gwBenchmarks: Stress-Testing LLM Agents on High-Precision Gravitational Wave Astronomy
gr-qc, astro-ph.HE, astro-ph.IM, cs.AI
Submitted: 2026-05-11
Updated: 2026-09-20
Comments: 26 pages, 4 figures
Code: https://github.com/tousifislam/gwBenchmarks
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Terminology
Sources
- Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models
- GAIA: a benchmark for General AI Assistants
- ScienceAgentBench: Toward Rigorous Assessment of Language Agents for Data-Driven Scientific Discovery
- Terminal-Bench: Benchmarking Agents on Hard, Realistic Tasks in Command Line Interfaces
- Program Synthesis with Large Language Models
- LiveCodeBench: Holistic and Contamination Free Evaluation of Large Language Models for Code
- Evaluating Large Language Models Trained on Code
- Training Verifiers to Solve Math Word Problems
- CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generation
- Reflexion: Language Agents with Verbal Reinforcement Learning
- The Denario project: Deep knowledge AI agents for scientific discovery
- CosmoBench: A Multiscale, Multiview, Multitask Cosmology Benchmark for Geometric Deep Learning
- Accurate models for recoil velocity distribution in black hole mergers with comparable to extreme mass-ratios and their astrophysical implications
- Kick matters: The impact of a new recoil model on the retention of hierarchical black-hole remnants in globular clusters
- New approach to template banks of gravitational waves with higher harmonics: Reducing matched-filtering cost by over an order of magnitude
- Generating optimal Gravitational-Wave template banks with metric-preserving autoencoders
- New black hole mergers in the LIGO-Virgo O3 data from a gravitational wave search including higher-order harmonics
- Searching for precessing binary systems with mode-by-mode filtering and marginalization
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