MechBench: Can AI Scientific Agents Discover Mechanisms Beyond Phenomenal Laws?

Zihan Yu; Jiadong Zhang; Jialin Cheng; Jingtao Ding; Yong Li

cs.AISep 28, 2026

MechBench: Can AI Scientific Agents Discover Mechanisms Beyond Phenomenal Laws?

Authors: Zihan Yu, Jiadong Zhang, Jialin Cheng, Jingtao Ding, Yong Li

Organizations: Department of Electronic Engineering, BNRist, Tsinghua University, Beijing, China · Institute of Automation, Chinese Academy of Sciences, Beijing, China · Beijing Zhongguancun Academy, Beijing, China · Xi’an Jiaotong University, Xi’an, China · Department of Earth System Science, Tsinghua University, Beijing, China

Abstract

Scientific discovery requires not only recovering mathematical laws that describe observable behavior, but also identifying the mechanisms that generate them. Existing benchmarks for symbolic regression and scientific agents primarily evaluate phenomenal-law recovery, leaving mechanism discovery largely untested. We introduce MechBench, a benchmark that explicitly separates these two capabilities. Each task is defined by a mechanistic model, a structured set of scientifically meaningful relations whose joint consequences entail an observable phenomenal law, while agents receive only observational data and scientific context. We evaluate mechanism recovery through mechanism probes, which query internal scientific consequences that cannot be inferred from the phenomenal law alone. To reduce reliance on memorized textbook mechanisms, we construct unfamiliar variants through controlled, scientifically interpretable mutations of canonical mechanisms, and screen for mechanistic indistinguishability to exclude ambiguous instances admitting comparable competing mechanisms. Experiments across representative scientific agents reveal a substantial phenomenal--mechanism recovery gap: for Codex with GPT-5.6-sol, phenomenal-law accuracy reaches 35.00% on the Core-set while mechanism accuracy is only 13.75%, with mechanism recovery failing in 64.29% of cases where the phenomenal law is correctly recovered. The gap widens as mechanisms become increasingly mutated, and even providing the correct phenomenal law leaves mechanism recovery below 50%. These results reveal a substantial generalization gap in mechanistic reasoning and establish mechanism discovery as a distinct challenge beyond recovering observable scientific laws.

Figures & tables

Figure 1: From phenomenal-law recovery to mechanistic discovery.

Figure 2: Construction and evaluation of MechBench. (a) Canonical mechanisms are modified and composed to create unfamiliar variants. (b) Each mechanism induces a phenomenal law used to generate observations and held-out ID/OOD tests. (c) Candidate competing mechanisms are screened for mechanistic indistinguishability. (d) Submitted mechanisms are evaluated by both phenomenal-law recovery and mechanism probes.

Base Model	Agent	Core-set (n=80)			Full-set (n=512)
Base Model	Agent	SA( $\mathcal{P}$ )↑	SA( $\mathcal{M}$ )↑	$1-(\mathcal{M}\mid\mathcal{P})$	SA( $\mathcal{P}$ )↑	SA( $\mathcal{M}$ )↑	$1-(\mathcal{M}\mid\mathcal{P})$
Deepseek-v4-flash-0731	PySR+Direct-Ask	0.00%	1.25%	/	0.20%	0.20%	/
GPT-5.6-sol	Codex	35.00%	13.75%	64.29%	16.02%	7.81%	57.32%
GLM-5.3-flash	Codex	6.25%	2.92%	67.22%	4.30%	1.76%	63.64%
GLM-5.3-flash	Claude Code	8.75%	3.75%	57.14%	/	/	/
Deepseek-v4-flash-0731	Codex	7.92%	3.75%	49.07%	/	/	/

Table 1: Symbolic recovery results on the Core-set and Full-set.

\mathrm{SA}(P)

and

\mathrm{SA}(M)

denote phenomenal-law and mechanism recovery, respectively.

Base Model	Core-set (n=80) · SA(M)↑		Full-set (n=512) · SA(M)↑
Base Model	Standard discovery	Gold-P Direct-Ask	Standard discovery	Gold-P Direct-Ask
GPT-5.6-sol	13.75%	45.00%	7.81%	49.02%
GLM-5.3-flash	2.92%	6.25%	1.76%	3.91%
Deepseek-v4-flash-0731	3.75%	10.00%	/	4.10%

Table 2: Mechanism recovery under standard discovery and Gold-P Direct-Ask on the Core-set and Full-set.

Base model	Agent / condition	Physics		Chemistry		Biology		Materials
Base model	Agent / condition	$\mathcal{P}$	$\mathcal{M}$	$\mathcal{P}$	$\mathcal{M}$	$\mathcal{P}$	$\mathcal{M}$	$\mathcal{P}$	$\mathcal{M}$
No LLM	PySR	0.00	–	0.00	–	0.00	–	0.00	–
DeepSeek-v4-flash-0731	PySR + Direct-Ask	–	5.00	–	0.00	–	0.00	–	0.00
GPT-5.6-sol	Codex	40.00	20.00	40.00	25.00	40.00	10.00	20.00	0.00
GLM-5.3-flash	Codex (3-run avg.)	11.67	11.67	5.00	0.00	3.33	0.00	5.00	0.00
GLM-5.3-flash	Claude Code	25.00	15.00	5.00	0.00	0.00	0.00	5.00	0.00

Table 3: Complete Core-set symbolic-recovery results by scientific domain. A dash denotes a metric that is not defined for the corresponding control or baseline.

Appendix figures & tables15 assets

Supplementary material from the paper’s appendix.

Appendix

Domain	Task family	Description
Physics	Circular Orbit	Models the orbital period of a test body using Newtonian inverse-square attraction, circular radial balance, and the speed–period relation.
	Hall Response	Models the voltage measured across offset contacts using a classical carrier-conductivity tensor and an open transverse circuit.
	Inclined Rolling	Models the downslope acceleration of a rigid body from coupled translational and rotational balances under no-slip rolling.
	Terminal Settling	Models the terminal speed of a sphere from gravity, buoyancy, and linear Stokes drag at low Reynolds number.
Chemistry	Acid–Base Buffer Relaxation	Models transient pH response through acid–base buffer capacities and coupled proton-exchange population balances.
	Competitive Inhibition Pulse Reaction	Models product response through mass-action balances among free enzyme, substrate-bound enzyme, and inhibitor-bound enzyme.

Appendix

Table 4: The 16 task families and the scientific constructions used for their Original reference mechanisms.

Mutation type	Scientific interpretation	Representative changes in the benchmark
Constitutive or component response	Change how a component property depends on its local state or an observable control.	Mobile carrier trapping in Hall Response; temperature-difference-dependent conductivity in Composite Heat Transport; pressure-dependent migration barrier in Vacancy Diffusion.
Internal state or storage	Add a scientifically meaningful population, compartment, reservoir, or relaxation mode.	Product-bound enzyme and surface-product intermediates; inactive channel state; slow thermal and anelastic reservoirs.
Additional transport or reaction channel	Open a parallel pathway that contributes a new flux while retaining the original pathway.	Finite-range orbital attraction, conductive heat bypass, fast grain-boundary path, and alternate vacancy-migration channel.
Source, sink, or balance modification	Add a force, production, loss, or exchange term to a governing balance.	Resisting torque in rolling, external lift in settling, direct Lindemann product formation, and feed-dependent mortality.
Geometric or kinematic constraint	Change how geometric quantities or motions are related without directly editing the final observable law.	Non-Euclidean circumferential radius, controlled rolling slip, conducting-area ratio, and pressure-dependent jump distance.

Appendix

Table 5: Scientific interpretations of the mechanism mutations.

Family	Mutation
Circular Orbit	$\Delta_{1}$ : Make relative inertia depend on radius.
	$\Delta_{2}$ : Add a finite-range central attraction.
	$\Delta_{3}$ : Modify circumferential geometry together with its radial derivative.
	$\Delta_{4}$ : Add an external central trapping field.
	$\Delta_{5}$ : Store tangential momentum in a coupled internal reservoir.
Hall Response	$\Delta_{1}$ : Trap a density-dependent fraction of electrons.

Appendix

Table 6: The five base mutations for each physics task family.

Family	Mutation
Acid–Base Buffer Relaxation	$\Delta_{1}$ : Split buffer sites between two acid families.
	$\Delta_{2}$ : Introduce a slowly exchanging interior buffer region.
	$\Delta_{3}$ : Add a diffusively coupled solvent pocket.
	$\Delta_{4}$ : Add proton-catalyzed exchange without changing equilibrium capacity.
	$\Delta_{5}$ : Insert a proton-storing relay between solution and primary buffer.
Competitive Inhibition Pulse Reaction	$\Delta_{1}$ : Add substrate-assisted catalytic release.

Appendix

Table 7: The five base mutations for each chemistry task family.

Family	Mutation
Allosteric Regulation	$\Delta_{1}$ : Add an inactive conformation binding three regulators.
	$\Delta_{2}$ : Couple inactive-state substrate affinity to substrate exposure.
	$\Delta_{3}$ : Add another productive active-state complex.
	$\Delta_{4}$ : Partition regulator into a local compartment.
	$\Delta_{5}$ : Change inactive-conformation catalytic turnover.
Gene Repression	$\Delta_{1}$ : Add a looped state containing two repressors.

Appendix

Table 8: The five base mutations for each biology task family.

Family	Mutation
Composite Heat Transport	$\Delta_{1}$ : Make the first component’s conductivity depend on the imposed temperature difference.
	$\Delta_{2}$ : Add a composition-dependent thermal interface resistance.
	$\Delta_{3}$ : Introduce a parallel conductive bypass.
	$\Delta_{4}$ : Change the second component’s conducting area.
	$\Delta_{5}$ : Apply controlled volumetric heating within the second component.
Grain-Boundary Electrical Response	$\Delta_{1}$ : Add a thin space-charge region beside each boundary.

Appendix

Table 9: The five base mutations for each materials-science task family.

Split	Region for each $x_{j}$	Samples	Agent access
Training	$[l_{j},b_{j})$	1,000	Yes
ID test	$[l_{j},b_{j})$	1,000	No
OOD test	$[b_{j},u_{j})$	1,000	No

Appendix

Table 10: Default observation-generation configuration.

Setting	Base Model	Agent / Pipeline	Core	Full
Standard discovery	GPT-5.6-sol	Codex	✓	✓
Standard discovery	GLM-5.3-flash	Codex	✓	✓
Standard discovery	DeepSeek-v4-flash-0731	Codex	✓
Standard discovery	GLM-5.3-flash	Claude Code	✓
Standard discovery	DeepSeek-v4-flash-0731	Claude Code	✓
Standard discovery	DeepSeek-v4-flash-0731	DeepSeek Harness	✓

Appendix

Table 11: Model–agent configurations, baselines, and controls used in the experiments.

Base model	Agent / condition	Physics		Chemistry		Biology		Materials
Base model	Agent / condition	$\mathcal{P}$	$\mathcal{M}$	$\mathcal{P}$	$\mathcal{M}$	$\mathcal{P}$	$\mathcal{M}$	$\mathcal{P}$	$\mathcal{M}$
No LLM	PySR	0.78	–	0.00	–	0.00	–	0.00	–
DeepSeek-v4-flash-0731	PySR + Direct-Ask	0.78	0.78	0.00	0.00	0.00	0.00	0.00	0.00
GPT-5.6-sol	Codex	25.78	9.38	17.19	12.50	13.28	5.47	7.81	3.91
GLM-5.3-flash	Codex	10.16	3.12	0.78	0.00	2.34	2.34	3.91	1.56

Appendix

Table 12: Complete Full-set symbolic-recovery results by scientific domain.

Domain	Task family	Codex (GPT-5.6-sol)		Codex (GLM-5.3-flash)
Domain	Task family	$\mathcal{P}$	$\mathcal{M}$	$\mathcal{P}$	$\mathcal{M}$
Physics	Circular Orbit	21.88	15.63	9.38	3.12
	Hall Response	25.00	12.50	3.12	6.25
	Inclined Rolling	50.00	6.25	28.12	3.12
	Terminal Settling	6.25	3.12	0.00	0.00
Chemistry	Acid–Base Buffer Relaxation	3.12	3.12	0.00	0.00

Appendix

Table 13: Full-set symbolic-recovery results by task family for the two standard discovery configurations evaluated on all 512 instances. Each family contains 32 instances.

Base model	Agent	Physics		Chemistry		Biology		Materials
Base model	Agent	ID	OOD	ID	OOD	ID	OOD	ID	OOD
No LLM	PySR	35.00	–	0.00	–	20.00	–	0.00	–
GPT-5.6-sol	Codex	55.00	45.00	50.00	50.00	60.00	45.00	55.00	35.00
GLM-5.3-flash	Codex (3-run avg.)	56.67	40.00	18.33	8.33	53.33	10.00	38.33	25.00
GLM-5.3-flash	Claude Code	60.00	50.00	20.00	15.00	35.00	10.00	35.00	25.00
DeepSeek-v4-flash-0731	Codex (3-run avg.)	45.00	36.67	18.33	11.67	46.67	16.67	38.33	23.33

Appendix

Table 14: Core-set numerical accuracy by scientific domain. Each entry is the percentage of tasks whose maximum relative error over the indicated split is at most

0.1

. A dash denotes an unavailable OOD result.

Base model	Agent	Core-set variants		Full-set variants
Base model	Agent	$\mathrm{SA}(\mathcal{P})$	$\mathrm{SA}(\mathcal{M})$	$\mathrm{SA}(\mathcal{P})$	$\mathrm{SA}(\mathcal{M})$
No LLM	PySR	0.00	–	0.00	–
DeepSeek-v4-flash-0731	PySR + Direct-Ask	–	0.00	–	0.00
GPT-5.6-sol	Codex	30.56	9.72	14.11	6.05
GLM-5.3-flash	Codex (3-run avg. / 1 run)	2.78	0.46	3.23	0.81
GLM-5.3-flash	Claude Code	4.17	1.39	–	–

Appendix

Table 15: Variant-only symbolic-recovery results after excluding Original instances. Codex with GLM-5.3-flash and Codex with DeepSeek-v4-flash-0731 use three runs only on the Core-set. A dash denotes a metric or set that is not evaluated for the corresponding configuration.

Base model	Agent	$\mathrm{SA}(\mathcal{P})$ by mutation count						$\mathrm{SA}(\mathcal{M})$ by mutation count
Base model	Agent	0	1	2	3	4	5	0	1	2	3	4	5
No LLM	PySR	0.00	0.00	0.00	0.00	0.00	0.00	–	–	–	–	–	–
DeepSeek-v4-flash-0731	PySR + Direct-Ask	–	–	–	–	–	–	12.50	0.00	0.00	0.00	0.00	0.00
GPT-5.6-sol	Codex	75.00	75.00	31.25	25.00	6.25	0.00	50.00	37.50	6.25	0.00	0.00	0.00
GLM-5.3-flash	Codex (3-run avg.)	37.50	8.33	0.00	4.17	0.00	0.00	25.00	2.08	0.00	0.00	0.00	0.00
GLM-5.3-flash	Claude Code	50.00	12.50	6.25	0.00	0.00	0.00	25.00	0.00	6.25	0.00	0.00	0.00

Appendix

Table 16: Core-set symbolic recovery stratified by the number of applied mutations. Mutation count zero denotes Original instances.

Base model	Agent	$\mathrm{SA}(\mathcal{P})$ by mutation count						$\mathrm{SA}(\mathcal{M})$ by mutation count
Base model	Agent	0	1	2	3	4	5	0	1	2	3	4	5
No LLM	PySR	6.25	0.00	0.00	0.00	0.00	0.00	–	–	–	–	–	–
DeepSeek-v4-flash-0731	PySR + Direct-Ask	–	–	–	–	–	–	6.25	0.00	0.00	0.00	0.00	0.00
GPT-5.6-sol	Codex	75.00	40.00	15.00	8.13	1.25	0.00	62.50	25.00	5.63	0.63	0.00	0.00
GLM-5.3-flash	Codex	37.50	13.75	2.50	0.63	0.00	0.00	31.25	5.00	0.00	0.00	0.00	0.00

Appendix

Table 17: Full-set symbolic recovery stratified by the number of applied mutations. Mutation count zero denotes Original instances.

Factor	Comparison or grouping	Metric	Rates (%)	$p$
Base model	GPT-5.6-sol vs GLM-5.3-flash (Codex)	$\mathrm{SA}(\mathcal{P})$	35.00 / 6.25	$<.001$
		$\mathrm{SA}(\mathcal{M})$	13.75 / 2.92	.008
		$\mathrm{Acc}_{0.1}^{\mathrm{OOD}}$	43.75 / 20.83	$<.001$
	GPT-5.6-sol vs DeepSeek-v4-flash-0731 (Codex)	$\mathrm{SA}(\mathcal{P})$	35.00 / 7.92	$<.001$
		$\mathrm{SA}(\mathcal{M})$	13.75 / 3.75	.008
		$\mathrm{Acc}_{0.1}^{\mathrm{OOD}}$	43.75 / 22.08	$<.001$

Appendix

Table 18: Core-set statistical comparisons across base models, harnesses, and scientific domains. Rates follow the order of the groups named in each row. Codex rates for the two flash models are task-level averages over three runs except in the three-harness DeepSeek comparison, which uses the first Codex run alongside the single Claude Code and DeepSeek Harness runs.

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When Isaac Newton discovered the law of gravitation, he did so through an iterative process of analyzing observed data such as planetary patterns, finding the underlying mechanisms by describing patterns in mathematical equations, and refining his theory against the Moon's orbit, revealing the startling insight that the same force governs both falling apples and orbiting planets. Would it be possible for AI agents to make similar discoveries? To measure this ability, we introduce EurekaBench, a cross-domain benchmark that tests AI agents' ability to conduct long-horizon experiments and discover mechanisms that explain observations. We evaluate these mechanisms by the scientific insights that can be derived from them. EurekaBench contains an expert-verified set of 26 long-horizon tasks across neuroscience, computer science, chemistry, astrophysics, geophysics, and plasma physics, with a total of 306 scientific insights that the discovered mechanisms are expected to support. Our evaluation framework tests three axes of scientific discovery: agents' ability to follow known scientific constraints, the predictive accuracy of the discovered mechanisms, and whether these mechanisms yield scientific insights or inform future research. Our results show that current AI agents often overly fixate on predictive accuracy optimization, surpassing human scientists, while falling substantially short in deriving scientific insights.

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