc.) into parallel method lanes. Verification-Guided Two-Stage Optimization then explores those lanes, promotes only reviewer-checked attempts, and finalizes packages with split/metric/artifact gates. Shared file-system memory, lifecycle management, and heartbeats make the loop auditable. The case studies are concrete: DENTEX with YOLO plus dental-arch priors, PUMA with UNI/Phikon/CellViT/PathoSAM decompositions, nnU-Net-style SEG.A/ISLES. That is real domain conditioning, not generic AutoML.\n\nThe stress-test concern is partly right and partly overstated. Many baseline FAIL/near-zero scores look like protocol and artifact collapse, so some of the headline delta is “AMID ships valid medical submissions under 24h/A6000.” The paper is honest that full DCMP / two-stage / reviewer ablations are deferred, that human references use original test sets while agents train on re-split public data with less data, and that graph classification and USenhance remain weak. Table 2’s GPT-5.5-controlled three-task comparison still helps: even with a stronger unified backend, baselines stay well below AMID. So the system claim holds; the precise mechanism attribution is incomplete, not imaginary.\n\nMath is not the point; this is empirical systems work. Citations look standard for MLE agents, medical FMs, and challenge datasets. Code and solutions are promised on the project page, which matters.\n\nThis is for people building autonomous medical MLE, challenge agents, or auditable clinical-model pipelines—not for pure method theorists. It deserves a serious referee. I would engage, cite the system framing and ReX-MLE results, and push for the missing ablations in revision.","headline":"Solid systems paper: first real end-to-end agent that ships valid medical-imaging challenge packages on 20 tasks; design ablations still missing.","tokens_in":23214,"tokens_out":578,"would_cite":true,"duration_ms":6632,"reading_group":"yes","serious_thinker":"yes","would_accept_peer_review":true},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"grok-4.5","headline":"An autonomous multi-agent system can build high-performing, auditable medical imaging models across heterogeneous tasks by planning from the data and verifying every score and submission artifact.","keywords":["autonomous agents","medical imaging","machine learning engineering","multi-agent systems","model development","verification","method planning","challenge benchmarks"],"falsifier":"Rerun the same twenty-task suite with matched model backend and wall-clock budget, ablating data-conditioned method planning and the reviewer verification gates separately; if the full system no longer beats those ablations and the general-purpose agents on accepted primary metrics, the central design claim fails.","tokens_in":23089,"feed_emoji":"🏥","tokens_out":921,"duration_ms":23042,"temperature":0.7,"pith_summary":"Medical imaging model development is not a generic machine-learning recipe hunt: modality, anatomy, patient splits, metrics, and submission formats all decide what works, and a high score is useless if the validation protocol or prediction files are wrong. This paper introduces AMID, a multi-agent framework that first turns the task data and runnable domain resources into parallel method lanes, then explores those lanes broadly before exploiting the best candidates while a reviewer rejects untrusted evidence. Across twenty medical imaging challenge tasks spanning segmentation, detection, classification, and enhancement, the system produced valid accepted artifacts for every task and beat the evaluated general-purpose machine-learning engineering systems on nearly all of them, approaching or matching strong human challenge solutions on several. The claim is that domain-conditioned planning plus continuous artifact verification can convert bespoke medical model engineering into a repeatable agentic workflow that leaves inspectable code, scores, and submissions.","feed_headline":"Agents beat generic MLE systems on 19 of 20 imaging challenges","feed_subtitle":"Data-conditioned planning and strict artifact checks turn medical model building into an auditable agent workflow.","key_machinery":"Data-Conditioned Method Planning (DCMP) plus Verification-Guided Two-Stage Optimization. DCMP profiles the public data and grounds candidates in runnable medical-imaging resources to form parallel method lanes; the optimizer then explores those lanes broadly, exploits promoted candidates, and admits only protocol-valid, metric-correct, submission-ready evidence through an independent reviewer.","core_discovery":"AMID shows that autonomous medical imaging model development works when the search space is made executable from the actual data and when only reviewer-checked validation protocols, metrics, and prediction artifacts may drive promotion and final selection. On a twenty-task challenge suite spanning diverse modalities and prediction types, this design produced valid end-to-end artifacts for every task, improved over the strongest listed general-purpose autonomous MLE baselines on nineteen of twenty tasks, and on several tasks approached or matched strong human-designed challenge solutions.","pith_inferences":["Similar data-conditioned lanes and artifact gates may transfer to other regulated domains where format-correct submissions matter as much as scores.","If full ablations of planning and verification remain weak, headline gains may largely reflect stronger coding runtimes and longer budgets rather than the search architecture.","Tasks that couple small-structure topology with downstream classification may need joint multi-stage objectives beyond single-lane exploitation under fixed budgets.","Reusable skill memory for foundation-model adaptation could shorten later runs as successful procedures accumulate across workers."],"forward_implications":["Medical model building can be treated as searchable, auditable engineering rather than one-shot code generation.","General-purpose MLE agents without domain-conditioned lanes and artifact gates will underperform on heterogeneous clinical imaging tasks.","The same verified search policy can deliver submission files, trained model packages, or Dockerized predictors.","Challenge-style agent evaluation becomes a system-level test of pipeline completeness, not only local validation scores.","Cost-aware long-horizon autonomy becomes the next practical bottleneck once planning and verification are in place."],"fun_headline_variants":["AMID beats generic MLE systems on 19 of 20 medical imaging tasks","Data-conditioned agents deliver valid models across 20 imaging challenges","Verification-guided agents approach human solutions on imaging tasks","Autonomous workflow tops general MLE baselines on 19 imaging challenges","AMID turns medical model building into auditable end-to-end agent runs"],"cache_read_input_tokens":128,"weakest_assumption_plain":"The gains are assumed to come mainly from the planning and verification design rather than coding-backend strength, compute budget, or baselines failing medical artifact checks—and full ablations of those pieces are still incomplete.","fun_headline_variants_meta":{"raw":{"variants":["AMID beats generic MLE systems on 19 of 20 medical imaging tasks","Data-conditioned agents deliver valid models across 20 imaging challenges","Verification-guided agents approach human solutions on imaging tasks","Autonomous workflow tops general MLE baselines on 19 imaging challenges","AMID turns medical model building into auditable end-to-end agent runs"]},"model":"grok-4.5","effort":"low","cost_usd":0.004688,"raw_usage":{"total_tokens":1370,"prompt_tokens":787,"num_sources_used":0,"completion_tokens":76,"cost_in_usd_ticks":46880000,"prompt_tokens_details":{"text_tokens":787,"audio_tokens":0,"image_tokens":0,"cached_tokens":256},"completion_tokens_details":{"audio_tokens":0,"reasoning_tokens":507,"accepted_prediction_tokens":0,"rejected_prediction_tokens":0}},"tokens_in":787,"tokens_out":76,"duration_ms":4991,"temperature":1.0,"reasoning_tokens":507,"cache_read_input_tokens":256,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-07-14T11:04:34.996942+00:00","model_set":{"reader":"grok-4.5"},"falsifier":"Rerun the same twenty-task suite with matched model backend and wall-clock budget, ablating data-conditioned method planning and the reviewer verification gates separately; if the full system no longer beats those ablations and the general-purpose agents on accepted primary metrics, the central design claim fails.","supporting_citations":[],"review_version":1}