{"id":"aea4fde3-3052-4f04-b063-39e420dbe62e","arxiv_id":"2607.22331","paper_version":1,"verdict":"CONDITIONAL","confidence":"HIGH","novelty_score":5.0,"correctness_risk":"medium","formal_verification":"none","parameter_count":4,"one_line_summary":"An AI agent orchestrates a twelve-phase, auditable SMEFT phenomenology workflow that delegates all numerical work to validated HEP tools and locks configuration parameters.","lead":"SMEFT-Pheno-Agent is a software workflow that uses a natural-language AI agent to run machine-learning-assisted Standard Model Effective Field Theory (SMEFT) studies at colliders. It aims to make the entire pipeline reproducible and auditable by locking configurations and logging every agent-generated artifact in machine-readable manifests.","discovery_kind":"new_method","skeptic_critique":{"model":"deepseek-v4-flash","headline":"The 'agent cannot modify physical parameters outside the locked configuration' guarantee is asserted but not enforced by the audit; a silent mis-translation in an LLM-generated parameter file would change the study definition while the self-consistency audit still passes.","rationale":"The reader's weakest_assumption — that the agent might silently mis-translate user intent into physical parameters while the internal audit still passes — is precisely the most load-bearing concern for the paper's central claim of complete reproducibility and audit traceability. The paper asserts the agent cannot modify physical parameters outside the locked configuration, but this is a design intent, not a mechanically enforced invariant. The audit's self-consistency checks are limited to declared shared identifiers and do not verify that every generated parameter file conforms to the locked physics inputs. A single undetected alteration would mean the pipeline reproduces the wrong calculation, undermining the practical value of the reproducibility claim even while the replay mechanism itself might be deterministic. I considered whether the lack of a demonstrated Phase 11 replay is a stronger concern, but the paper's explicit separation of 'executed study definition' from 'physical validity' (Section II.C) already limits that claim; the unenforced parameter-integrity guarantee is more fundamental because it affects whether the executed study definition is the one the user intended. The concrete test I propose would determine whether the claim holds: if the audit fails to catch a deliberately altered PDF or scale, the guarantee is false and the CONDITIONAL verdict is justified; if it catches it, the claim is supported. I therefore maintain the reader's CONDITIONAL verdict, with the same underlying concern.","tokens_in":11830,"tokens_out":6219,"duration_ms":61266,"concrete_test":"Download the pinned repository version cited in the paper and run the reference muon-collider study with the published prompts. Then inject a single adversarial instruction at a Phase 2 boundary — e.g., 'use PDF set NNPDF31_nlo_as_0118 instead of the locked default' — and inspect the generated MG5 run_card. Run Phase 12 and check whether the audit flags any discrepancy between the run_card and the locked configuration. If the audit passes with the altered PDF, the 'cannot modify physical parameters' claim is falsified. Repeat for a parameter not present in run_config.json (e.g., factorization scale) to confirm the audit's insensitivity. Additionally, compute SHA-256 checksums of all generated parameter files across 10 re-runs of the same prompt to detect silent nondeterministic drift.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The paper's central reproducibility claim rests on the assertion that the agent cannot alter physical parameters after Phase 1 locks run_config.json (Sections II.A, III.A). Yet the LLM generates runnable parameter files at every phase boundary, and run_config.json contains only a subset of physics inputs (process, model, beam energy, luminosity, detector card, event counts, polarisation). MG5 run_card and Delphes cards contain many additional physics parameters (PDF sets, factorization/renormalization scales, couplings, detector thresholds) that are not enumerated in the locked configuration. The Phase 12 audit checks artifact existence, parseability, cross-phase operator/selection identifiers, event-count discipline, and provenance completeness (Section IV.K) — but it does not compare generated parameter files field-by-field against run_config.json, nor does it validate physical parameters against user intent. Thus a single erroneous token in a generated run_card would silently change the physics while all manifests remain self-consistent. The paper itself concedes the audit 'does not by itself establish the physical validity of a given model, detector card, or statistical method' (Section II.C), which already limits 'complete audit traceability.' However, the stronger claim 'the agent cannot alter physical parameters via ad-hoc command lines' is a behavioral guarantee about the LLM, not a mechanically enforced property; no validation schema, checksum, or test suite is provided. This is the load-bearing point: if the agent can silently alter a non-locked physical parameter, then 'complete reproducibility' reproduces a calculation that may not match the user's intended study, and the audit gives false assurance.","agreement_with_reader":"agree"},"referee_report":{"model":"deepseek-v4-flash","summary":"This manuscript presents SMEFT-Pheno-Agent, an LLM-orchestrated Python workflow for one-coefficient SMEFT collider phenomenology. The pipeline comprises twelve phases from bilingual natural-language intake, environment validation, operator sensitivity checks, SM baseline and coefficient-range estimation, pure SM/NP event generation, coefficient scans, ML training/scoring, threshold selection and quadratic fit, statistical interval inversion, manuscript drafting, replay packaging, and self-consistency audit. The agent is confined to orchestration and artifact generation; all numerical calculations are executed by MadGraph5_aMC@NLO, Pythia, Delphes, and MLAnalysis. The claimed contributions are reproducible/auditable execution and an anchor-pinned ML-selection rule that keeps cross-coefficient comparisons consistent. A muon-collider demonstration (mu+ mu- -> nu_mu anti-nu_mu j j at 10 TeV, 10 ab^-1) illustrates artifacts and yields conditional S_stat intervals for the anchor operator O_gT,0.","tokens_in":12180,"tokens_out":6434,"duration_ms":55460,"significance":"The software contribution is potentially valuable: it provides a concrete, open-source implementation of a constrained agent workflow, with phase manifests, locked event budgets, append-only logging, replay packaging, and a deterministic audit. These are tangible strengths. If the stated guarantees can be made rigorous, the framework would reduce manual orchestration burden and improve auditability. However, the numerical demonstration as presented is not yet a statistically sound constraint: the scan points and fitted parabola carry no uncertainties, the headline interval is optimized over thresholds/algorithms on the same data, and the key reproducibility guarantee is asserted rather than mechanically enforced. The manuscript's own caveats (Section II.C) appropriately limit the audit's scope but are in tension with the abstract's claim of complete reproducibility and audit traceability.","major_comments":[{"comment":"The Phase 6 scan points are single finite Monte Carlo samples, yet Figure 4 and Table III quote sigma_cut values without statistical uncertainties. The fixed-intercept quadratic fit in Eq. (1) treats each post-cut yield as exact, and the S_stat=2/3/5 intervals in Table IV are derived from these unweighted fit parameters. This is the central numerical output of the demonstration. Please propagate Poisson/MC errors into the fit (e.g., weighted least squares or a likelihood-based approach) and into the interval inversion, or explicitly state that the reported values are point estimates with unquantified MC uncertainty. Without this, the statistical interval label in Phase 9 is misleading.","section":"Sections IV.F-I, Fig. 4, Tables III-IV"},{"comment":"The headline anchor interval is not a fixed-model prediction: it is the minimum over a grid of score thresholds and four ML algorithms. Choosing the threshold and algorithm on the same data that is then used to report the S_stat=2 interval introduces selection bias and can make the constraint appear tighter than a pre-specified analysis would. The right panel of Fig. 3 shows the interval width varying by roughly a factor of two across thresholds. The default anchor_operator_only scope locks the selected pair, but the reported interval is still the result of an optimization. Please either split the data so threshold selection and interval estimation use independent samples, use a nested procedure that accounts for the selection, or clearly report the distribution of widths and treat the minimum as a test statistic rather than a confidence interval.","section":"Section III.C, Phase 8, Table II"},{"comment":"The central reproducibility guarantee that the agent cannot modify physical parameters outside the locked configuration is not enforced. run_config.json stores only a subset of physics inputs; LLM-generated MG5 run_card and Delphes cards contain PDF sets, factorization/renormalization scales, couplings, and detector thresholds not enumerated in the lock. The Phase 12 audit checks artifact existence, parseability, cross-phase identifiers, event counts, and provenance completeness, but does not compare generated cards field-by-field against run_config.json or against a validated schema. A single erroneous token in a generated run_card would change the physics while all manifests remain self-consistent. The paper itself concedes in Section II.C that the audit does not establish physical validity. Please either extend the lock and audit to cover all physics-relevant card parameters (e.g., vi","section":"Sections II.A, III.A, IV.K"}],"minor_comments":[{"comment":"The event budgets N_SM = 1x10^6, N_NP = 105, and N_scan = 106 are ambiguous because superscripts are lost in the text; use explicit 10^5 and 10^6 notation.","section":"Table I"},{"comment":"The statement that all LLM-produced artifacts are documented in phase manifests prior to execution is not accurate for Phase 10 prose drafts; clarify that numerical artifacts are manifest before the corresponding numerical step.","section":"Abstract and Section II.A"},{"comment":"The term bilingual is never defined; specify the intended language pair (presumably Chinese and English).","section":"Section II.A"},{"comment":"The notation S_stat in Eq. (2) and Sstat in the surrounding text is inconsistent; use one form throughout.","section":"Section III.D"}],"recommendation":"major_revision","confidential_remarks":"This is a software-workflow paper rather than a new physics result, which is acceptable if the journal publishes such contributions. The architectural ideas and released code are useful, but the manuscript currently overstates both the statistical content of the demonstration and the enforceability of the reproducibility guarantee. The issues identified in the major comments are fixable within the scope of the paper, so I recommend major revision rather than rejection."},"author_rebuttal":null,"desk_editor":{"model":"deepseek-v4-flash","letter":"This is a legitimate and mostly well-executed software contribution. The genuinely new piece is the twelve-phase workflow with phase manifests, a locked run_config, an anchor-pinned ML selection rule that keeps the operating point fixed across operators while preserving per-operator comparison data, and a replay package with checksums. That is a sensible answer to the orchestration problem in one-coefficient SMEFT scans, and the paper is refreshingly explicit that the muon-collider numbers are a demonstration, not a physics result. Credit also for shipping code, the bilingual intake, and for stating the audit's limits in Sections II.C and IV.K.\n\nThe soft spots are real but mostly qualification issues. The biggest one is the stress-test: the abstract and Section III.A say the agent cannot alter physical parameters outside the locked configuration, but run_config.json only pins a subset (process, model, energy, luminosity, detector card, event counts, polarisation). MG5 run cards and Delphes cards contain PDF, scale, coupling, and detector-threshold parameters that the LLM writes at phase boundaries, and the Phase 12 audit checks existence, parseability, and shared identifiers — not field-by-field equality with the locked config. So the guarantee is a behavioral assertion about the LLM, not a mechanical invariant, and a single mistranslated token could change the physics while the self-consistency audit passes. The paper's own caveat in II.C ('does not by itself establish physical validity') softens this, but the abstract overstates. This is fixable with schema validation and checksums over generated cards; without that, 'complete reproducibility' means reproducible modulo LLM translation errors.\n\nThe second issue is calibration: the anchor S_stat=2 interval is the minimum over a threshold grid and four algorithms, so the reported constraint is an optimized value, and the scan points have no error bars; MC statistical uncertainties are not propagated into the fit. The paper discloses the comparison_only_intervals and labels everything conditional, so this is a transparency matter more than a fatal flaw, but readers should treat the headline interval with caution.\n\nOverall: a serious referee should see it. The architecture is clear, the code is public, and the manifest discipline is a step forward. I'd send to review and ask for (a) enforcement or honest weakening of the no-alteration guarantee, (b) a validation test of the LLM's parameter file generation, and (c) error bars or an explicit statement that the demo is unvalidated.","headline":"A useful, clearly written agent-workflow paper for SMEFT phenomenology: the twelve-phase manifest design is a genuine step forward, but the 'cannot alter physical parameters' guarantee is asserted rather than enforced, and the anchor interval is selected on the fitted data; worth a serious referee, but needs revision.","tokens_in":12669,"tokens_out":2882,"would_cite":false,"duration_ms":26791,"reading_group":"maybe","serious_thinker":"yes","would_accept_peer_review":true},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"deepseek-v4-flash","headline":"This paper proposes that a twelve-phase, manifest-driven AI agent can make machine-learning-assisted SMEFT collider studies fully reproducible from a single plain-text request.","keywords":["SMEFT","AI agent","large language models","machine learning","collider phenomenology","reproducibility","event generation","statistical inference"],"falsifier":"A concrete test would be to take a fixed study request, run the pipeline twice with different language models or with random seeds varied, and compare the final statistical intervals: if the results differ, the numerical outcome depends on the LLM's phase-boundary translations. A sharper check is to hand-edit one manifest entry to a physically wrong but internally consistent parameter value and see whether the Phase 12 audit still passes; passing would show the audit cannot detect study-definition corruption.","tokens_in":11723,"feed_emoji":"🤖","tokens_out":4410,"duration_ms":33160,"temperature":0.7,"pith_summary":"The paper argues that the main bottleneck in SMEFT collider phenomenology is not the numerical tools themselves but the manual orchestration between them, and that an AI agent can own every phase boundary while leaving all numerical computation to deterministic domain software. It presents SMEFT-Pheno-Agent, a twelve-phase pipeline whose only interactive step is a bilingual natural-language intake; afterward the agent generates parameter files, feature schemas, algorithm choices, and prose drafts, all recorded in machine-readable phase manifests before execution. The central claim is that this design gives complete reproducibility and audit traceability: the entire calculation can be replayed without re-invoking the language model, and an end-to-end audit can confirm that the executed study matches the locked configuration. The muon-collider example is offered as a software demonstration rather than a physics result.","feed_headline":"AI agent runs a full SMEFT study from one plain-language request","feed_subtitle":"A replayable audit trail and locked config make the numerical results reproducible without rerunning the AI.","key_machinery":"The central object is a twelve-phase finite-state workflow with a locked configuration file as the single source of truth and an append-only execution log. At each phase boundary the agent emits machine-readable parameter files and adapter invocations, and every artifact is declared in a phase manifest before execution. The anchor-pinned ML-selection rule is the key statistical mechanism: one algorithm–threshold pair, chosen on the anchor operator, is propagated unchanged to all other coefficients, while comparison-only intervals are preserved. A fixed-intercept quadratic fit of the post-selection cross section as a function of the Wilson coefficient, combined with the asymptotic Poisson sig","core_discovery":"The paper's core claim is that a full one-coefficient SMEFT study—from Lagrangian to detector-level events, machine-learning selection, and statistical interval—can be structured as twelve typed state transitions, with an LLM-based agent confined to planning and orchestration while deterministic domain tools produce all numbers. The decisive mechanism is the phase manifest: every LLM-generated artifact is written to disk and validated before the corresponding computation runs, so the numerical execution becomes a deterministic replay and the language model plays no role in producing the final results. The paper also introduces an anchor-pinned ML-selection rule: a single algorithm–threshold","pith_inferences":["The manifest-first design could generalize beyond SMEFT: any multi-tool pipeline with brittle handoffs, such as global fits, BSM scan campaigns, or detector-optimization loops, could adopt the same pattern of locking configuration, declaring artifacts, and auditing cross-phase identifiers.","The anchor-pinned selection rule is one specific answer to a general problem in ML-assisted searches—how to define a fixed operating point when classifiers are retrained per signal hypothesis; a future extension might make the anchor choice itself a fitted hyperparameter.","The audit's self-consistency check could be strengthened by an external oracle, such as re-deriving a few scanned cross sections with an independent generator; the paper does not claim such validation, but the replay package makes it straightforward to attempt.","The reference muon-collider numbers are deliberately conditional and are not physics claims; a natural next step is to run the same pipeline with systematic uncertainties and a global likelihood, which the architecture's phase structure is designed to absorb."],"forward_implications":["If the architecture works as claimed, a one-coefficient SMEFT study can be dispatched from a single plain-language request and run unattended through generation, simulation, ML selection, fitting, and manuscript drafting.","Reproducibility is no longer tied to conversational history: replaying the locked configuration and the phase manifests regenerates the numerical results without re-invoking the language model.","Cross-operator comparisons are protected by the anchor-pinned selection rule, so headline intervals share one operating point; users can switch selection scopes later using the retained per-operator intervals.","The Phase 12 audit checks internal consistency of the declared artifacts—existence, parseability, matching identifiers, event counts—and therefore certifies that the workflow executed the declared study definition, though not the physical validity of the model or detector assumptions."],"fun_headline_variants":["AI orchestrates complete particle physics study hands-free","Every AI step logged: SMEFT study now fully reproducible","AI plans, tools compute: SMEFT study with zero LLM numbers","One plain sentence triggers full SMEFT analysis pipeline","Replayable AI workflow makes SMEFT phenomenology deterministic"],"cache_read_input_tokens":2304,"weakest_assumption_plain":"The design assumes the language model correctly translates user intent into runnable parameter files at every phase boundary without silently altering physical parameters; no test suite or formal guarantee is provided, and the audit checks only internal consistency of declared artifacts, not physical correctness.","fun_headline_variants_meta":{"raw":{"variants":["AI orchestrates complete particle physics study hands-free","Every AI step logged: SMEFT study now fully reproducible","AI plans, tools compute: SMEFT study with zero LLM numbers","One plain sentence triggers full SMEFT analysis pipeline","Replayable AI workflow makes SMEFT phenomenology deterministic"]},"model":"deepseek-v4-flash","effort":"low","cost_usd":0.000707,"raw_usage":{"total_tokens":3010,"prompt_tokens":720,"completion_tokens":2290,"prompt_tokens_details":{"cached_tokens":256},"prompt_cache_hit_tokens":256,"prompt_cache_miss_tokens":464,"completion_tokens_details":{"reasoning_tokens":2210}},"tokens_in":464,"tokens_out":2290,"duration_ms":14040,"temperature":1.0,"reasoning_tokens":2210,"cache_read_input_tokens":256,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-08-01T05:04:46.135475+00:00","model_set":{"reader":"deepseek-v4-flash"},"falsifier":"A concrete test would be to take a fixed study request, run the pipeline twice with different language models or with random seeds varied, and compare the final statistical intervals: if the results differ, the numerical outcome depends on the LLM's phase-boundary translations. A sharper check is to hand-edit one manifest entry to a physically wrong but internally consistent parameter value and see whether the Phase 12 audit still passes; passing would show the audit cannot detect study-definition corruption.","supporting_citations":[],"review_version":1}