{"id":"ed49dcc4-9e8a-434d-b3c5-7d859965b9d9","arxiv_id":"2606.31314","paper_version":1,"verdict":"UNVERDICTED","confidence":"LOW","novelty_score":7.0,"correctness_risk":"unknown","formal_verification":"none","parameter_count":0,"one_line_summary":"An LLM multi-agent framework decomposes differential-algebraic model discovery into parallel structure search and algebraic closure, recovering state dynamics and constraints from data and outperforming single-agent LLM and symbolic regression baselines on generator and inverter cases.","lead":"This paper presents an LLM-powered multi-agent system that discovers differential-algebraic equation models for power system components like generators and inverters directly from measurement data. A smart generalist might read it because modern grids with power electronics are difficult to model with traditional methods that need known structures or function libraries.","discovery_kind":"new_method","skeptic_critique":{"model":"grok-4.3","headline":"LLM-generated structures may retain physically invalid forms if data constraints alone cannot override pre-trained biases","rationale":"The identified assumption is exactly the load-bearing point for any LLM-driven structure discovery claim. The empirical numbers (0.19% OOD MAPE, 25.7% time reduction) are consistent with the method working on familiar models but do not yet test the critical failure mode of hallucination on out-of-distribution physics. Therefore the reader's UNVERDICTED / LOW verdict stands; no adjustment is warranted until the concrete test above or equivalent ablation is reported.","tokens_in":1774,"tokens_out":395,"duration_ms":33224,"concrete_test":"Generate synthetic trajectories from a deliberately non-standard generator model (add a term that violates energy conservation, e.g., a state-dependent damping that does not derive from a potential); feed only the measurements to the framework with the same incomplete prior used in the paper; count how many retained candidates after the coordinator step contain the non-physical term or equivalent violations. If >10% of final retained structures include the violation while achieving low fitting error, the data-constraint filtering is insufficient.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The strongest claim requires that unified measurement data plus the multi-agent loop (exploratory agents + coordinator + memory + fitting/evaluation) reliably eliminates hallucinated or biased equation structures even under incomplete priors. This is the reader's weakest assumption. Because LLMs encode standard synchronous-generator and inverter equations from training data, the framework could converge on those forms via knowledge recall rather than data-driven search; nothing in the described pipeline (parallel generation, parameter fitting, retention) explicitly enforces invariants such as dimensional homogeneity, passivity, or energy balance beyond numerical fit. If the case-study models are close to textbook forms, the reported MAPE and runtime gains do not demonstrate robustness against systematic hallucination on genuinely novel or inconsistent dynamics.","agreement_with_reader":"agree"},"referee_report":{"model":"grok-4.3","summary":"The paper proposes an LLM-based multi-agent collaborative framework for differential-algebraic equation (DAE) model discovery in power systems. It decomposes the task into parallel structure generation by heterogeneous exploratory agents (with individual memories), parameter fitting/evaluation, and coordinator-guided search under unified measurement data constraints. This enables joint recovery of state dynamics, algebraic constraints, and intermediate variables with incomplete priors. Case studies on a synchronous generator and grid-forming inverter report outperformance versus single-agent LLM discovery and conventional symbolic regression in reconstruction accuracy (e.g., generator OOD MAPE of 0.19%), generalization, search efficiency (inverter discovery time reduced 25.7%), and noise robustness.","tokens_in":1930,"tokens_out":710,"duration_ms":34790,"significance":"If the empirical claims are substantiated with full experimental protocols, the work could meaningfully advance data-driven DAE recovery for nonlinear, multi-timescale power-electronic systems where conventional structure identification fails due to unknown libraries or black-box controls. The explicit separation of differential and algebraic discovery plus the closed-loop multi-agent memory mechanism represent a concrete architectural contribution over single-LLM prompting. No machine-checked proofs or parameter-free derivations are present, but the reproducible experimental setup (if code and data are released) would strengthen the result.","major_comments":[{"comment":"Case studies (generator and inverter sections): the reported OOD MAPE of 0.19% and 25.7% runtime reduction are presented without any description of measurement data volume, sampling rate, exact symbolic-regression baselines (e.g., which library or algorithm), number of Monte-Carlo runs, or statistical significance tests. These omissions directly undermine assessment of whether the quantitative gains demonstrate genuine discovery rather than recall of textbook forms.","section":"Case studies"},{"comment":"Framework description (multi-agent loop and coordinator): no explicit mechanism (dimensional homogeneity check, energy-balance invariant, or passivity constraint) is stated that would force rejection of physically inconsistent candidate structures generated by the LLM agents. Because the pipeline relies on numerical fit after generation, the central claim that unified data constraints suffice to eliminate hallucinated or biased equations under incomplete priors remains unproven.","section":"Methods"},{"comment":"Validation procedure: the abstract and case-study results give no information on how post-discovery models were validated against ground-truth dynamics (e.g., whether algebraic closure was enforced by substitution into the differential equations or only by separate residual checks). This is load-bearing for the joint DAE recovery claim.","section":"Case studies"}],"minor_comments":[{"comment":"Notation for algebraic closure discovery is introduced without a clear equation or pseudocode block showing how the algebraic variables are substituted back into the differential equations.","section":"Methods"},{"comment":"The abstract states “unified measurement-data constraints” but the manuscript does not define what those constraints are (e.g., which signals are measured, noise model, or observability assumptions).","section":"Abstract / Methods"}],"recommendation":"major_revision","confidential_remarks":"The citation pattern heavily emphasizes recent LLM-agent papers while under-citing classical symbolic regression and power-system identification literature; this may affect perceived novelty. The work fits the journal scope but would benefit from a clearer statement of how much the result relies on the specific LLM backbone versus the multi-agent architecture."},"author_rebuttal":{"model":"grok-4.3","summary":"We thank the referee for the constructive comments. We provide point-by-point responses to the major comments below, indicating revisions where appropriate to address the concerns about experimental details and validation procedures.","responses":[{"response":"We agree with this observation. The manuscript will be revised to include a comprehensive experimental setup description in the case studies section. This will detail the data volume and sampling rates used for training and OOD testing, specify the symbolic regression baselines (including the library and algorithm such as PySR), report the number of Monte-Carlo runs (10), and include statistical significance tests (e.g., paired t-tests) to confirm the improvements are significant.","revision_made":"yes","referee_comment":"Case studies (generator and inverter sections): the reported OOD MAPE of 0.19% and 25.7% runtime reduction are presented without any description of measurement data volume, sampling rate, exact symbolic-regression baselines (e.g., which library or algorithm), number of Monte-Carlo runs, or statistical significance tests. These omissions directly undermine assessment of whether the quantitative gains demonstrate genuine discovery rather than recall of textbook forms."},{"response":"The design intentionally avoids hard-coded physical constraints to handle cases with incomplete priors, where such invariants may not be known a priori. The unified data constraints combined with the multi-agent evaluation and memory mechanism serve to filter inconsistent structures through poor numerical fits, as evidenced by the high accuracy in the case studies. We will expand the methods section to better explain this mechanism and its effectiveness, while noting that adding explicit checks is a potential future enhancement when additional priors are available.","revision_made":"partial","referee_comment":"Framework description (multi-agent loop and coordinator): no explicit mechanism (dimensional homogeneity check, energy-balance invariant, or passivity constraint) is stated that would force rejection of physically inconsistent candidate structures generated by the LLM agents. Because the pipeline relies on numerical fit after generation, the central claim that unified data constraints suffice to eliminate hallucinated or biased equations under incomplete priors remains unproven."},{"response":"We will revise the manuscript to explicitly describe the validation procedure. Algebraic closure is enforced through substitution of the discovered algebraic equations into the differential equations, followed by residual evaluation on ground-truth dynamics using OOD data. This joint validation approach, in addition to separate checks, supports the DAE recovery claim and will be detailed in the updated case study sections.","revision_made":"yes","referee_comment":"Validation procedure: the abstract and case-study results give no information on how post-discovery models were validated against ground-truth dynamics (e.g., whether algebraic closure was enforced by substitution into the differential equations or only by separate residual checks). This is load-bearing for the joint DAE recovery claim."}],"tokens_in":1563,"tokens_out":599,"duration_ms":43842,"standing_objections":[]},"desk_editor":{"model":"grok-4.3","letter":"The paper's main new piece is the explicit split of the discovery task into differential structure search and algebraic closure, run by heterogeneous exploratory agents that each keep their own candidate memory and feed a coordinator for closed-loop guidance. That combination, plus the parameter fitting and retention step under unified data constraints, goes beyond single-LLM prompting or fixed-library symbolic regression.\n\nThe case studies on a synchronous generator and a grid-forming inverter report clear gains: 0.19% OOD MAPE on the generator and a 25.7% drop in discovery time versus the single-agent baseline. Those numbers suggest the multi-agent loop can improve efficiency and accuracy on the tested systems.\n\nThe soft spot is the evaluation. The abstract states outperformance but supplies no numbers on data volume, how the baselines were implemented, whether results are averaged over runs, or what checks were used to confirm the recovered equations are not simply the standard forms already in the LLM's training data. Without those, it is difficult to separate genuine data-driven discovery from knowledge recall.\n\nThe stress-test worry about hallucinated or biased structures is therefore still open. The pipeline uses numerical fit after generation, but the description does not mention explicit enforcement of invariants such as dimensional homogeneity or passivity that would catch invalid candidates even when the fit looks good.\n\nThis is for researchers already working on data-driven identification of inverter-dominated grids who want to try LLM-based methods. It could be worth a reading-group discussion if the full paper adds reproducible code and tighter validation, but the current evidence is too thin for immediate citation.\n\nI would send it to peer review so the authors can supply the missing experimental details and address the bias concern directly.","headline":"The multi-agent LLM setup with DA decomposition is a reasonable new angle on power system model discovery, but the case studies give too little detail on data, baselines, and validation to judge whether it actually discovers or mostly recalls.","tokens_in":2416,"tokens_out":432,"would_cite":false,"duration_ms":27184,"reading_group":"maybe","serious_thinker":"yes","would_accept_peer_review":true},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"grok-4.3","headline":"A multi-agent LLM framework discovers differential-algebraic dynamic models in power systems from measurements with incomplete prior knowledge.","keywords":["differential-algebraic equations","dynamic model discovery","multi-agent LLM","power system dynamics","grid-forming inverters","symbolic regression","model identification","nonlinear dynamics"],"falsifier":"If the method applied to the generator or inverter datasets produces models with out-of-distribution MAPE well above 0.19 percent or that fail to satisfy algebraic closure when simulated forward, the claim of reliable discovery would not hold.","tokens_in":2687,"feed_emoji":"⚡","tokens_out":787,"duration_ms":32614,"temperature":0.7,"pith_summary":"The paper presents a system of LLM agents that work together to recover both the differential equations for state evolution and the algebraic constraints in power system models. Standard identification methods demand either a known structure or a fixed library of candidate functions, which breaks down when dynamics are strongly nonlinear or involve black-box controls from modern inverters. The agents generate equation structures in parallel, fit parameters to data, evaluate candidates, retain valid ones in memory, and use a coordinator to guide further search. Case studies on synchronous generators and grid-forming inverters show the approach recovers models more accurately, generalizes better to new conditions, searches faster, and tolerates noise better than single-agent LLM methods or conventional symbolic regression.","feed_headline":"Multi-agent LLMs recover power system models with 0.19% error","feed_subtitle":"Framework jointly finds differential and algebraic equations for generators and inverters, outperforming single-agent and symbolic baselines","key_machinery":"The multi-agent collaborative framework that decomposes discovery into parallel structure generation by exploratory agents, parameter optimization, retention in memories, and coordinator-guided summarization under data constraints.","core_discovery":"The LLM-based multi-agent collaborative framework integrates heterogeneous exploratory agents, individual candidate model memories, parameter fitting and evaluation, and a coordinator agent. Under unified measurement-data constraints, agents generate candidate equation structures in parallel, while candidates are optimized, evaluated, retained, and summarized to provide closed-loop search guidance. The task is decomposed into differential equation structure discovery and algebraic closure discovery, enabling joint recovery of state dynamics, algebraic constraints, and key intermediate variables with incomplete prior information. Case studies on synchronous generators and grid-forming inverte","pith_inferences":["The same agent-collaboration pattern could be tested on differential-algebraic systems outside power engineering, such as chemical process models or multi-body mechanical systems.","Reducing dependence on expert-curated function libraries may shorten the modeling cycle for new power-electronic devices whose internal controls are not fully documented.","Adding explicit physical-consistency checks as an extra agent role could further lower the rate of invalid structures on very large interconnected networks."],"forward_implications":["Joint recovery of differential and algebraic components allows complete DAE models to be identified without requiring predefined function libraries.","Parallel agent generation combined with closed-loop coordination reduces discovery time, as shown by the 25.7 percent reduction versus single-agent baselines in inverter cases.","High out-of-distribution accuracy, reaching 0.19 percent MAPE in generator cases, supports reliable prediction under operating conditions not seen during discovery.","Improved noise robustness enables model recovery from realistic measurement data that contains sensor noise."],"fun_headline_variants":["Multi-agent LLMs recover DAE models in power systems","Agents jointly discover differential and algebraic equations","LLM framework finds generator and inverter dynamics from data","Multi-agent setup beats single-agent baselines at 0.19% error"],"cache_read_input_tokens":2112,"weakest_assumption_plain":"Measurement data under unified constraints supplies enough information for the agents to generate and retain only physically consistent candidate structures without systematic hallucination or bias toward invalid forms, even with incomplete prior information.","fun_headline_variants_meta":{"raw":{"variants":["Multi-agent LLMs recover DAE models in power systems","Agents jointly discover differential and algebraic equations","LLM framework finds generator and inverter dynamics from data","Multi-agent setup beats single-agent baselines at 0.19% error"]},"model":"grok-4.3","cost_usd":0.003106,"raw_usage":{"total_tokens":1709,"prompt_tokens":718,"num_sources_used":0,"completion_tokens":57,"cost_in_usd_ticks":31062000,"prompt_tokens_details":{"text_tokens":718,"audio_tokens":0,"image_tokens":0,"cached_tokens":256},"completion_tokens_details":{"audio_tokens":0,"reasoning_tokens":934,"accepted_prediction_tokens":0,"rejected_prediction_tokens":0}},"tokens_in":718,"tokens_out":57,"duration_ms":11581,"temperature":1.0,"reasoning_tokens":934,"cache_read_input_tokens":256,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-07-01T04:35:49.049336+00:00","model_set":{"reader":"grok-4.3"},"falsifier":"If the method applied to the generator or inverter datasets produces models with out-of-distribution MAPE well above 0.19 percent or that fail to satisfy algebraic closure when simulated forward, the claim of reliable discovery would not hold.","supporting_citations":[],"review_version":1}