AI在真实光学平台自主发现新物理机制并实验验证。
End-to-end autonomous scientific discovery on a real optical platform
- 基于大模型的智能体系统，实现从问题到实验的全流程自主科研。
- 首次实验证实相干序结构，并发现类Transformer的光双线性相互作用机制。
- 适合关注自主科研智能体、光学计算与人工智能驱动发现的研究者。
科学探索长期依赖人类主导，通过不断修正问题、方法和结论推动知识进步。尽管基于大语言模型（LLM）的智能体已开始超越辅助预设流程，但尚未在真实物理系统中实现端到端自主发现并提供实验证据。本文提出量子发现引擎（Qiushi Discovery Engine），一个基于LLM的智能体系统，可在真实光学平台上完成端到端自主科学发现。该系统融合非线性研究阶段、元追踪记忆与双层架构，支持跨数千次推理、测量与修正动作的长期稳定研究。它自主复现了非原平台的传输矩阵实验，并将抽象相干序理论转化为可观测现象，首次观测到此类相干序结构。更重要的是，在涉及1.459亿词元、3,242次LLM调用、1,242次工具调用、163份研究笔记和44个脚本的开放式研究中，系统提出了并实验验证了光双线性相互作用，其结构与Transformer注意力的核心操作类似。这一由AI发现的机制为高速、低功耗光学硬件实现成对计算提供了新路径。据我们所知，这是首个在真实系统中自主识别并实验验证非平凡、此前未报道的物理机制的案例，标志着研究级自主智能体的重要里程碑。
原文摘要 · Abstract (English)
Scientific research has long been human-led, driving new knowledge and transformative technologies through the continual revision of questions, methods and claims as evidence accumulates. Although large language model (LLM)-based agents are beginning to move beyond assisting predefined research workflows, none has yet demonstrated end-to-end autonomous discovery in a real physical system that produces a nontrivial result supported by experimental evidence. Here we introduce Qiushi Discovery Engine, an LLM-based agentic system for end-to-end autonomous scientific discovery on a real optical platform. Qiushi Engine combines nonlinear research phases, Meta-Trace memory and a dual-layer architecture to maintain adaptive and stable research trajectories across long-horizon investigations involving thousands of LLM-mediated reasoning, measurement and revision actions. It autonomously reproduces a published transmission-matrix experiment on a non-original platform and converts an abstract coherence-order theory into experimental observables, providing, to our knowledge, the first observation of this class of coherence-order structure. More importantly, in an open-ended study involving 145.9 million tokens, 3,242 LLM calls, 1,242 tool calls, 163 research notes and 44 scripts, Qiushi Engine proposes and experimentally validates optical bilinear interaction, a physical mechanism structurally analogous to a core operation in Transformer attention. This AI-discovered mechanism suggests a route towards high-speed, energy-efficient optical hardware for pairwise computation. To our knowledge, this is the first demonstration of an AI agentic system autonomously identifying and experimentally validating a nontrivial, previously unreported physical mechanism, marking a milestone for research-level autonomous agents.
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