Scientific research typically follows an iterative cycle where hypotheses are proposed, validated against experimental conclusions, and refined accordingly. While recent advances in large language models (LLMs) have enabled significant progress in automating individual stages of this process, existing systems are typically developed as standalone solutions, making it difficult to coordinate multiple research activities within a coherent research workflow. In this study, we present a modular framework for automated hypothesis validation and refinement in scientific research. Rather than introducing new task-specific models, the framework integrates established techniques, including natural language inference (NLI)-based hypothesis validation, attribution-guided hypothesis refinement, and retrieval-augmented generation (RAG)-based external evidence retrieval, into a unified and controllable workflow. We evaluate the proposed framework on scientific texts in the chemistry domain to assess its applicability in practical scientific research scenarios. Extensive experiments demonstrate the effectiveness of the proposed framework and suggest that it produces reliable intermediate signals that enhance transparency and traceability throughout hypothesis validation and refinement. Our work offers a modular solution for deploying LLM-based systems in scientific research workflows.
Chen et al. (2026) studied this question.