{"id":"ecc511fe-29bb-40cb-8d88-38e4d7e269d7","arxiv_id":"2607.22813","paper_version":1,"verdict":"CONDITIONAL","confidence":"MODERATE","novelty_score":6.0,"correctness_risk":"medium","formal_verification":"none","parameter_count":2,"one_line_summary":"An agentic AI system with a physicist in the loop re-casts an ATLAS ttZ measurement into a global top-quark SMEFT fit and recovers injected coloron Wilson coefficients in a repeatable benchmark.","lead":"This paper builds agentic AI software that helps physicists re-analyze LHC measurements and fold them into a global Standard Model effective field theory (SMEFT) fit. The authors show a human-in-the-loop agent system adding a new ATLAS top-pair-plus-Z measurement to the existing SFitter fit framework.","discovery_kind":"new_method","skeptic_critique":{"model":"deepseek-v4-flash","headline":"SMEFTatNLO scan in Sec. 4.3 may reproduce the App. B silent-failure mode; paper never shows the scan avoided wrong truncation and top-width handling.","rationale":"The reader's weakest assumption—that the SMEFTatNLO scans in Sec. 4.3 are free of the silent failures documented in App. B—is exactly the most load-bearing concern. The paper's central claim is that SFitterAgents can select, simulate, and add a new ttZ measurement to a global SMEFT analysis with a physicist in the loop. That claim stands or falls on the correctness of the κ parameterization derived from the SMEFT scan. The paper itself shows that the specific silent failure mode (wrong perturbative truncation and top width) is not reliably handled by any agent configuration, yet no evidence is provided that the Sec. 4.3 scan was validated against these issues. This is not a disagreement with consensus; it is an internal inconsistency: the paper's own validation appendix undermines the unshown assumption in the main demonstration. The concrete test—re-running the scan with corrected settings and comparing the resulting constraints—would settle the concern. If the constraints are robust, the conditional acceptance stands; if not, the paper's headline conclusion is unsupported. I agree with the reader's CONDITIONAL verdict and recommend no change to that assessment.","tokens_in":28114,"tokens_out":3435,"duration_ms":34427,"concrete_test":"Re-run the Sec. 4.3 SMEFT scan using the corrected SMEFTatNLO setup: restore the SM amplitude by disabling the spurious dimension-six contact interaction in the truncation, and recompute the top width for each scanned Wilson coefficient. Then re-extract the per-bin κ coefficients and refit the global SFitter likelihood. Compare the profiled constraints and the χ² pulls on Cφt, CtZ, CφQ with the published Figs. 4–5. If any constraint shifts by more than the Monte Carlo uncertainty (or if the κ for the SM bin changes significantly), the silent-failure concern is confirmed and the headline re-casting claim must be revised. Alternatively, run App. B question 5 on the exact SFitterAgents pipeline used for Sec. 4.3; if it fails as in Table 7, the paper must demonstrate how the physicist-in-the-loop caught the error.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The central demonstration (Sec. 4.3) builds the κ-parameterization of the new ttZ measurement from SMEFTatNLO simulations of pT(Z) and m(ttZ) with 21 Wilson coefficients. These κ enter the global SFitter likelihood and drive the claimed improvements in Figs. 4–5. Appendix B, silent-failure test 5, describes exactly this class of SMEFTatNLO setup: MadGraph's default tree-level perturbative truncation can omit the SM amplitude and the requested dipole operator, and the top width must be recomputed because the dipole modifies the decay rate. The paper's own Table 7 shows that none of the tested agent configurations answers this question correctly in more than 0–2 out of 10 runs (warm: 0/10). The text explicitly states: 'The SMEFT question is the sole exception... none of the configurations answers it reliably.' Yet Sec. 4.3 reports no check, no reviewer output, and no physicist-in-the-loop validation that the actual scan avoided these failure modes. If the scan ran with default truncation, the SM ttZ background template itself could be generated through a dimension-six contact interaction instead of the SM, and the top width could be inconsistent—corrupting both the κ shapes and the resulting global constraints. Since the paper's core claim is that an agentic system can reliably update a global SFitter analysis, the correctness of this SMEFT scan is load-bearing and unverified.","agreement_with_reader":"agree"},"referee_report":{"model":"deepseek-v4-flash","summary":"The paper presents MadAgents.v3, a consultant-based agentic layer for MadGraph, and SFitterAgents, an agentic interface to the SFitter global-fitting framework. The central demonstration is a physicist-in-the-loop re-casting exercise: the agents select the ATLAS ttZ measurement (arXiv:2312.04450), re-simulate the SM signal at parton and particle level, run SMEFTatNLO simulations with 21 Wilson coefficients, build κ parameterizations for pT(Z) and m(ttZ), and add these to the global top-sector SMEFT analysis, reproducing previous constraints and tightening them. Validation consists of five 'silent failure' tests (App. B) and a repeatable coloron-injection benchmark with six pseudo-datasets (App. C).","tokens_in":28458,"tokens_out":7824,"duration_ms":75174,"significance":"The paper has real strengths: App. C is a partly independent validation (coloron UV model, Eq. 17), the matching is checked against full coloron samples in Fig. 8, and the reproducible documentation structure plus Table 8 are concrete assets. App. B is unusually candid about hard failures. However, the main re-casting claim is not yet fully supported: the SMEFTatNLO setup used in Sec. 4.3 is exactly the class for which Table 7 shows all agent configurations fail most of the time (SMEFT setup: 0-2/10, warm 0/10), and the paper does not show that the actual scan avoided the truncation and top-width failure modes. Because the κ parametrization feeds the global likelihood and drives Figs. 4-5, this is a load-bearing gap. The central claim is defensible and the gap appears fixable, but requires additional evidence.","major_comments":[{"comment":"Table 7 reports that the SMEFT setup question — correct perturbative truncation of SMEFTatNLO and recomputation of the top width for a dipole-modified decay — is answered correctly 0/10 times by the warm configuration and at most 2/10 by any configuration; the text states 'none of the configurations answers it reliably.' Section 4.3 then builds the full κ parametrization of the new ttZ measurement from SMEFTatNLO runs with 21 Wilson coefficients, and these κ shapes feed the global SFitter likelihood behind Figs. 4–5 and the paper's central claim. The paper does not show that the actual Sec. 4.3 campaign avoided the two failure modes (default tree-level truncation dropping the SM amplitude and dipole operator; inconsistent top width). A reviewer output, a Feynman-diagram check, an independent cross-check of one κ bin, or a statement from the human supervisor is required to establish that","section":"Sec. 4.3 and App. B, Table 7"},{"comment":"App. B's validation is self-referential in two ways that matter for the headline claim that MadAgents.v3 prevents silent failures. The grading is performed by an LLM agent (Claude Opus 4.8), and the warm configuration is trained on the same kind of silent-failure lessons on which it is tested. Table 7 gives no information on the grader's false-positive/negative rate, and for the SMEFT row all three configurations score 0–2/10. The paper should (i) report a human re-scoring of at least the SMEFT-question runs and (ii) soften the Sec. 5 statement that the workflow can be expanded 'with no risk concerning the quality of the results', which Table 7 does not support.","section":"App. B (validation procedure) and Sec. 5"}],"minor_comments":[{"comment":"The selected option 'NLO parton+reuse κ for particle plots' reuses parton-level κ shapes for particle-level plots. Since Sec. 4.4 uses parton-level data for the global fit, this does not affect the main result, but Fig. 3 should state this explicitly.","section":"Sec. 4.3, p. 13"},{"comment":"The correlation matrix sets ρ_ij=0.99 for all systematics pairs. This is a regularized full-correlation approximation, not exact full correlation; a sentence explaining the choice and any sensitivity test would help.","section":"Sec. 2.2, Eq. (3)"},{"comment":"The injected truth is defined by tree-level matching; the text already says higher-order matching would be more precise. Please add a sentence in the benchmark summary marking that the recovery test validates the tree-level matching value, not a full higher-order SMEFT prediction.","section":"App. C, Eq. (17)"},{"comment":"Typo: 'out global analysis' should be 'our global analysis'.","section":"Sec. 4.3, user prompt"}],"recommendation":"major_revision","confidential_remarks":"The Sec. 4.3 vs. App. B mismatch is the key issue. If the authors can show that the actual SMEFT run was validated against the truncation and top-width failure modes, I would be willing to accept after further minor revisions. The paper's candid reporting of silent failures is a positive signal and should be preserved."},"author_rebuttal":null,"desk_editor":{"model":"deepseek-v4-flash","letter":"The real news here is the tooling. MadAgents.v3 with its consultant architecture and source-grounding rule is a step up from the earlier versions, and the SFitterAgents wrapper makes a genuine months-long SFitter task into a supervised workflow. The coloron injection benchmark is the most valuable part: it gives an external truth value, ships the simulation setup, and the repeated runs mostly land around the injected coefficients. The paper also deserves credit for printing its failures in Table 7 instead of hiding them. That is real evidence, and it should count.\n\nNow the soft spots, in proportion. The one that matters is the SMEFT setup test in Appendix B. Table 7 shows no configuration answers the SMEFTatNLO question more than 2 out of 10 times; the warm setup gets 0/10. The paper says so itself: \"none of the configurations answers it reliably.\" Yet the central demonstration in Sec. 4.3 builds the kappa parameterization of the ttZ measurement from SMEFTatNLO scans of 21 Wilson coefficients, and nowhere in that section is there a check that the scan avoided the two traps the Appendix identifies: MadGraph's tree-level truncation dropping the SM amplitude and the dipole, and the need to recompute the top width. If the scan ran with default settings, the kappa shapes could be wrong, and the claimed global constraints in Figures 4–5 would not be trustworthy. The reader's stress-test note lands here and it is load-bearing, not a nitpick.\n\nNext, the coloron benchmark's grading is done by Claude Opus 4.8, and the \"warm\" configuration is trained on the same class of silent-failure lessons it is later tested on. That limits the benchmark's claim to generalizable reliability, though it does not invalidate what is shown. Run 2's outlier is explained away with narrative judgment rather than a quantitative criterion. And the claim that the new global analysis \"reproduces previous results\" would be stronger with an explicit side-by-side against the earlier SFitter fits from Refs. [9,13]; the paper gestures at this but does not show the figure.\n\nNone of this is a fatal problem. The architecture is sound, the benchmark is thoughtful, and the failure reporting is unusually honest. The missing piece is verification that the actual production scan in Sec. 4.3 is clean. That takes a commit hash, an explicit comparison to the previous fit, and a rerun of the SMEFT scan with the silent-failure fixes applied.\n\nThis paper deserves a serious referee. The weaknesses are concrete and addressable, and the tool-building contribution is real. I would bring it to a reading group and would cite it, but I would not yet use its SFitter constraints as a baseline for physics conclusions.","headline":"A genuinely useful agentic re-casting toolkit with an honest validation appendix, but the central SMEFT scan may be running in the exact silent-failure mode the paper itself documents.","tokens_in":28960,"tokens_out":695,"would_cite":true,"duration_ms":10003,"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":"An agentic system with a physicist in the loop can add a new LHC measurement to a global SMEFT fit and tighten constraints on top-quark couplings.","keywords":["agentic AI","analysis re-casting","SMEFT","SFitter","top quark sector","ttZ production","repeatable benchmark","silent failure modes"],"falsifier":"Re-run the SMEFT scan for the pT(Z) and m(ttZ) observables using the two settings that pass the Appendix B silent-failure test (adjusted truncation and recomputed top width), extract the kappa parameters, and redo the global fit; if the profiled constraints in Figure 5 change materially—in particular the 34–42% tightening or the large negative Cφt shift—the agentic re-casting result as presented is not reproducible.","tokens_in":27959,"feed_emoji":"🤖","tokens_out":8042,"duration_ms":75554,"temperature":0.7,"pith_summary":"The paper shows that the months-long expert workflow of LHC analysis re-casting—extracting a published measurement, re-simulating it under a new theory hypothesis, and refitting a global likelihood—can be carried out by an LLM agent system supervised by a physicist. It demonstrates this with SFitter's global top-sector SMEFT fit: the agents select and extract an ATLAS ttZ differential measurement, re-simulate it at NLO, scan 21 dimension-six operators, assemble the datacard, and run the global analysis, which tightens the bounds on the top-electroweak Wilson coefficients by 34–42%. The paper also reports a blind benchmark in which six independent agent runs recover the Wilson coefficients of an injected coloron signal, consistent with the truth within statistical fluctuations. If this holds, published LHC measurements could be folded into global fits quickly, reproducibly, and with a physicist retaining control at every step.","feed_headline":"Agents add ttZ data to global fit, tighten top-sector limits","feed_subtitle":"LLM agents with a human in the loop re-cast an ATLAS measurement and pass a blind new-physics recovery test.","key_machinery":"SFitterAgents, an agentic system built on MadAgents.v3, whose orchestrator routes each query to specialized consultant, worker, and reviewer subagents. Its operating principles—source grounding in the locally installed code, lasting memory records, completion-vs-correctness checks, recorded confidence, and adversarial review—are the mechanism that keeps silent simulation failures from corrupting the physics. The re-casting chain itself is carried by four steps: measurement extraction, SM re-simulation, SMEFT scan with per-bin kappa extraction (the linear and quadratic dependence of each bin on the Wilson coefficients), and SFitter likelihood construction with the physicist validating each st","core_discovery":"On its own terms, the paper establishes that SFitterAgents—built on the MadAgents.v3 consultant architecture—can perform the complete re-casting chain for a new measurement: it picks the ttZ measurement, extracts per-bin values and uncertainties, re-simulates the SM signal at parton and particle level, scans the SMEFT Wilson coefficients to extract the per-bin kappa parametrization, validates and assembles the SFitter datacard, and runs the exclusive-likelihood global fit. Adding the normalized pT(Z) spectrum tightens the profiled constraints on Cφt, C−φQ, CtZ and CtW by 34–42%, while m(ttZ) alone gives 19% on CtZ; the agent flags a 3σ underfluctuation in one pT(Z) bin that pulls Cφt to larg","pith_inferences":["If the workflow scales as claimed, the same pipeline could maintain a continuously updated global SMEFT fit throughout the HL-LHC run, folding in every new differential measurement with a public likelihood as it appears.","A natural next step is to instrument the SMEFT scan so that the silent-failure checks from Appendix B (dimension-six truncation handling and top-width recomputation) run automatically before the kappa parameters are extracted; the paper's own validation shows that setup is exactly the case its agents answer correctly only 0–2 times out of 10.","The benchmark's observed breakdown of the dimension-six description near the coloron pole suggests an extension: at high invariant mass the workflow could match to UV-complete models directly instead of SMEFT, using the same agentic re-simulation chain.","Agent-driven re-casting could also serve as an automated new-physics scanner: bins that pull Wilson coefficients far from the SM, like the flagged 3σ pT(Z) bin, are surfaced to the physicist as candidate signals rather than being averaged away."],"forward_implications":["Adding the normalized pT(Z) ttZ spectrum to the top-sector global fit tightens the profiled constraints on the top-electroweak operators by 34–42%, and no existing bound is loosened.","The public likelihood with 276 nuisance parameters can be folded into the fit for the statistics-dominated ttZ measurement with results nearly identical to simpler per-bin uncertainty treatments; the same machinery will matter once systematics-dominated analyses are re-cast.","Six independent agent runs on blind coloron datasets reproduce the injected Wilson coefficients, with marginal likelihoods centered on the truth, establishing a repeatable benchmark for agentic re-casting.","The documented workflow structure allows an agent to reproduce a previous global analysis exactly, making agent-run fits auditable.","The four-step re-casting workflow and the agentic interface generalize beyond SFitter and beyond the top sector, applying to any simulation tool and any global analysis framework."],"fun_headline_variants":["Agent-cast ttZ data tightens top-sector constraints","AI agents re-simulate LHC data to sharpen SMEFT limits","Human-in-loop agents tighten top quark limits by up to 42%","Agents pass blind test tightening top-sector limits with ttZ","Agentic re-simulation narrows top-sector constraints in global fit"],"cache_read_input_tokens":2304,"weakest_assumption_plain":"The re-casting demonstration assumes the SMEFTatNLO simulations for the new ttZ measurement correctly handle MadGraph's perturbative truncation of dimension-six operators and recompute the top width; the paper's own Appendix B silent-failure test shows this setup is answered correctly in only 0–2 of 10 runs, and no check confirms the actual scan avoided that failure mode.","fun_headline_variants_meta":{"raw":{"variants":["Agent-cast ttZ data tightens top-sector constraints","AI agents re-simulate LHC data to sharpen SMEFT limits","Human-in-loop agents tighten top quark limits by up to 42%","Agents pass blind test tightening top-sector limits with ttZ","Agentic re-simulation narrows top-sector constraints in global fit"]},"model":"deepseek-v4-flash","effort":"low","cost_usd":0.000875,"raw_usage":{"total_tokens":3571,"prompt_tokens":638,"completion_tokens":2933,"prompt_tokens_details":{"cached_tokens":256},"prompt_cache_hit_tokens":256,"prompt_cache_miss_tokens":382,"completion_tokens_details":{"reasoning_tokens":2853}},"tokens_in":382,"tokens_out":2933,"duration_ms":20548,"temperature":1.0,"reasoning_tokens":2853,"cache_read_input_tokens":256,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-08-01T04:23:57.113034+00:00","model_set":{"reader":"deepseek-v4-flash"},"falsifier":"Re-run the SMEFT scan for the pT(Z) and m(ttZ) observables using the two settings that pass the Appendix B silent-failure test (adjusted truncation and recomputed top width), extract the kappa parameters, and redo the global fit; if the profiled constraints in Figure 5 change materially—in particular the 34–42% tightening or the large negative Cφt shift—the agentic re-casting result as presented is not reproducible.","supporting_citations":[],"review_version":1}