天体粒子物理研究者终于有了一个能自动完成模型构建、约束审计和引力波谱拟合的 AI 系统，DarkAgents 直接解决了该领域计算管线复杂、假设审计繁琐的痛点，做相关理论研究的团队值得一试。
DarkAgents 是一个利用大语言模型推理和代码生成能力，结合确定性人类编写代码的多智能体系统，用于构建理论天体粒子物理研究的自动化管线。该系统针对该领域的特定挑战，如模型构建、复杂管道计算、多重约束和假设审计。它支持多种命令行工具，包括 Mistral、Anthropic、OpenAI 以及通过 Ollama 运行的本地模型。首次应用是研究宇宙学一级相变，从经典尺度不变粒子物理模型开始，最终拟合 NANOGrav 纳赫兹引力波谱。测试运行发现了文献中一些拟合的不一致性，并基于耗散体流引力波模板产生了新的拟合结果。代码已在 GitHub 上开源。
DarkAgents
We present DarkAgents: a multi-agent system that leverages the reasoning and code-generation capabilities of large language models (LLMs), together with deterministic tested human-written code, to build orchestrated pipelines for theoretical astroparticle physics research. While related approaches have been proposed in collider physics and cosmology, DarkAgents targets the specific challenges of this domain, such as model building, complex pipeline computations, multiple constraints and assumption auditing. The framework can be powered by different agentic command-line tools, including Mistral's, Anthropic's, OpenAI's and local LLMs via Ollama. As first implementation, we apply DarkAgents to the study of cosmological first order transitions, starting from a classically scale-invariant particle-physics model and ending with the fit to the NANOGrav nanohertz gravitational-waves spectrum. DarkAgent-PT provides as output i) the best-fit values of model parameters, ii) their existing experimental and observational constraints, iii) an audit report of the assumptions and priors entering both i) and ii), of particular relevance for astroparticle physics. Our test runs identify inconsistencies in some fits in the literature and produce novel ones based on the dissipative bulk-flow GW template. The code is publicly available at https://github.com/PhysicsZandi/DarkAgents.