Science One 框架：通过证据链实现可验证的自主研究框架

Rui Meng, Research Scientist, and Tomas Pfister, Director, Google Cloud Introducing the Science One Framework, an experimental research prototype designed to eliminate hallucinations by natively building verifiable evidence chains, and CoE Audit, an automated protocol to evaluate the integrity of AI-generated papers. Large language models (LLMs) are increasingly being deployed not just as coding assistants but as autonomous agents capable of conducting end-to-end scientific research workflows. Recent systems (e.g., Sakana’s AI-Scientist , AutoResearchClaw , DeepScientist , AI-Researcher ) can review literature, formulate hypotheses, execute experiments and write complete manuscripts that are comparable to human-authored papers. However, as the surface-level quality of these AI-generated manuscripts improves, a critical structural problem has emerged: verifiability. Because current autonomous research pipelines generate text iteratively, errors introduced at any stage are amplified. Some existing systems can generate non-existent citations , exhibit misalignments between the described methods and the actual code, and report experimental scores that are not fully reproducible from the provided code. In our paper , we tackle this problem by introducing Chain-of-Evidence (CoE), a new verifiability framework for AI-driven research. We instantiate CoE with the Science One Framework, an autonomous research prototype that natively builds and maintains evidence chains, and the CoE Audit, a set of automated evaluation metrics that measures the integrity of AI-generated papers against their underlying code and evidence. Our results show that baseline systems hallucinate up to 21% of their references and frequently misalign their code and text, whereas the Science One Framework achieves zero phantom references and fully verifiable scores while achieving state-of-the-art performance on frontier benchmarks like MLE-Bench and Parameter-Golf . The CoE is a conceptual framework that defines what makes a research artifact trustworthy, much as ACID defines what makes a database transaction reliable. Rather than prescribing how to build a research agent, the framework specifies the properties its outputs must have. It follows a single principle with two halves: every claim in a research artifact must carry a recorded evidence chain (completeness), and each chain must genuinely support the claim it is attached to (correctness). A claim may be a reference, a reported number, a method description, or a conclusion, that must link back to corresponding evidence, such as a peer-reviewed paper, an experimental log line, the code that actually ran, or the results table. A hallucinated reference points to a paper that does not exist. An unreproducible score does not reappear when the code is re-run. A misdescribed method claims one algorithm in the paper while the code implements another. Each is a claim whose chain back to its evidence is broken; the CoE Audit makes these breaks measurable. To demonstrate that verifiable AI research is possible without sacrificing problem-solving performance, we designed the Science One Framework . Unlike previous agents that generate a paper and try to link facts retroactively, the Science One Framework instantiates the CoE framework by construction through three main modules: Problem investigator (literature grounding): To prevent hallucinated references, the Science One Framework builds a citation graph via the Semantic Scholar API . It reads up to 100 full-text PDFs per topic to produce a structured research brief. Every reference in the final paper originates from this grounded API call entirely eliminating reliance on model memory. Discovery engine (parallel explore-exploit): The Science One Framework systematically explores and exploits ideas across multiple parallel branches. In each isolated cycle, a Solver agent implements a solution and a task-specific evaluator scores it. High-performing branches are