HypoForge: A Self-Improving Multi-Agent Framework for Automated Hypothesis Generation and Testing via Scientific Skill Learning

2026-08-26

AI Summary

HypoForge通过学习科学技能，采用多代理框架解决自动假设生成和测试问题，利用阶段特定的监督信号实现持续改进。

Abstract

Large language models (LLMs) have enabled AI scientist systems to automate scientific discovery, yet existing approaches most rely on static prompting or fixed workflows and fail to accumulate experience for continual improvement. We propose HypoForge, an experience-guided multi-agent framework that learns reusable scientific skills for automated hypothesis generation and hypothesis testing. HypoForge is built on the observation that these two stages involve different supervision signals. For hypothesis generation, where explicit feedback is unavailable, HypoForge adopts an adversarial generator--discriminator mechanism to improve reasoning through comparative critique. For hypothesis testing, where empirical feedback is available, HypoForge learns testing skills from execution outcomes and ground-truth results. By matching skill learning strategies with stage-specific supervision, HypoForge enables continual improvement without fine-tuning foundation models. Experiments on hypothesis generation and testing benchmarks show that HypoForge consistently outperforms existing AI scientist frameworks and skill-level variants. Further analysis demonstrates the effectiveness of the proposed stage-specific skill learning paradigms.

Problem

• Large language models
• Automated scientific discovery
• Continual improvement
• Hypothesis generation
• Hypothesis testing

Innovation

• Self-Improving Multi-Agent Framework
• Scientific Skill Learning
• Adversarial Generator--Discriminator Mechanism
• Stage-Specific Supervision