EvoSim: Learning to Model, Modeling to Learn

Yun-Wei Song; Jinkai Tao; Jun-Dong Zhang; Rui Zhang; Yi-Min Wu; Qiang Zhang

cs.AIOct 8, 2026

EvoSim: Learning to Model, Modeling to Learn

Authors: Yun-Wei Song, Jinkai Tao, Jun-Dong Zhang, Rui Zhang, Yi-Min Wu, Qiang Zhang

Organizations: Beijing Key Laboratory of Complex Solid-State Batteries, Department of Chemical Engineering, Tsinghua University, Beijing, China · State Key Laboratory of Chemical Engineering and Low-Carbon Technology, Tsinghua University, Beijing, China · Beijing Tsingyu Technology Co., Ltd., Beijing, China · School of Computing and Data Science, The University of Hong Kong, Hong Kong, China · Beijing Huairou Laboratory, Beijing, China · Tanwei College, Tsinghua University, Beijing, China · AI Solid-State Battery Innovation Center, Yibin, China · Shanxi Research Institute for Clean Energy, Tsinghua University, Taiyuan, China

Abstract

Physics-based models connect scientific explanation with quantitative prediction. Constructing them requires selecting physical processes, defining states and governing equations, specifying couplings, and identifying parameters from experiments. Existing AI systems remain limited in making these model structure decisions autonomously. We introduce EvoSim, a self-evolving AI scientist for physical modeling. It uses experimental discrepancies to drive mechanism and equation revisions and held-out experimental data to test physical plausibility. Exploration traces make updates to knowledge, skills, and multi-agent orchestration. This co-evolution improves physics-based models and EvoSim's ability to select mechanisms, diagnose failures, and coordinate research. We evaluate EvoSim on two industrial battery modeling tasks. It predicts lithium-metal-plating onset from 25 to 45 degrees Celsius and 2 C to 6 C with a mean absolute error of 1.79% in state of charge. Dynamic voltage prediction under vehicle driving conditions achieves a root mean square error of 7.62 mV, surpassing the reported accuracy of models developed by human experts. Self-evolution reduces model and physics errors by approximately 36% relative to baseline, demonstrating improved scientific modeling capability. EvoSim turns experimental observations into validated models and cumulative research expertise.

Figures & tables

Figure 1: EvoSim co-evolves a physics-based model and the AI scientist that develops it.

Figure 2: EvoSim’s architecture and coupled evolution. (a) The research loop connects hypothesis formation, research planning, model construction, validation, and Scientific Evolution. (b) Model evolution develops and evaluates alternative hypotheses through computational modeling and scientific validation. Scientific evolution uses feedback from past investigations to update knowledge, skills, tools, and research strategies.

Figure 3: Lithium-plating onset and dynamic-voltage predictions. (a) Onset SOC and accumulated irreversible lithium for two graphite-electrode designs across temperatures and charging rates. (b) Measured and predicted onset SOC for 20 conditions (left), and voltage RMSE compared with reference values (right). The voltage panel reports cross-validation errors for 30 ∘ C DST (7.62 mV) and 20 ∘ C US06 (8.97 mV). (c) Complete measured and predicted voltage traces for the same two cross-validation results.

Figure 4: Effect of Scientific Evolution. (a) Baseline investigations and investigations using saved procedures on three tasks. (b) Failure counts and execution measures across 45 matched pairs from 11 modeling tasks. Each measure is normalized to its corresponding Baseline value, shown as 100%. The figure label Improved denotes EvoSim with Scientific Evolution.

Condition	Best valid RMSE (mV)	Progress during search
Baseline	89.478	Unchanged through 12 attempts
Naive Memory	89.383	Unchanged through 12 attempts
Scientific Evolution	22.015	Attained on attempt 2

Table 1: Ablation on the four-state voltage task. Performance and progress during the model search.

Appendix figures & tables31 assets

Supplementary material from the paper’s appendix.

Appendix

Part	Fixed before a run	Operation available to the agent	Evidence saved after the run
Fixed task inputs	Scientific target, starting model, input conditions, required outputs, observations, and run rules	Read the fixed files and propose permitted model modifications	Input-file list, file hashes, and task directory
Starting model	Physics-based equations, geometry, parameters, initial conditions, input time range, and relation to the observations	Copy the current model and edit permitted model or solver elements	Submitted Java source or physics-based model file, parameter values, and model-state checks
Multiphysics solver	Local or remote solver and the required output format	Build each candidate, solve it, and request the specified fields and states	Solver status, logs, output files, and resource use
Research record	Current model, permitted earlier records, saved procedures, and acceptance rule	Interpret feedback and select the next candidate under the same rules	Hypothesis, change, diagnosis, decision, and link to the parent model
Checks and metric	Implementation checks, solution checks, measurement metric, and any specified comparison target	Receive the observations and feedback released by the task	Rebuild, solve, export, compare, and record the decision
Budget and isolation	Work slots, time and job limits, fixed inputs, and permitted directories	Spend a slot and write only inside the candidate directory	Input-file list, access log, budget record, and saved outputs

Appendix

Table 2: Fixed inputs, permitted actions, and saved evidence in the reported experiments.

Case	Observed result	Physical hypothesis and model change	Evidence and decision
Plating	A basic DFN/P2D model requires cell-specific transport and reaction functions before onset can be calculated.	Specify geometry, electrolyte transport, particle diffusion, graphite equilibrium potential, and intercalation kinetics for A1 and A2.	Unit, reference-condition, positivity, state-limit, and conservation checks establish an executable Li $\mid$ graphite model.
Plating	Transport and intercalation cannot generate metallic lithium.	Add a competing plating/stripping reaction driven by $\eta_{\mathrm{Li}}=\phi_{s}-\phi_{e}-U_{\mathrm{Li}}$ .	Retain only if negative plating overpotential activates deposition during fast charging and the added reaction preserves lithium conservation.
Plating	Instantaneous reaction current and net plated lithium can decrease during stripping.	Integrate $\max[-a_{s}i_{\mathrm{Li}},0]$ through the graphite thickness and time, then convert the accumulated deposition to irreversible lithium.	Retain the monotonic accumulated state because it matches the measured onset definition and remains well defined during later stripping.
Plating	A1 and A2 have different areal capacities and onset boundaries.	Normalize the onset threshold by areal capacity and represent irreversible lithium as a fixed fraction of accumulated deposition.	The capacity-normalized thresholds support comparison across the measured A1 and A2 conditions.
Plating	The constant-fraction representation retains systematic onset residuals across charge rates.	Compare it with a rate-dependent mapping from deposited lithium to irreversible lithium.	On the same six validation conditions, the rate-dependent representation lowers MAE from 3.79 to 1.79 percentage points and RMSE from 5.03 to 2.11 percentage points.
Voltage	Four electrochemical states reproduce the main voltage level but not the response after a current change.	Solve average and surface electrode states with temperature-dependent transport, kinetics, and resistance.	The starting diagnostic gives 32.74 mV.

Appendix

Table 3: Physical observations, tested hypotheses, and decisions in the two model-development paths.

Electrode	Areal capacity	Thickness	Temperature	Charge rates
	(mAh cm -2 )	( $\mu$ m)	( ∘ C)
A1	2.1	47	25, 35, 45	4 C, 5 C, 6 C
A2	3.1	70	25	2 C, 3 C, 4 C, 5 C, 6 C
A2	3.1	70	35, 45	4 C, 5 C, 6 C

Appendix

Table 4: Experimental matrix used for lithium-plating onset modeling.

Observation	Tested model change	Decision
The common starting point supplies only the basic DFN/P2D equations.	Set electrode thickness, phase fractions, particle radius, initial concentrations, and the conversion from C-rate to applied current.	Retained after geometry, capacity, and initial-state checks for A1 and A2.
Electrolyte polarization changes strongly with concentration and temperature.	Implement $D_{e}(c_{e},T)$ , $\kappa_{e}(c_{e},T)$ , and $t_{+}^{0}(c_{e},T)$ together with porous-medium corrections.	Verify finite positive transport properties and $0<t_{+}^{0}<1$ over 25–45 ∘ C and the simulated concentration range.
Graphite transport and reaction rates change with filling fraction and temperature.	Implement $D_{s}(x,T)$ , $U_{\mathrm{gr}}(x)$ , and $i_{0,\mathrm{int}}(c_{e},c_{s},T)$ and evaluate them at the reference conditions.	Verify physical particle states, voltage response, and lithium conservation before testing plating kinetics.
The DFN/P2D model cannot produce metallic lithium.	Add a plating/stripping reaction coupled to electrolyte concentration and the local solid–electrolyte potential difference.	Retained. The reaction becomes active during fast charging and produces a spatial deposition rate.
The reversible plated-lithium state can decrease during stripping.	Compare instantaneous current, net plated lithium, and a deposition-only accumulated state.	Retain the deposition-only state for the onset calculation.
The experimental target is a capacity-normalized accumulated quantity.	Define a constant-fraction representation as $Q_{\mathrm{irr}}=0.30Q_{\mathrm{plate}}$ and apply a threshold equal to 0.05% of areal capacity.	Retained as the reference representation. Capacity normalization gives thresholds of 37.8 and 55.8 C m -2 for A1 and A2.

Appendix

Table 5: EvoSim’s physical hypotheses and decisions in the lithium-plating case.

Parameter	Retained value
Graphite thickness ( $\mu$ m)	47/70
Electrolyte volume fraction	0.374/0.354
Active-solid volume fraction	0.5954/0.5901
Graphite particle radius ( $\mu$ m)	4
Initial electrolyte concentration (mol m -3 )	1200
$i_{0,\mathrm{Li}}^{\mathrm{ref}}$ (A m -2 )	0.75

Appendix

Table 6: Selected quantities in the retained rate-dependent lithium-plating model. Slash-separated entries give A1/A2 values.

Elec.	Temp. ( ∘ C)	Rate	Data role	Exp. (%)	Constant (%)	Rate dep. (%)	Constant error (pp)	Rate-dep. error (pp)
A1	25	4 C	Validation	61.44	65.31	62.47	+3.88	+1.04
A1	25	5 C	Training	57.33	55.94	55.07	-1.39	-2.26
A1	25	6 C	Training	48.25	45.47	47.83	-2.78	-0.43
A1	35	4 C	Training	67.11	69.81	66.96	+2.69	-0.15
A1	35	5 C	Validation	59.28	61.77	61.29	+2.49	+2.01
A1	35	6 C	Training	54.41	54.02	56.06	-0.39	+1.65

Appendix

Table 7: Pointwise comparison of experimental onset SOC with the constant-fraction and rate-dependent representations. The fixed split contains 14 training conditions and six validation conditions. Model selection uses only the training conditions. Errors are prediction minus experiment in SOC percentage points.

Condition	Experiment (%)	Published model (%)	EvoSim (%)	Published error (pp)	EvoSim error (pp)
A1, 25 ∘ C, 4C	61.44	60.53	62.47	-0.90	+1.04
A1, 35 ∘ C, 5C	59.28	59.58	61.29	+0.30	+2.01
A1, 45 ∘ C, 6C	56.75	60.11	60.45	+3.36	+3.69
A2, 25 ∘ C, 2C	70.39	69.47	69.94	-0.92	-0.45
A2, 35 ∘ C, 4C	49.80	47.22	52.40	-2.58	+2.60
A2, 45 ∘ C, 6C	46.49	37.59	45.54	-8.90	-0.95

Appendix

Table 8: Comparison on the six conditions reserved for EvoSim validation. EvoSim does not use these conditions for parameter identification. The source-study model used the full 20-condition matrix during model development ( Konz et al., 2023 ) . Errors are prediction minus experiment in SOC percentage points.

Candidate and observation	Model definition or modification set	Reported RMSE	Supported conclusion
Starting diagnostic	Four electrode filling states; equilibrium potentials; charge-transfer polarization; internal resistance	32.74 mV	The main voltage level is present. The response after current changes remains incomplete.
Electrochemical foundation	Four electrode filling states with equilibrium, charge-transfer, and ohmic terms.	32.12 mV	Establishes the starting physical voltage model.
Extended physics-based model	Solve electrolyte transport, solid diffusion, Faradaic intercalation, double-layer charging, and temperature-dependent transport and kinetics together.	Run result	The complete voltage trace remains finite and satisfies the physical checks.
Held-out runs	Apply the fixed physics-based model to complete traces at temperatures excluded from parameter identification.	7.62 and 8.97 mV	These are results for individual runs at 30 ∘ C DST and 20 ∘ C US06.

Appendix

Table 9: Recorded hypotheses, model changes, and decisions in the dynamic-voltage model sequence.

Figure 5: Selected complete-trace runs of the physics-based voltage model. Figure 3 c reports two separate held-out results.

Figure 6: Remaining complete-trace fits using all available data. Together with Figure 5 , these panels contain all 16 traces.

Model structure	Comparison condition	RMSE (mV)
Same A123 trace and inputs; original identification procedures
Physics-based model (this work)	Full-data run; not held out	6.08
CPG-SPMT ( Guo and Couto, 2026a )	Published result on the same condition	32.10
DST/US06 dynamic-voltage results reported by each study
Physics-based model (this work)	Held-out results shown in Figure 3	7.62, 8.97
NDCTNet ( Tu et al., 2024 )	Reported results	12.25, 13.01

Appendix

Table 10: Reported dynamic-voltage RMSE under DST or US06 operating conditions. The first block uses the same A123 A1-007 25 ∘ C DST measurement trace and inputs.

Question	Evidence	Conclusion supported
Does Scientific Evolution produce a usable procedure?	An earlier continuous-state model gives 88.518 mV error against the reference implementation. EvoSim diagnoses the discrete state update, checks the corrected implementation at 4.1136 mV, and saves the procedure.	EvoSim converts its own model evidence into a checked action with stated conditions.
Does the module improve the next decision?	Baseline, Naive Memory, and Scientific Evolution use the same comparison task, starting model, data, solver, checks, and maximum of 12 attempts. The first three attempts are the matched comparison. Naive Memory and Scientific Evolution use the same source investigation.	Scientific Evolution reaches 22.015 mV. The chronological summary remains at 89.383 mV.
Does recursive updating occur?	Four successive research steps connect failure diagnosis, a checked correction, a 24-condition application, and the controlled comparison.	Each of the first three results changes the procedure used in the following investigation. The following result supplies evidence for the next update.
Does the updated procedure help later work?	The procedure changes the later state implementation and reaches a valid lower-error model on attempt 2. Three related-task records report fewer revisions or lower error after later use.	The updates change later model decisions, implementation effort, and numerical results within and across tasks.
Does the effect persist across repeated tasks?	Forty-five paired experiments compare Baseline with Scientific Evolution after the procedures and pair list are fixed.	Model / physics failures decrease from 33 to 21; the exact paired test remains significant after correction across six labels.

Appendix

Table 11: Evidence used to evaluate Scientific Evolution.

Condition	Earlier record	Information available for the next decision
Naive Memory	“A later version represented the sample-wise state recurrence explicitly and reduced the 30 degC DST COMSOL-to-Python voltage RMSE to about 4 mV.”	Identifies the general cause and records the earlier result. It does not state the recurrence, sample timing, state binding, initial-state check, temperature rule, or invalid-output conditions.
Scientific Evolution	“For every $k>0$ , use current and temperature from sample $k$ ” and “Export time, current, temperature, all four COMSOL dependent states, and terminal voltage directly from COMSOL.”	Gives the exact recurrence, initial values, one temperature conversion, four dependent-state equations, direct-output checks, supported conditions, and failure conditions.

Appendix

Table 12: EvoSim-generated records available to the two conditions. Both records come from the same earlier investigation.

Condition	Earlier record available to the run	Experimental-voltage RMSE (mV)
Baseline	No retained record from the earlier investigation	89.478 mV
Naive Memory	EvoSim-generated chronological summary of the same earlier investigation	89.383 mV
Scientific Evolution	EvoSim-generated procedure produced and checked from the same earlier investigation	22.015 mV

Appendix

Table 13: Best valid result produced within the first three attempts (physics-based model–experimental-voltage RMSE).

Budget level	Baseline	Naive Memory	Scientific Evolution	Interpretation
	RMSE (mV)
$B=3$	89.478	89.383	22.015	First three submissions
$B=6$	89.478	89.383	–	Baseline and Naive Memory persistence check
$B=12$	89.478	89.383	–	Baseline and Naive Memory persistence check

Appendix

Table 14: Best valid RMSE in the first three submissions and during the Baseline and Naive Memory persistence check.

Attempt	Baseline	Naive Memory	Scientific Evolution
1	Continuous physics-based model state equations (89.478; valid)	Continuous Global Equations starting model (89.383; valid)	Setup failed; no model output
2	Previous-sample current and temperature (89.666; valid)	Discrete states and event update; build failure	Exact ZOH, one temperature conversion, and four algebraic states (22.015; valid)
3	Exact ZOH interpolation without the required dependent-state representation (83.638; invalid)	Event reinitialization leaves states at their initial values (394.480; valid)	Nearest/sample-hold state transfer; diagnostic export, not retained

Appendix

Table 15: First three modeling attempts in the three-condition comparison.

Attempt	Baseline	Naive Memory
4	Solver bypass: bypasses the required model equations and returns a supplied target output (invalid)	Global event selection activates updates, but the surface state collapses to the average state (89.578; valid)
5	Repeats the same solver bypass (invalid)	Explicit-Euler surface-state update diverges; failed
6	Repeats the same solver bypass (invalid)	Analytic relaxation restores stability (89.578; valid)
7	Repeats the same solver bypass (invalid)	Exact matrix recurrence, average state first (89.757; valid)
8	Repeats the same solver bypass (invalid)	Surface-state update created first (89.757; valid)
9	Repeats the same solver bypass (invalid)	Shifted zero-order-hold current (89.573; valid)

Appendix

Table 16: Baseline and Naive Memory attempts after the first three attempts. A candidate is invalid when it bypasses the required solver-based state and voltage equations.

Task	Fixed task and acceptance check	Observed outcome	What the outcome demonstrates
Voltage-model implementation	Four required physics-based model states, fixed terminal-voltage relation, complete finite export	Baseline 89.478 mV; Naive Memory 89.383 mV; Scientific Evolution 22.015 mV	The checked procedure changes the implementation and lowers the voltage error
Capacity matching	Specified cell configurations; every candidate must build, solve, and export	Required attempts decrease from 8 to 2	A retained procedure reduces repeated model revisions
P2D–thermal coupling	Coupled model must solve and export the complete temperature trace	Model changes decrease from 10 to 1	A retained procedure changes how the coupling investigation is organized
Thermal-mechanism selection	Particle size, polarization, boundary heat, and convection use one temperature metric	Temperature RMSE decreases from 9.30 to 3.74 ∘ C	A common-check comparison changes both candidate selection and prediction error

Appendix

Table 17: Outcomes of the four task-level model-development comparisons.

Earlier investigation	Later investigation	Reused modeling decision	Result in the later task
Capacity investigation	Specified-configuration capacity-matching task	Apply the tested capacity-matching order and stop only after the configuration, solve, and export checks agree	Required attempts decrease from 8 to 2
Thermal-mechanism investigation	Subsequent P2D–thermal coupling task	Compare independent coupling mechanisms before repeatedly modifying one candidate; retain only a candidate that passes the common temperature check	Sequential model changes decrease from 10 to 1
Thermal-mechanism investigation	Thermal-mechanism selection target	Use one temperature metric and compare particle-size, polarization, boundary-heat, and convection candidates under the same starting conditions	Temperature RMSE decreases from 9.30 to 3.74 ∘ C

Appendix

Table 18: Use of saved procedures in three related modeling tasks. Each procedure is completed before the later task begins. Each later task uses its own acceptance check.

Research step	New evidence	Change to the saved procedure	Observed later use
Failed earlier model	Continuous state equations solve but give 88.518 mV RMSE against the official discrete reference implementation. The surface states contain the largest differences.	Record the discrete state update as the cause to test before changing parameters or the voltage equation.	The next model implements the exact sample-wise zero-order-hold update.
Single-condition correction	The corrected 30 ∘ C DST model gives 4.1136 mV RMSE against the reference and passes the state and output checks.	Retain the state update, temperature conversion, four-state representation, and output checks as a reusable procedure.	A later investigation applies the procedure to every available temperature and drive-cycle condition.
Twenty-four-condition application	All 24 conditions solve. Their mean RMSE against the reference implementation is 4.4625 mV.	Expand the supporting evidence from one trace to 24 conditions while retaining the one-second CPG-SPMT equations and input rules as the stated scope.	The checked procedure becomes available to the later controlled voltage-modeling task.
Controlled comparison	The first application detects a repeated temperature conversion. The next attempt corrects the conversion, represents all four dependent states, and gives 22.015 mV RMSE against measured voltage.	Add the failed double conversion as direct evidence for the temperature check and record the state-output check with the valid implementation.	The procedure changes the modeling sequence and reaches the valid low-error model on attempt 2.

Appendix

Table 19: Successive evidence and updates for the exact zero-order-hold procedure.

Record element	Recorded evidence	Consequence for the next decision
Earlier candidate	The earlier voltage investigation used continuous state equations in the physics-based model. The candidate passed the finite-output and input-value checks but failed the target state and voltage comparison.	The discrepancy motivates separate decisions about the model and the state-update procedure.
Model decision	Reject the continuous implementation for the stated task.	Use the diagnosed discrepancy to examine the state-update procedure.
Proposed procedure	The logs support an exact sample-wise ZOH update. Convert the trace temperature once. Use nearest-neighbor current and temperature inputs with the recorded half-step timing. Represent the computed states as linear time functions on the one-second grid. Preserve the stated equations, voltage bounds, and checked solver settings.	Define a specific operation and the checks needed to evaluate it.
Procedure decision	Retain the exact ZOH procedure for the one-second, 30 ∘ C DST task after the implementation checks pass.	Make the procedure available with the same unit, state-variable, and output checks.
Saved procedure	EvoSim stores the procedure, its source evidence, supported conditions, and file list with the run.	A later attempt can retrieve the operation and its conditions from one record.
First later use	The first application converts a ∘ C value to Kelvin twice and fails the submission check. The next attempt uses one conversion, four algebraic states in the physics-based model, and the fixed output format.	Correct the temperature conversion before testing the retained operation.

Appendix

Table 20: Model and procedure decisions in the fixed four-state voltage task.

Quantity	Count
Modeling tasks with repeated runs	11
Baseline runs	45
Scientific Evolution runs	45
Unique runs	90
Baseline–Scientific Evolution pairs	45

Appendix

Table 21: Run accounting for the paired-run analysis.

Failure mode	Recorded condition
False completion	Completion is declared while a required output, check, or scientific result still fails
Tool operation	Model inspection, build, save, export, or data access is executed incorrectly
Model / physics	A required physical process is absent or inactive, or the tested model form cannot reproduce the target behavior under the task criterion
Data / protocol	Inputs, units, initial conditions, stopping rules, time alignment, or data roles are bound incorrectly
Numerical solver	The solve fails, diverges, becomes singular, or produces non-finite values
Looping / stagnation	Repeated actions or failed recovery do not advance the task to a supported decision

Appendix

Table 22: Operational definitions used for failure-mode labels.

Failure mode	Baseline count	Scientific Evolution count	Baseline- only	Both	Evolution- only	Net decrease (%)
False completion	12	0	12	0	0	100.0%
Tool operation	19	2	17	2	0	89.5%
Model / physics	33	21	15	18	3	36.4%
Data / protocol	14	0	14	0	0	100.0%
Numerical solver	5	2	5	0	2	60.0%
Looping / stagnation	25	5	21	4	1	80.0%

Appendix

Table 23: Failure-mode transitions across 45 same-task pairs.

Failure mode	Baseline- only ( $b$ )	Evolution- only ( $c$ )	Exact $p$ -value	Holm-adjusted $p$ -value
False completion	12	0	0.000488	0.001465
Tool operation	17	0	0.000015	0.000076
Model / physics	15	3	0.007538	0.015076
Data / protocol	14	0	0.000122	0.000488
Numerical solver	5	2	0.453125	0.453125
Looping / stagnation	21	1	0.000011	0.000066

Appendix

Table 24: Exact paired tests for the failure-mode transitions.

Failure mode	Baseline tasks	Scientific Evolution tasks	Baseline runs	Scientific Evolution runs
False completion	1	0	12	0
Data / protocol	4	0	14	0
Tool operation	4	1	19	2
Looping / stagnation	6	2	25	5
Numerical solver	2	1	5	2
Model / physics	7	7	33	21

Appendix

Table 25: Number of tasks and runs with each failure mode. A task is counted when the failure appears in at least one run. Run counts include repeated experiments on the same task.

Measure	Baseline total	Scientific Evolution total	Ratio (%)
Final-check failures	167	53	31.7
Unclosed work items	548	187	34.1
Active runtime (h)	1086.3	623.6	57.4
Elapsed time (h)	1217.7	708.7	58.2
Assigned work items	3792	3169	83.6

Appendix

Table 26: Process totals and ratios across the 45 same-task pairs.

Task family	Scientific objective	Required model output	Evidence required for completion
Parameter identification and calibration	Identify parameter values that explain the recorded observations	Transport, kinetic, geometric, or thermal parameters with specified bounds	Successful solve, exported observables, and comparison with the recorded observations
Forward prediction	Predict a new protocol or operating condition with a fixed model	Voltage, state of charge, capacity, or lifetime curve or map	Prediction metric on the specified evaluation protocol
Mechanism and multiphysics	Determine whether an added physical process is needed and correctly coupled	Executable mechanism, state, equation, or multiphysics coupling	Implementation check, solution check, and comparison with observations
Model diagnosis and procedure	Diagnose a failed model change and establish a reproducible correction	Failure diagnosis, corrected procedure, or verified negative result	Build, solve, and export checks with a record that links the cause to the correction

Appendix

Table 27: Task definitions and completion requirements in the recorded run collection.

Quantity	Count
Recorded runs	179
Runs for application tasks	64
Runs for physics-based battery tasks	115
Task families in the recorded run collection	32
Task families in the cross-task analysis set	26
Run-level records in the cross-task analysis set	65

Appendix

Table 28: Counts for runs, task families, and evaluations.

Outcome	Task families	Count	Reason
Complete evaluation	Molicel P42A transfer; LG M50T diagnostic-to-another-condition; degradation-mode prediction; Panasonic 18650PF drive-cycle evaluation; pouch-cell thermal prediction; LG M50-family cross-dataset transfer; COBRAPRO UDDS; CPG-SPMT FUDS; SEI mechanism competition	9	Fixed candidate, comparison target, numerical result, final decision, and supported conditions are available.
Comparison used for later model changes	Panasonic multi-temperature model; LG M50 full-matrix model	2	The comparison later enters model development, or the final conclusion uses a metric from an earlier model.
Development record	Lithium-plating temperature–rate boundary; lithium-plating risk over temperature and rate	2	The result contributes to threshold construction or has no matching metallic-lithium outcome.
No comparison result	Early-cycle lifetime surrogate; missing-data stop; model-family ablation; phase-field dead-lithium reconstruction; parameter-modification probe; NMC811 material studies; thermal smoke test; NMC811 plating studies; generic plating studies; Samsung 21700-50E calibration; SOC export; rate-capability sweep; SEI degradation demonstrations	13	At least one required part of a complete evaluation record is absent.
Total		26

Appendix

Table 29: Completeness check for the 26 task families in the cross-task evaluation.

Task	Comparison result	Decision	Conclusion supported by the result
Molicel P42A 25 ∘ C C/3 forward comparison	44 segments; 8,827 points; 18.258 mV RMSE	Tested only	Specified 25 ∘ C C/3 segments
LG M50T prediction on another condition	3,610 points; 44.878 mV RMSE	Tested only	Prediction on the specified additional condition
LG M50T degradation-mode prediction	RMSE on another condition: SOH 5.893%; LAM-NE 3.718%; LAM-PE 5.212%; resistance 4.323 m $\Omega$	Limited	Derived degradation quantities
Panasonic 18650PF drive-cycle evaluation	35 cases; mean RMSE 71.107 mV; 34 full windows and 1 valid early cutoff	Tested only	Specified additional drive-cycle set
Reduced pouch-cell thermal model	Surface-mean temperature 2.461 K; voltage 164.593 mV RMSE	Limited	Scalar surface-mean temperature
LG M50-family cross-dataset transfer	146 points; 152.077 mV RMSE	Limited	Source-data calibration

Appendix

Table 30: Complete evaluation records from the task-family analysis.

Explore similar work

SOCIA-EVO: Automated Simulator Construction via Dual-Anchored Bi-Level Optimization
Apr 19, 2026Yuncheng Hua, Sion Weatherhead, Mehdi Jafari +2Evolutionary Optimization
School of Computer Science and Engineering, University of New South Wales, Australia
PhysEvo: Astra Can Act, Let It
Oct 6, 2026Wenqing Tian, Zeyu Zhang, Zhaocheng Liu +3Self-Evolving Agents Robot Policy Adaptation
University of Chinese Academy of Sciences · Institute of Automation, Chinese Academy of Sciences · Independent Researcher +1
EvoScientist: Towards Multi-Agent Evolving AI Scientists for End-to-End Scientific Discovery
Mar 9, 2026Yougang Lyu, Xi Zhang, Yuyue Zhao +11Multi-Agent LLM Systems AI Agents for Scientific Discovery
Huawei Technologies Co., Ltd. · Vrije Universiteit Amsterdam

Apr 19, 2026, cs.AIJ/K move · Enter open · S save

SOCIA-EVO: Automated Simulator Construction via Dual-Anchored Bi-Level Optimization

Yuncheng Hua, Sion Weatherhead, Mehdi Jafari, Hao Xue+1

School of Computer Science and Engineering, University of New South Wales, Australia

Evolutionary Optimization

Open paper AskPDF arXiv

Automated simulator construction requires distributional fidelity, distinguishing it from generic code generation. We identify two failure modes in long-horizon LLM agents: contextual drift and optimization instability arising from conflating structural and parametric errors. We propose SOCIA-EVO, a dual-anchored evolutionary framework. SOCIA-EVO introduces: (1) a static blueprint to enforce empirical constraints; (2) a bi-level optimization to decouple structural refinement from parameter calibration; and (3) a self-curating Strategy Playbook that manages remedial hypotheses via Bayesian-weighted retrieval. By falsifying ineffective strategies through execution feedback, SOCIA-EVO achieves robust convergence, generating simulators that are statistically consistent with observational data. The code and data of SOCIA-EVO are available here: https://github.com/cruiseresearchgroup/SOCIA/tree/evo.

Abstract

Explore similar work

SOCIA-EVO: Automated Simulator Construction via Dual-Anchored Bi-Level Optimization

PhysEvo: Astra Can Act, Let It

EvoScientist: Towards Multi-Agent Evolving AI Scientists for End-to-End Scientific Discovery