Existing LLM-based molecular optimization methods frame the task as molecule-to-molecule mapping, forcing models to implicitly learn structure-property relationships from paired SMILES alone. We argue this misses a key ingredient: task-level chemical knowledge that tells the model which structural motifs to prefer or avoid for a given property direction, before it ever looks at a specific molecule. We propose SkillMol, which constructs compact task skills – sets of recommended and discouraged structural motifs for each optimization objective – from domain knowledge, and pairs them with an atom-anchored action interface that decomposes editing into discrete, executable operations. Skills are used twice: as explicit training context so the model learns skill-conditioned edit actions, and as functional-group constraints during inference search. A deterministic RDKit executor applies the proposed graph edits. On the MoleculeSTM benchmark across 14 molecular editing tasks, SkillMol achieves 92.5% loose and 80.0% strict hit ratio on single-property tasks and 65.3% average across loose and strict multi-property columns, outperforming strong LLM baselines. Our code can be found at https://github.com/jiajunyu1999/SkillMol.

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.