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    <abstract>Scientific idea generation requires more than fluent continuation from a problem statement: strong ideas often emerge from inspecting the same problem through multiple informative perspectives. We propose PRISM, an entropy-guided multi-view framework for scientific ideation. Given a research problem, PRISM constructs candidate inspection views, estimates their pairwise orthogonality through hypothesis-transition entropy, selects a maximally orthogonal subset, and conditions a backbone model on the selected views to synthesize a single proposal-style idea. We evaluate PRISM on 500 research problems across 23 scientific domains using three generation backbones: Llama-3.1-8B-Instruct, Qwen3-8B, and GPT-5.4. To assess generated ideas, we use two complementary automated judges—a Galactica-based impact discriminator trained from citation-derived scientific preferences and GPT-5.5 blind five-way novelty ranking—as well as a blind human study with five PhD-level evaluators. Across 7,500 generated ideas, PRISM achieves the best impact score and an 80.2% impact pairwise win rate, a 90.9% novelty pairwise win rate, and the highest mean score (3.91/5) in human evaluation. These results suggest that entropy-guided multi-view exploration provides an effective inference-time mechanism for producing more novel and impactful scientific ideas. Our code is available at https://github.com/jiajunyu1999/PRISM-idea-generation.</abstract>
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%0 Conference Proceedings
%T PRISM: Entropy-Guided Multi-View Exploration for Scientific Idea Generation
%A Yu, Jiajun
%A Mao, Weifan
%A Zeng, Kaipeng
%A Bian, Kejia
%A Wang, Zhuo
%A Li, Kun
%A Wang, Haishuai
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F yu-etal-2026-prism
%X Scientific idea generation requires more than fluent continuation from a problem statement: strong ideas often emerge from inspecting the same problem through multiple informative perspectives. We propose PRISM, an entropy-guided multi-view framework for scientific ideation. Given a research problem, PRISM constructs candidate inspection views, estimates their pairwise orthogonality through hypothesis-transition entropy, selects a maximally orthogonal subset, and conditions a backbone model on the selected views to synthesize a single proposal-style idea. We evaluate PRISM on 500 research problems across 23 scientific domains using three generation backbones: Llama-3.1-8B-Instruct, Qwen3-8B, and GPT-5.4. To assess generated ideas, we use two complementary automated judges—a Galactica-based impact discriminator trained from citation-derived scientific preferences and GPT-5.5 blind five-way novelty ranking—as well as a blind human study with five PhD-level evaluators. Across 7,500 generated ideas, PRISM achieves the best impact score and an 80.2% impact pairwise win rate, a 90.9% novelty pairwise win rate, and the highest mean score (3.91/5) in human evaluation. These results suggest that entropy-guided multi-view exploration provides an effective inference-time mechanism for producing more novel and impactful scientific ideas. Our code is available at https://github.com/jiajunyu1999/PRISM-idea-generation.
%U https://aclanthology.org/2026.emnlp-main.1180/
%P 27051-27072

Markdown (Informal)

[PRISM: Entropy-Guided Multi-View Exploration for Scientific Idea Generation](https://aclanthology.org/2026.emnlp-main.1180/) (Yu et al., EMNLP 2026)

• PRISM: Entropy-Guided Multi-View Exploration for Scientific Idea Generation (Yu et al., EMNLP 2026)

ACL

• Jiajun Yu, Weifan Mao, Kaipeng Zeng, Kejia Bian, Zhuo Wang, Kun Li, and Haishuai Wang. 2026. PRISM: Entropy-Guided Multi-View Exploration for Scientific Idea Generation. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 27051–27072, Budapest, Hungary. Association for Computational Linguistics.