Gravity-Bench-v1: A Benchmark on Gravitational Physics Discovery for Agents
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Abstract (arXiv)
Modern science emerged from reasoning over repeatedly-observed planetary motions. We present Gravity-Bench-v1, an environment-based benchmark that challenges AI agents on tasks that parallel this historical development. Gravity-Bench-v1 evaluates agents on the discovery of physics concealed within a dynamic environment, using rigorous gravitational dynamics simulations. Gravity-Bench includes out-of-distribution cases, i.e. with physics that deviates from the real world, to evaluate true scientific generalization capabilities. Agents must plan to collect data within an experimental budget and must perform a dynamic form of data analysis and reasoning to solve tasks efficiently. Our benchmark admits an open-ended space of solutions. Reference solutions for each task are provided to calibrate AI performance against human expertise. Technically at an upper-undergraduate level, our benchmark proves challenging to baseline AI agents. Gravity-Bench-v1 and planned extensions should help map out AI progress towards scientific discovery capabilities.
Key facts. Cited by 13 papers in the map (data through 14 Oct 2026). Builds on 14 of its 36 references that are in the map. First posted on arXiv 30 Jan 2025.
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14 of 36 references.
References · 14
- 2021Measuring Mathematical Problem Solving With the MATH Datasetcited by 5,371
- 2018Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challengecited by 4,886
- 2019HellaSwag: Can a Machine Really Finish Your Sentence?cited by 4,100
- 2023SWE-bench: Can Language Models Resolve Real-World GitHub Issues?cited by 3,373
- 2023GPQA: A Graduate-Level Google-Proof Q&A Benchmarkcited by 2,879
- 2024The AI Scientist: Towards Fully Automated Open-Ended Scientific Discoverycited by 1,071
- 2024Large Language Monkeys: Scaling Inference Compute with Repeated Samplingcited by 991
- 2024ResearchAgent: Iterative Research Idea Generation over Scientific Literature with Large Language Modelscited by 210
- 2024LLM and Simulation as Bilevel Optimizers: A New Paradigm to Advance Physical Scientific Discoverycited by 86
- 2025LLM4SR: A Survey on Large Language Models for Scientific Researchcited by 83
- 2022Rediscovering orbital mechanics with machine learningcited by 71
- 2020A Bayesian machine scientist to aid in the solution of challenging scientific problemscited by 61
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Citing papers per year: 2025: 4; 2026: 9.
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- 2025From Automation to Autonomy: A Survey on Large Language Models in Scientific Discoverycited by 93
- 2025AstroVisBench: A Code Benchmark for Scientific Computing and Visualization in Astronomycited by 13
- 2025ReplicationBench: Can AI Agents Replicate Astrophysics Research Papers?cited by 11
- 2026DiscoverPhysics: Benchmarking LLMs for Out-of-the-Box Scientific Thinkingcited by 10
- 2026Collider-Bench: Benchmarking AI Agents with Particle Physics Analysis Reproductioncited by 7
- 2025HypoSpace: A Diagnostic Benchmark for Set-Valued Hypothesis Generation under Underdetermination and Sublinear Coverage Boundscited by 6
- 2026Stargazer: A Scalable Model-Fitting Benchmark Environment for AI Agents under Astrophysical Constraintscited by 3
- 2026Can LLMs Discover Scientific Laws in Real and Parallel Worlds?cited by 2
- 2026Grounding LLMs in Scientific Discovery via Embodied Actionscited by 0
- 2026WhatWorkedBench: Benchmarking Experimental Understanding in AI Agentscited by 0
- 2026The Marathon of Scientific Reasoning: Robustness of Scientific Agents to Perturbations in Multi-Turn Interactionscited by 0
- 2026EurekaBench: Measuring Agentic Ability to Discover New Scientific Insightscited by 0
- 2026LLM-IDEA: Identifiability-Driven Experimental Agent for Autonomous Discovery of Mechanistic World Models8 Oct 2026
- 2026EurekaBench: Measuring Agentic Ability to Discover New Scientific Insights30 Sep 2026
- 2026The Marathon of Scientific Reasoning: Robustness of Scientific Agents to Perturbations in Multi-Turn Interactions28 Sep 2026
- 2026WhatWorkedBench: Benchmarking Experimental Understanding in AI Agents23 Sep 2026
- 2026Can LLMs Discover Scientific Laws in Real and Parallel Worlds?1 Sep 2026
- 2026DiscoverPhysics: Benchmarking LLMs for Out-of-the-Box Scientific Thinking25 May 2026
- 2026Collider-Bench: Benchmarking AI Agents with Particle Physics Analysis Reproduction13 May 2026
- 2026Stargazer: A Scalable Model-Fitting Benchmark Environment for AI Agents under Astrophysical Constraints17 Apr 2026
- 2026Grounding LLMs in Scientific Discovery via Embodied Actions24 Feb 2026
- 2025ReplicationBench: Can AI Agents Replicate Astrophysics Research Papers?28 Oct 2025
- 2025HypoSpace: A Diagnostic Benchmark for Set-Valued Hypothesis Generation under Underdetermination and Sublinear Coverage Bounds17 Oct 2025
- 2025AstroVisBench: A Code Benchmark for Scientific Computing and Visualization in Astronomy26 May 2025
- 2025ReplicationBench: Can AI Agents Replicate Astrophysics Research Papers?cited by 11
- 2026Collider-Bench: Benchmarking AI Agents with Particle Physics Analysis Reproductioncited by 7
- 2026WhatWorkedBench: Benchmarking Experimental Understanding in AI Agentscited by 0
- 2026EurekaBench: Measuring Agentic Ability to Discover New Scientific Insightscited by 0
- 2026EurekaBench: Measuring Agentic Ability to Discover New Scientific Insights30 Sep 2026
- 2026WhatWorkedBench: Benchmarking Experimental Understanding in AI Agents23 Sep 2026
- 2026Collider-Bench: Benchmarking AI Agents with Particle Physics Analysis Reproduction13 May 2026
- 2025ReplicationBench: Can AI Agents Replicate Astrophysics Research Papers?28 Oct 2025
- 2025From Automation to Autonomy: A Survey on Large Language Models in Scientific Discoverycited by 93
- 2025AstroVisBench: A Code Benchmark for Scientific Computing and Visualization in Astronomycited by 13
- 2025HypoSpace: A Diagnostic Benchmark for Set-Valued Hypothesis Generation under Underdetermination and Sublinear Coverage Boundscited by 6
- 2026Stargazer: A Scalable Model-Fitting Benchmark Environment for AI Agents under Astrophysical Constraintscited by 3
- 2026Can LLMs Discover Scientific Laws in Real and Parallel Worlds?cited by 2
- 2026Grounding LLMs in Scientific Discovery via Embodied Actionscited by 0
- 2026Can LLMs Discover Scientific Laws in Real and Parallel Worlds?1 Sep 2026
- 2026Stargazer: A Scalable Model-Fitting Benchmark Environment for AI Agents under Astrophysical Constraints17 Apr 2026
- 2026Grounding LLMs in Scientific Discovery via Embodied Actions24 Feb 2026
- 2025HypoSpace: A Diagnostic Benchmark for Set-Valued Hypothesis Generation under Underdetermination and Sublinear Coverage Bounds17 Oct 2025
- 2025AstroVisBench: A Code Benchmark for Scientific Computing and Visualization in Astronomy26 May 2025
- 2025From Automation to Autonomy: A Survey on Large Language Models in Scientific Discovery19 May 2025