SeisAgentBench: Benchmarking and Reinforcement Learning for Seismic Data-Processing Agents

Seismic data processing is a realistic scientific workflow in which experts assemble and revise multi-stage pipelines over wavefield data. Unlike isolated prediction tasks, success depends on a sequence of data-dependent choices and on the scientific quality of the resulting artifact. Existing agent benchmarks seldom combine executable, multi-turn interaction with task-specific evaluation of scientific data products. We introduce **SeisAgentBench**, to our knowledge the first executable benchmark designed for language agents to construct and evaluate seismic artifacts through multi-step processing, together with **SeisAgent**, an LLM policy specialized for this setting through trajectory-level supervised learning and group-relative reinforcement-learning post-training. SeisAgentBench contains 95 tasks across 19 processing families and evaluates outputs using deterministic, family-specific seismic diagnostics, compatible reference agreement, artifact-preservation checks, and workflow cost. Together, SeisAgentBench and SeisAgent provide a controlled testbed for planning, numerical parameter selection, feedback-driven revision, and scientific artifact evaluation.