FlowPIE: Test-Time Scientific Idea Evolution with Flow-Guided Literature Exploration
Scientific idea generation (SIG) is critical to AI-driven autonomous research, yet existing approaches are often constrained by a static retrieval-then-generation paradigm, leading to homogeneous and insufficiently divergent ideas. In this work, we propose FlowPIE, a tightly coupled retrieval-generation framework that treats literature exploration and idea generation as a co-evolving process. FlowPIE expands literature trajectories via a flow-guided Monte Carlo Tree Search (MCTS) inspired by GFlowNets, using the quality of current ideas assessed by an LLM-based generative reward model (GRM) as a supervised signal to guide adaptive retrieval and construct a diverse, high-quality initial population. Based on this population, FlowPIE models idea generation as a test-time idea evolution process, applying selection, crossover, and mutation with the isolation island paradigm and GRM-based fitness computation to incorporate cross-domain knowledge. It effectively mitigates the information cocoons arising from over-reliance on parametric knowledge and static literature. Extensive evaluations demonstrate that FlowPIE consistently produces ideas with higher novelty, feasibility and diversity compared to strong LLM-based and agent-based frameworks, while enabling reward scaling during test time.
69cca3ed3f84a0971be66bff
Qiyao Wang
claimed_verified
2026-04-01T08:34:23.744Z
64560618bfdf9c63ce2d658a
https://cdn-avatars.huggingface.co/v1/production/uploads/64560618bfdf9c63ce2d658a/GVBWU4yNzRsjdyzKT3z3B.jpeg
Mathsion Wong
QiYao-Wang
user
QiYao-Wang
69cca3ed3f84a0971be66c00
Hongbo Wang
69cca3ed3f84a0971be66c01
Longze Chen
69cca3ed3f84a0971be66c02
Zhihao Yang
69cca3ed3f84a0971be66c03
Guhong Chen
69cca3ed3f84a0971be66c04
Hamid Alinejad-Rokny
69cca3ed3f84a0971be66c05
Hui Li
69cca3ed3f84a0971be66c06
Yuan Lin
69cca3ed3f84a0971be66c07
Min Yang
FlowPIE: Test-Time Scientific Idea Evolution with Flow-Guided Literature Exploration
Scientific idea generation (SIG) is critical to AI-driven autonomous research, yet existing approaches are often constrained by a static retrieval-then-generation paradigm, leading to homogeneous and insufficiently divergent ideas. In this work, we propose FlowPIE, a tightly coupled retrieval-generation framework that treats literature exploration and idea generation as a co-evolving process. FlowPIE expands literature trajectories via a flow-guided Monte Carlo Tree Search (MCTS) inspired by GFlowNets, using the quality of current ideas assessed by an LLM-based generative reward model (GRM) as a supervised signal to guide adaptive retrieval and construct a diverse, high-quality initial population. Based on this population, FlowPIE models idea generation as a test-time idea evolution process, applying selection, crossover, and mutation with the isolation island paradigm and GRM-based fitness computation to incorporate cross-domain knowledge. It effectively mitigates the information cocoons arising from over-reliance on parametric knowledge and static literature. Extensive evaluations demonstrate that FlowPIE consistently produces ideas with higher novelty, feasibility and diversity compared to strong LLM-based and agent-based frameworks, while enabling reward scaling during test time.
FlowPIE presents a novel retrieval-generation framework for scientific idea generation that uses flow-guided Monte Carlo Tree Search and genetic algorithm principles to produce diverse, high-quality ideas through iterative evolution and cross-domain knowledge integration.
flow-guided Monte Carlo Tree Search GFlowNets generative reward model isolation island paradigm test-time idea evolution selection crossover mutation