LLM-SR: [ICLR 2025 Oral] This is the official repo for the paper LLM-SR on Scientific Equation Discovery and Symbolic

by deep-symbolic-mathematics · Agent Tool · ★ 276

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Security audit verdict: SAFE · quality 74/100

None of the 42 rules matched — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against 42 published rules built on the SlowMist agent-security taxonomy, refreshed every 8 hours.

The quality score combines nine heuristic checks of the repo and its README: completeness, clarity, specificity, examples, structure, agent readiness, step-by-step content, instruction quality and security notes. It rates how well the project explains itself to an agent, not the code.

ai4codeai4mathai4scienceequation-discoverylarge-language-modelsllm-agentprogram-synthesisscientific-discoverysymbolic-regression

Quick Facts

Stars276
Forks53
LanguagePython
CategoryAgent Tool
LicenseMIT
Quality Score74/100
Open Issues8
Last Updated2025-07-31
Created2024-04-19
Platformspython
Est. Tokens~598k

About LLM-SR

: Scientific Equation Discovery and Symbolic Regression via Programming with LLMs Official Implementation of paper LLM-SR: Scientific Equation Discovery via Programming with Large Language Models (ICLR 2025 Oral).

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How this security grade is produced

Grades come from a rule-based scan built on the SlowMist agent-security taxonomy: 42 published rules, covering credential harvesting, data exfiltration, curl | sh installers and more. It is a first-layer scan, not a manual audit — we say so rather than overstate it.

The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.

Sources & who's responsible:

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