AI in Science & Research
FrontierScience: Evaluating AI's Ability to Perform Expert-Level Scientific Tasks
The FrontierScience benchmark introduces a new standard for evaluating AI in complex scientific reasoning. It features hundreds of difficult, verifiable, and original questions in physics, chemistry, and biology, developed by international olympiad medalists and PhD scientists. Unlike previous benchmarks that rely on multiple-choice or published information, FrontierScience assesses true expert-level problem-solving and open-ended research tasks.
Key Impact & Performance Metrics
FrontierScience sets a new bar for AI evaluation in scientific reasoning. Our initial assessments highlight the significant progress of frontier models, yet reveal substantial opportunities for advancement in real-world research problem-solving.
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GPT-5.2 Olympiad Score
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GPT-5.2 Research Score
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Olympiad Gold Set Questions
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Research Gold Set Questions
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
FrontierScience: A Dual-Track Approach to AI Reasoning
FrontierScience is structured into two distinct tracks to comprehensively evaluate AI's scientific reasoning: Olympiad and Research. The Olympiad track consists of short-answer, highly constrained problems crafted by international olympiad medalists (totaling 108 medals across 42 experts) to assess precise problem-solving. The Research track comprises open-ended, PhD-level subproblems designed and verified by 45 PhD scientists (doctoral candidates, post-docs, professors) to mimic real-world scientific inquiry.
The Olympiad problems are evaluated based on a single numeric, algebraic expression, or fuzzy string match, ensuring clear verification. In contrast, Research problems utilize a granular, 10-point rubric-based architecture, allowing for nuanced assessment of intermediate reasoning steps, crucial for evaluating more complex, open-ended tasks.
77% / 25%
GPT-5.2 Scores on Olympiad / Research Sets
Rigorous Data Collection & Verification Pipeline
To ensure the high quality, originality, and difficulty of FrontierScience problems, a four-stage task development pipeline was implemented: Creation, Review, Resolution, and Revision. All problems are novel, designed to minimize contamination risks, and draw inspiration from existing scientific ideas but are re-contextualized with creative combinations to test reasoning beyond mere knowledge retrieval.
Olympiad problems are calibrated to be at least the difficulty level of international olympiad questions. Research problems are designed such that a 7-8 out of 10 points on the rubric is considered a successful solution, reflecting a PhD-level challenge taking 3-5 hours to complete. All questions and solutions undergo rigorous peer review by independent domain experts, with Research problems receiving at least two independent reviews and a meta-review to ensure robustness given their open-ended nature. This process filtered an initial pool of over 500 Olympiad and 200 Research questions down to a gold set of 100 and 60 respectively.
Enterprise Process Flow
Creation (Independently drafted by domain expert)
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Review (Independent expert validates accuracy, originality, difficulty)
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Resolution (Writer & reviewer reach consensus on feedback)
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Revision (Final version incorporating feedback)
Nuanced Grading and Benchmark Composition
The Olympiad set utilizes direct answer matching (numeric, algebraic, or fuzzy string) for efficient and objective verification. For the more expressive and open-ended Research tasks, an experimental rubric-based grading architecture is employed. Each Research problem includes a 10-point scoring rubric with independent, objectively assessable items that evaluate both final accuracy and correctness of intermediate reasoning steps, allowing for detailed failure analysis.
To scale evaluations without human expert graders, a GPT-5 judge model (operating at "high" reasoning effort) is used to assign scores based on the provided rubrics and attempted answers. The Olympiad set is weighted towards physics and chemistry due to the feasibility of verifiable expressions, while the Research gold set of 60 questions is equally split across physics, chemistry, and biology, reflecting diverse research specialties of the contributors.
Case Study: Research Problem & Rubric Example
Consider a sample Chemistry Research Subtask:
"Provide a comprehensive chemical analysis of this system, addressing: a) The strategic rationale for employing the two-stage precursor ROMP approach and the specific catalyst choice. b) The complete mechanistic basis for the conversion of the precursor polymer P to mPA under the notably mild TEA/oxidant conditions. c) The key structure-property relationships in mPA that determine its electronic characteristics (LUMO level, n-type behavior) and potential for electrical conductivity (backbone planarity). d) The overall significance of this approach for developing n-type conjugated polymers."
The evaluation rubric for such a task breaks down the solution into granular, verifiable components. For instance, points are awarded for:
- Conductivity: Role of Planarity (1.0 point): Explaining the importance of backbone planarity for high conductivity by linking it to efficient π-orbital overlap.
- Electronic Structure: Low LUMO Consequence (1.0 point): Linking low LUMO energy to facile n-doping and electrochemical stability.
- Mechanism: Redox Transformation & Oxidant Function (1.0 point): Accurately identifying the P → mPA conversion as a net two-electron, two-proton oxidation.
This allows for a precise diagnosis of AI capabilities, beyond just a simple pass/fail, identifying specific strengths and weaknesses within a complex problem.
Frontier Model Performance & Key Insights
Initial evaluations show GPT-5.2 as the top-performing model on FrontierScience, achieving 77% on the Olympiad set and 25% on the Research set. Gemini 3 Pro showed comparable performance on Olympiad (76%), and GPT-5 tied GPT-5.2 on Research (25%). Notably, models generally performed better on chemistry, followed by physics and biology for Olympiad problems. For Research, chemistry again led, followed by biology, then physics.
Increasing reasoning effort (more test-time tokens) significantly boosted GPT-5.2's performance, raising its Olympiad score from 67.5% to 77.1% and Research score from 18% to 25%. Analysis of transcripts revealed common areas of struggle for models, including reasoning/logic errors, failure to understand niche concepts, calculation errors, and factual inaccuracies, highlighting remaining headroom for progress, particularly in open-ended research-style tasks.
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Performance Boost for GPT-5.2 with High Reasoning Effort on Olympiad (67.5% → 77.1%)
Limitations & Future Directions for AI in Science
While FrontierScience marks a significant step, it has limitations. The benchmark primarily focuses on constrained problem-solving, evaluating reasoning within defined parameters rather than ideation or novel hypothesis generation, which are critical parts of scientific research. The rubric reliability, while rigorously designed and verified, remains inherently less objective than single-expression matching and relies on the capabilities of the model judge.
Furthermore, the current benchmark is text-only. Real-world scientific research often involves multi-modal interactions (e.g., images, experimental data, wet labs) that are not covered. Future work includes exploring human baselining for the highly specialized questions and continued development of robust, relevant benchmarks to accelerate scientific progress and leverage AI's beneficial impacts in science.
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Your AI Implementation Roadmap
Successfully integrating AI for expert-level scientific tasks requires a structured approach. Here’s a typical journey we guide our partners through.
Phase 01: Discovery & Assessment
Analyze current scientific workflows, identify high-leverage areas for AI integration, and define measurable objectives based on your research goals and the FrontierScience capabilities.
Phase 02: Pilot & Customization
Develop and test AI models on a subset of your scientific problems, leveraging FrontierScience's insights. Customize solutions to integrate with existing research infrastructure and data.
Phase 03: Scaled Deployment
Roll out AI tools across relevant research teams, provide training, and establish monitoring mechanisms to track performance and impact on scientific output and discovery timelines.
Phase 04: Continuous Optimization
Iteratively refine AI models and strategies based on ongoing results, new scientific data, and evolving research challenges to maximize long-term value and accelerate innovation.
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