Evaluating AI's ability to perform scientific research tasks
Date: December 16, 2025
Reasoning sits at the center of scientific work. The key isn't recall-it's forming hypotheses, testing them, and stitching ideas together across disciplines.
Over the last year, frontier models have hit serious milestones, including top-tier results on elite competitions like the International Math Olympiad and the Olympiad in Informatics. In practice, researchers report faster literature review, cross-lingual search, and help with complex proofs-work that used to take days now taking hours.
FrontierScience: a benchmark for expert-level scientific reasoning
Most science benchmarks are multiple-choice, saturated, or not truly research-focused. FrontierScience fills that gap. It measures expert-level performance across physics, chemistry, and biology with original, difficult, and meaningful questions.
- Olympiad track: 100 short-answer problems designed by international olympiad medalists. These are at least as hard as top competition questions.
- Research track: 60 open-ended, multi-step subtasks written by PhD-level scientists. Each is graded on a 10-point rubric to assess both reasoning and outcomes.
The full evaluation spans 700+ questions, with a 160-question gold set. Tasks are expert-written and verified, and creation included selection against strong internal models to maintain difficulty. A four-stage pipeline-Creation, Review, Resolution, Revision-keeps quality high while holding out non-gold tasks to track contamination.
What these results say about today's models
Initial evaluations show strong progress on constrained expert problems and more modest results on open-ended research tasks. Reported numbers: GPT-5.2 leads FrontierScience-Olympiad at 77% and FrontierScience-Research at 25%, while Gemini 3 Pro posts 76% on Olympiad. Longer thinking time improves accuracy across models.
Failure modes are familiar: reasoning slips, calculation mistakes, missing niche concepts, and factual errors. The takeaway is practical-models already help with structured reasoning and workflow acceleration, while scientists still frame problems and validate outputs.
How FrontierScience is graded
- Olympiad: Short answers graded by exact or fuzzy matches. Verification is straightforward but less expressive.
- Research: Rubric-based grading (10 points) that scores intermediate steps and final conclusions. A score of 7/10 or higher counts as correct.
Responses are graded by a model-based grader using question-specific rubrics, with calibration steps to align difficulty and correctness. This trades perfect objectivity for scalability while enabling finer-grained failure analysis.
What FrontierScience measures (and what it doesn't)
- Measures: expert-level reasoning on hard, original tasks; performance on constrained olympiad-style questions; step-by-step scientific reasoning with rubric scoring.
- Doesn't measure: generating new hypotheses from scratch, multimodal reasoning (e.g., video, instrument data), or running and interpreting physical experiments.
Think of FrontierScience as a north star for expert reasoning-not a full proxy for lab work or discovery. The metric that truly matters is novel science AI helps create; the benchmark sits upstream of that.
Examples of the task design
- Olympiad: Theoretical problems across chemistry, physics, and biology that demand multi-step reasoning and precise answers.
- Research: Realistic subtasks, such as analyzing how meso-nitrogen modifications in nickel(II) phthalocyanines shift π-electron counts, aromaticity, spectra, redox behavior, and reactivity-and explaining the synthetic methodology that enables those changes.
How to use these systems in a real research workflow
- Accelerate literature review: Cross-disciplinary and cross-lingual search; extract trends, contradictions, and canonical references.
- Draft and debug reasoning: Work through derivations, proofs, and edge cases. Always spot-check math and assumptions.
- Prototype experiment plans: Generate candidate methodologies, variables, and controls-then refine with domain constraints.
- Comparative synthesis: Summarize competing models, map causal graphs, and identify missing data needed to decide between hypotheses.
- Error-aware workflow: Expect hallucinations and arithmetic slips. Use ensemble prompts, citation checks, and unit tests on intermediate steps.
- Longer thinking helps: If the option exists, allow more reasoning time for hard tasks.
Limitations and what's next
Rubric grading adds nuance but can be less objective than a single final answer. Constrained prompts don't capture messy lab realities, instrument quirks, or the creative process of forming entirely new hypotheses.
Progress will likely come from two fronts: better general reasoning and dedicated tooling for scientific work. Expect future versions of FrontierScience to expand domains, incorporate more realistic tasks, and pair with real-world evaluations that test actual impact on research.
Why this matters for you
If your work involves heavy reasoning, you can already offload structured tasks and accelerate review cycles. Keep humans in the loop for framing, validation, and decisions-use models to reduce iteration time and widen your search space.
The direction is clear: stronger expert reasoning on hard benchmarks with headroom on open-ended science. Use that signal to update your workflow, not your standards.
Resources
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