自主科学发现的新范式：DiscoPER通过迭代元反思实现开放式研究

Computer Science > Computer Vision and Pattern Recognition

arXiv:2607.01131v1 (cs) [Submitted on 1 Jul 2026]

Title:Autonomous Scientific Discovery via Iterative Meta-Reflection

Authors:Bingchen Zhao, Sara Beery, Oisin Mac Aodha View a PDF of the paper titled Autonomous Scientific Discovery via Iterative Meta-Reflection, by Bingchen Zhao and 2 other authors View PDF HTML (experimental)

Abstract:Autonomous scientific discovery systems offer the potential to accelerate research by automating the process of hypothesis generation and validation. However, current systems operate within constrained search spaces or require predefined research questions, limiting their capacity for true open-ended inquiry. Furthermore, while they generate hypotheses iteratively, they largely lack the ability to explicitly synthesize their own accumulated findings to uncover complex, interconnected phenomena. We introduce DiscoPER, an autonomous large language model-powered framework that conducts open-ended research by dynamically generating and executing code to explore datasets without pre-specified research objectives. To ensure rigorous scientific validity, every proposed discovery must pass statistical testing. To overcome the limitations of isolated search, our framework introduces a second-order reasoning mechanism that periodically analyzes its own accumulated discoveries. By treating prior discoveries as empirical data, DiscoPER identifies structural patterns, confounds, and epistemic gaps, actively redirecting hypothesis exploration toward uncharted regions of the search space. The search space is further expanded by incorporating tool use, enabling the system to explore hypotheses beyond structured metadata by seamlessly processing and extracting useful information from multimodal sources like images. Evaluated on iNatDisco, a new multimodal ecological knowledge benchmark with pattern-level ground truth obtained from peer-reviewed literature, DiscoPER recovers 8 of 9 known patterns with a 72.7% hypothesis support rate, outperforming both classical causal discovery and LLM-guided baselines. Ablations show that DiscoPER scales with more data, and confirms the benefits of second-order meta-reflection.

Subjects:	Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
Cite as:	arXiv:2607.01131 [cs.CV]
	(or arXiv:2607.01131v1 [cs.CV] for this version)
	https://doi.org/10.48550/arXiv.2607.01131 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Bingchen Zhao [view email]
[v1] Wed, 1 Jul 2026 16:16:07 UTC (2,521 KB)
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