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.

Document question answering plays a crucial role in enhancing employee productivity by providing quick and accurate access to information. Two primary approaches have been developed: retrieval-augmented generation (RAG), which reduces input tokens and inference costs, and long-context question answering (LC), which processes entire documents for higher accuracy. We introduce EXPLAIN (EXtracting, Pre-summarizing, Linking and enhAcINg RAG), a novel retrieval-augmented generation method that automatically extracts useful entities and generates summaries from documents. EXPLAIN improves accuracy by retrieving more informative entity summaries, achieving precision comparable to LC while maintaining low token consumption. Experimental results on internal dataset (ROUGE-L from 30.14% to 30.31%) and three public datasets (HotpotQA, 2WikiMQA, and Quality, average score from 62% to 64%) demonstrate the efficacy of EXPLAIN. Human evaluation in ant group production deployment indicates EXPLAIN surpasses baseline RAG in comprehensiveness.