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arXiv · 2606.21658

Towards LLM-Powered Automation of a Dark Matter Constraint Repository

Lanqing Yuan·Karthik Ramanathan
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Abstract

Dark matter constraint repositories are critical community infrastructure, yet the most widely-used are volunteer-maintained, a sustainability risk as results accelerate. Limits are typically published as figures rather than machine-readable records, compounding the burden. We present a large language model (LLM) pipeline that monitors arXiv, extracts limit curves, integrates them as code, and opens pull requests for human review. On a 329-paper benchmark of measured limits graded against the upstream-curated repository, it identifies the coupling type for 98.1% of gradable papers. The 81% yielding a comparable extraction reach a median residual of 0.17 dex from a single model read, and 51% of all papers land within a factor of two of the curator's curve. The LLM is confined to perception; arithmetic, unit conversion, and the guards that reject mis-extractions are deterministic, testable code. Deployed, the pipeline has generated limit proposals, but none have merged: governance of AI-generated scientific data is itself unsolved.

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Lanqing Yuan, Karthik Ramanathan. 2026-09-09. Towards LLM-Powered Automation of a Dark Matter Constraint Repository. https://arxiv.org/abs/2606.21658

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