arXiv AI
SHELDON: Reasoning through Dual Search over Hypothesis and Observation Spaces
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Evidence-Informed LLM Beliefs for Continual Scientific Discovery
arXiv:2606. 29182v1 Announce Type: new Abstract: Open-ended scientific discovery with large language models (LLMs) increasingly operates as a long-horizon loop of hypothesis search and verification, where a reward signal guides which hypotheses to test next.
arXiv AI
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StatefulDiscovery: Evidence-Calibrated Claim Formation in Open-Ended Scientific Discovery
arXiv:2606. 11851v1 Announce Type: new Abstract: Open-ended scientific discovery asks agents to move beyond executing analyses for predefined questions.
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DN-Hypo-Pipeline: An AI-Driven Workflow for Generating Hypotheses using Large Language Models and Scientific Explanations
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Autonomous Scientific Discovery via Iterative Meta-Reflection
arXiv:2607. 01131v1 Announce Type: cross Abstract: Autonomous scientific discovery systems offer the potential to accelerate research by automating the process of hypothesis generation and validation.
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Principle-Evolvable Scientific Discovery via Uncertainty Minimization
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Principia: Test-Time Hypotheses to Principle Construction for Scientific Discovery
Principia is an LLM-based framework that constructs and refines scientific principles during test-time discovery. It generates comparative causal hypotheses from related inputs and outcomes, synthesizes recurring relationships into inductive principles, and iteratively updates these principles as new observations are obtained. Across benchmarks in molecular optimization, protein engineering, gene perturbation, and molecular property prediction, Principia outperforms baselines that rely on internal knowledge, external sources, or experience summaries, producing plausible principles consistent with scientific literature.