Why This Matters Now

Scientific reasoning is the last refuge of human intellectual pride. We love to believe that even if LLMs can write poems, debug JavaScript, and imitate Dickens on command, surely they struggle with physics. After all, physics is unforgiving: units must match, formulas must cohere, numbers must compute.

SymPyBench—a new benchmark from Meta’s Reality Lab—confirms that intuition… but also complicates it. Unlike conventional benchmarks that test whether a model can guess the right answer from four choices, SymPyBench tests whether the model can think, consistently and across variations. And it does so using something most benchmarks avoid: executable ground-truth Python code.

The result is a stress test for AI reliability in scientific domains—a domain businesses increasingly depend on, from R&D automation to engineering design review.

Background: The Benchmark Landscape Before SymPyBench

Most scientific QA benchmarks suffer from the same flaw: they test recognition, not reasoning. Datasets like ScienceQA, SciEval, JEEBench, and MMLU’s physics sections—summarized in Table 8 of the paper—are structurally simple, often multiple-choice, and rarely require multi-step derivations.

That simplicity creates misleading confidence. If a model chooses correctly from four symbolic expressions, was it genuinely reasoning—or playing pattern-matching roulette?

SymPyBench answers that critique decisively:

• 15,045 university-level physics problems, all parameterized.
• Step-by-step reasoning templates.
• Executable Python code as the final judge.
• Systematic variation: linguistic, numerical, and format.

In other words: same physics, different phrasings, different numbers, stable truth conditions. If an LLM truly understands the concept, performance should remain stable.

Spoiler: it doesn’t.

What SymPyBench Actually Does

The pipeline (illustrated in Figure 2 of the paper) is an elegant machinery for generating an effectively infinite set of scientific reasoning problems:

1. Extract a raw physics problem from open-source textbooks.
2. Convert to structured JSON: variables, constants, reasoning steps.
3. Generate a symbolic template with placeholders.
4. Produce three natural-language variants.
5. Generate Python functions that compute ground truth.
6. Validate the generated code against the structured answers.

Then each problem is emitted in three formats:

• Free-form (open-ended numeric answers)
• MC-Symbolic (multiple-choice algebraic expressions)
• MC-Numerical (multiple-choice numeric substitutions)

The benchmark’s originality is not the physics. It’s the dynamism. Every question is parameterized, meaning new numerical instances can be generated endlessly. This creates a controlled playground for testing consistency.

Findings: Beyond Accuracy, Toward Stability

The headline results (Table 2) reveal that even frontier models struggle with stability.

1. Accuracy Isn’t the Problem. Consistency Is.

Many models score around or above 60% Exact Match Accuracy. Respectable.

But the Consistency Score — the fraction of problem groups where the model answers all variants correctly — is shockingly low:

A model that solves a problem once may fail the same problem when the numbers change slightly.

For businesses: this is the equivalent of a financial model that works on Mondays but not on Wednesdays.

2. Multiple-choice hides weaknesses. Free-form reveals them.

The gap between MC-Symbolic and free-form is enormous:

MC-Symbolic provides so much scaffolding that even weaker models look competent. Free-form requires:

• maintaining units,
• consistent symbolic manipulation,
• multi-step arithmetic,
• producing cleanly formatted answers.

It’s everything real-world industrial workflows require.

3. LLMs hallucinate physics when inputs are incomplete.

A striking example (Section: Hallucination Case Study):

• The model is asked to compute diffusion time without being given the diffusion coefficient.
• Instead of asking for clarification, it invents an imaginary “image,” pulls a fake number from thin air, and performs a proportionality calculation.

This is not merely incorrect—it is fabricated physical reasoning.

In compliance-heavy engineering or scientific R&D, such behavior is catastrophic.

4. Numerical instability is a systemic weakness.

Models often:

• use the correct formulas,
• articulate the correct reasoning,
• and yet produce wildly incorrect final numbers.

Example (page 13): a Coulomb’s law calculation off by three orders of magnitude.

This indicates a failure mode between reasoning and arithmetic—a generation-understanding gap.

Implications for Industry

If your organization uses LLMs for technical or scientific tasks, SymPyBench offers three sobering lessons.

1. Do not trust LLM reasoning consistency without stress testing.

Your model may:

• solve a physics problem once,
• fail when the parameters shift 20%, or
• hallucinate constants when they’re missing.

Any AI deployed in R&D, simulation, engineering review, or automated QA should undergo variant testing, not single-instance testing.

2. Multiple-choice benchmarks overstate model capability.

Real workflows are not multiple-choice. They require generation. They require formatting. They require multi-step reasoning.

If your model selection process relies on MC benchmarks, you’re buying a sports car based solely on its paint job.

3. For AI governance, consistency metrics should become standard.

SymPyBench’s three core robustness metrics—Consistency, Confusion, and Complete Failure—should be adopted widely.

They answer essential operational questions:

• Does the model behave the same across similar inputs?
• Does it flip a coin under uncertainty?
• Does it catastrophically fail in entire domains?

For safety-critical workflows, these matter more than raw accuracy.

Conclusion: The Next Era of AI Evaluation

SymPyBench signals a shift from evaluating how models answer to evaluating how models reason. Its dynamic, code-validated structure does something few benchmarks dare: it treats the model like a scientific collaborator, not a quiz participant.

For enterprises integrating LLMs into technical or scientific processes, this is a gift. It provides the blueprint for auditing model reliability in environments where correctness is non-negotiable.

Physics doesn’t tolerate sloppy reasoning. Neither should your AI systems.

Cognaptus: Automate the Present, Incubate the Future.