{"id":"86de3d6f-672c-44ee-91f7-7f4961b95b5a","arxiv_id":"2605.18033","paper_version":1,"verdict":"UNVERDICTED","confidence":"LOW","novelty_score":6.0,"correctness_risk":"unknown","formal_verification":"none","parameter_count":0,"one_line_summary":"MAD uses a multi-output Gaussian process with co-regionalization kernel to jointly map crystal structures via NMF and optimize electrical resistance in a closed-loop, identifying promising PCMs in the Mn-Sb-Te ternary within 25 iterations for a reported seven-fold speedup.","lead":"The paper introduces the Multi-instrument Autonomous Discovery (MAD) framework that runs X-ray diffraction and electrical resistance measurements together in real time to discover phase-change memory materials in the unexplored Mn-Sb-Te system. A smart generalist might read it to see how autonomous labs can coordinate different instruments during live experiments instead of analyzing data after the fact.","discovery_kind":"new_method","skeptic_critique":{"model":"grok-4.3","headline":"Seven-fold speed-up claim lacks explicit, reproducible baseline comparison","rationale":"Reader correctly flags the multi-output kernel as a key assumption, but the speedup number is the most load-bearing quantitative claim in the strongest_claim sentence. Verifying its baseline is a single, falsifiable check that directly tests whether the reported acceleration is real rather than rhetorical.","tokens_in":1781,"tokens_out":271,"duration_ms":21326,"concrete_test":"Locate the paragraph or supplementary note that states the baseline iteration count and the exact formula used to obtain the factor of seven; recompute the ratio from the reported 25 iterations and check whether the baseline matches a documented control experiment or simulation.","verdict_should_be":"CONDITIONAL","load_bearing_attack":"The headline result (SPSPR identified in 25 closed-loop iterations = 7× speedup) requires that a specific baseline iteration count was measured or simulated under otherwise identical conditions without the MAD multi-output co-regionalization. The abstract states the factor but supplies no equation, table, or section defining the comparator (random search, sequential single-instrument loops, or post-hoc analysis). If the baseline is not independently recoverable from the methods or supplementary data, the numerical claim cannot be verified and the practical advantage of simultaneous structural+resistance optimization remains unquantified.","agreement_with_reader":"partial"},"referee_report":{"model":"grok-4.3","summary":"The manuscript introduces the Multi-instrument Autonomous Discovery (MAD) framework for real-time integration of heterogeneous data streams from multiple instruments in closed-loop autonomous materials discovery. Applied to the unexplored Mn-Sb-Te ternary system for phase-change memory (PCM) materials, it employs a multi-output Gaussian process model with co-regionalization kernel to simultaneously perform structural mapping via non-negative matrix factorization on XRD data and optimize electrical resistance. The framework identifies promising PCM compositions and the synthesis-process-structure-property relationship (SPSPR) in 25 iterations, reported as a seven-fold speedup.","tokens_in":1896,"tokens_out":519,"duration_ms":41231,"significance":"If the performance claims hold, the work offers a meaningful step toward practical multi-instrument autonomous labs by demonstrating simultaneous structural and functional optimization from unsynchronized data. The experimental application to a new PCM system and use of standard multi-output GP machinery with fresh measurements provide a concrete example that could inform scaling to larger facilities.","major_comments":[{"comment":"Abstract and Results section: the headline claim that the SPSPR was identified within 25 closed-loop iterations 'corresponding to a seven-fold speed-up' is load-bearing for the central contribution, yet no explicit baseline (random search, sequential single-instrument loops, or simulated comparator) is defined or reported with iteration counts, error bars, or supplementary data, preventing independent verification of the numerical factor.","section":"Abstract / Results"},{"comment":"Section 3 (Multi-output model description): the co-regionalization kernel is stated to merge heterogeneous, unsynchronized XRD and resistance streams into shared posteriors that support simultaneous decision-making, but the manuscript provides no quantitative validation (e.g., cross-validation metrics or ablation on synchronization handling) showing that the shared uncertainty estimates remain reliable when the two tasks have different objectives.","section":"Section 3"}],"minor_comments":[{"comment":"Notation for the co-regionalization kernel and NMF components could be introduced more explicitly with a small equation block to aid readers unfamiliar with multi-output GPs.","section":"Section 3"},{"comment":"Figure captions should specify the exact number of iterations shown and whether error bars represent model uncertainty or experimental replicates.","section":"Figures"}],"recommendation":"major_revision","confidential_remarks":"The manuscript aligns with the journal's scope in autonomous materials discovery; the primary concern is documentation of the speedup baseline rather than a fundamental methodological flaw."},"author_rebuttal":{"model":"grok-4.3","summary":"We thank the referee for their constructive and insightful comments on our manuscript. We have addressed each major comment point by point below, providing clarifications and committing to revisions where the concerns are valid and can be resolved with additional analysis or data.","responses":[{"response":"We agree that an explicit baseline comparison is necessary to substantiate the seven-fold speedup claim and allow independent verification. The reported figure was based on an internal estimate comparing the 25 iterations of the joint MAD framework against the iteration counts observed in our prior single-instrument sequential experiments on similar systems, but this was not documented with sufficient detail. In the revised manuscript we will add a new supplementary section that includes simulated random-search and sequential single-instrument baselines, each run with multiple random seeds to provide iteration counts, mean performance curves, and error bars. This will make the speedup calculation fully transparent and reproducible.","revision_made":"yes","referee_comment":"[Abstract / Results] Abstract and Results section: the headline claim that the SPSPR was identified within 25 closed-loop iterations 'corresponding to a seven-fold speed-up' is load-bearing for the central contribution, yet no explicit baseline (random search, sequential single-instrument loops, or simulated comparator) is defined or reported with iteration counts, error bars, or supplementary data, preventing independent verification of the numerical factor."},{"response":"We acknowledge that the manuscript would benefit from quantitative validation of the multi-output model under unsynchronized conditions. The co-regionalization kernel follows the standard formulation of multi-task Gaussian processes and was chosen precisely because it permits joint posterior inference even when the two outputs (NMF-derived structural features and resistance) have distinct objectives and sampling times. To address the referee’s request, we will include in the revision (i) k-fold cross-validation metrics on held-out XRD and resistance data, and (ii) an ablation comparing the joint model against independent single-output GPs, quantifying the effect of synchronization handling on uncertainty calibration and decision quality. These additions will demonstrate that the shared uncertainty estimates remain reliable for simultaneous structural mapping and property optimization.","revision_made":"yes","referee_comment":"[Section 3] Section 3 (Multi-output model description): the co-regionalization kernel is stated to merge heterogeneous, unsynchronized XRD and resistance streams into shared posteriors that support simultaneous decision-making, but the manuscript provides no quantitative validation (e.g., cross-validation metrics or ablation on synchronization handling) showing that the shared uncertainty estimates remain reliable when the two tasks have different objectives."}],"tokens_in":1476,"tokens_out":537,"duration_ms":45439,"standing_objections":[]},"desk_editor":{"model":"grok-4.3","letter":"The main point is that this work shows a live fusion of XRD and resistance data inside an autonomous loop for phase-change materials. They apply a multi-output Gaussian process with a co-regionalization kernel to the Mn-Sb-Te ternary, using non-negative matrix factorization for structural coverage while pushing for high resistance in parallel. After 25 iterations they report promising PCM candidates and the overall synthesis-process-structure-property map, along with a claimed seven-fold speed-up over conventional routes.","headline":"The paper runs a closed-loop multi-instrument setup with a co-regionalization kernel on Mn-Sb-Te to map structures and optimize resistance at once, but the seven-fold speedup rests on an undefined baseline.","tokens_in":2407,"tokens_out":179,"would_cite":false,"duration_ms":34943,"reading_group":"maybe","serious_thinker":"yes","would_accept_peer_review":true},"rs_alignment":{"model":"grok-4.3","evidence":[],"headline":"Standard multi-task GP + BO for materials discovery; no RS-shaped cost or forcing structure","alignment":"orthogonal","rationale":"The paper's central machinery (co-regionalized multi-output GP with ICM kernel, NMF phase abundances, separate MU/EI acquisition functions for simultaneous phase mapping and resistance optimization) is conventional Bayesian active learning applied to heterogeneous experimental streams. It contains no J-cost, reciprocal symmetry, golden-ratio ladder, 8-tick periodicity, or parameter-free derivation of constants. The claimed seven-fold speedup is benchmarked against independent GPs and random sampling, not against any RS-derived cost or recognition-theoretic baseline. No RS theorem (e.g., reality_from_one_distinction, Jcost uniqueness, phi_fixed_point, DimensionForcing) is paralleled or contradicted.","tokens_in":51332,"confidence":"high","tokens_out":178,"duration_ms":8999,"cache_read_input_tokens":38528,"cache_creation_input_tokens":0},"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"grok-4.3","headline":"The MAD framework merges live XRD and resistance data to map crystal structures and optimize phase-change memory properties simultaneously in a closed loop.","keywords":["autonomous discovery","phase-change memory","multi-instrument integration","closed-loop optimization","Mn-Sb-Te ternary","X-ray diffraction","electrical resistance","materials discovery"],"falsifier":"Running the same Mn-Sb-Te search with independent per-instrument loops instead of the merged multi-output model and observing that more than 25 iterations are required to reach comparable identification of promising PCMs and the SPSPR.","tokens_in":2702,"feed_emoji":"🔬","tokens_out":726,"duration_ms":28343,"temperature":0.7,"pith_summary":"The paper presents the Multi-instrument Autonomous Discovery (MAD) framework to solve the problem of integrating heterogeneous and unsynchronized data from multiple instruments during ongoing experiments rather than after full collection. It applies this to the previously unexplored Mn-Sb-Te ternary system for phase-change memory materials by using a multi-output model with a co-regionalization kernel that links X-ray diffraction structural information to electrical resistance measurements. The shared probabilistic posteriors and uncertainty estimates support two concurrent goals: maximizing knowledge of crystal structure distributions through non-negative matrix factorization while locating the composition with peak resistance. In practice this identified promising electrical PCM candidates and the underlying synthesis-process-structure-property relationship after only 25 closed-loop iterations. The work indicates that future large-scale autonomous facilities can run experiments in parallel with shared knowledge instead of handling each instrument independently.","feed_headline":"Multi-instrument loop finds phase-change materials in 25 steps","feed_subtitle":"MAD merges live XRD and resistance data to map structures and optimize resistance seven times faster than separate runs.","key_machinery":"The multi-output model with co-regionalization kernel that links unsynchronized XRD and resistance measurements to produce shared posteriors and uncertainty estimates for joint decision making across differing objectives.","core_discovery":"The MAD framework combines structural property mapping and functional property optimization in real time by employing a multi-output model whose co-regionalization kernel merges heterogeneous XRD and resistance data streams, yielding shared posteriors that enable simultaneous non-negative matrix factorization of crystal structures and direct optimization of maximum resistance values, thereby identifying the undetermined SPSPR and promising PCM compositions in the Mn-Sb-Te system within 25 closed-loop iterations.","pith_inferences":["The same merged-data approach could accelerate discovery in other material families where structural and transport measurements are complementary but collected at different times.","Extending the framework to three or more instruments would require only additional output dimensions in the same co-regionalization structure.","The reported seven-fold reduction in iterations suggests that closed-loop multi-instrument methods scale favorably as the number of parallel experimental stations increases."],"forward_implications":["Promising electrical PCM candidates can be located in unexplored ternary composition spaces with far fewer total measurements.","The SPSPR for a material system can be determined while actively optimizing a key functional figure of merit in the same experimental campaign.","Decision making can draw on shared knowledge across instruments even when the immediate goals of each task differ.","Large-scale autonomous facilities can schedule parallel rather than sequential experiments across characterization tools."],"fun_headline_variants":["MAD merges XRD and resistance data to map PCM structures in 25 steps","MAD optimizes resistance while mapping crystal structures in 25 loops","Multi-instrument MAD identifies phase-change materials in Mn-Sb-Te","MAD framework uses co-regionalization for PCM discovery in 25 steps"],"cache_read_input_tokens":64,"weakest_assumption_plain":"The co-regionalization kernel can reliably combine heterogeneous, unsynchronized XRD and resistance data into accurate shared posteriors and uncertainty estimates that guide effective simultaneous structural mapping and resistance optimization.","fun_headline_variants_meta":{"raw":{"variants":["MAD merges XRD and resistance data to map PCM structures in 25 steps","MAD optimizes resistance while mapping crystal structures in 25 loops","Multi-instrument MAD identifies phase-change materials in Mn-Sb-Te","MAD framework uses co-regionalization for PCM discovery in 25 steps"]},"model":"grok-4.3","cost_usd":0.014817,"raw_usage":{"total_tokens":6319,"prompt_tokens":731,"num_sources_used":0,"completion_tokens":72,"cost_in_usd_ticks":148165500,"prompt_tokens_details":{"text_tokens":731,"audio_tokens":0,"image_tokens":0,"cached_tokens":64},"completion_tokens_details":{"audio_tokens":0,"reasoning_tokens":5516,"accepted_prediction_tokens":0,"rejected_prediction_tokens":0}},"tokens_in":731,"tokens_out":72,"duration_ms":77372,"temperature":1.0,"reasoning_tokens":5516,"cache_read_input_tokens":64,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-05-20T09:51:16.544292+00:00","model_set":{"reader":"grok-4.3"},"falsifier":"Running the same Mn-Sb-Te search with independent per-instrument loops instead of the merged multi-output model and observing that more than 25 iterations are required to reach comparable identification of promising PCMs and the SPSPR.","supporting_citations":[],"review_version":1}