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.