{"slug": "training-chemical-plausibility-aware-large-language-models-for-single-step", "title": "Training Chemical Plausibility-Aware Large Language Models for Single-Step Retrosynthesis", "summary": "Researchers introduced Top-K prompting and the C3LM (Chemistry Constraint-Consistent Language Model), trained on the CREED-CCV-2+USPTO-XL dataset of ~45.6 million verified reactions, achieving state-of-the-art performance on the OOD URSA-expert-2026 benchmark for single-step retrosynthesis. The study, released on arXiv (2608.18940v1), shows that LLMs and conventional models explore complementary reaction spaces, motivating ensemble-based retrosynthesis systems.", "body_md": "arXiv:2608.18940v1 Announce Type: cross\nAbstract: Single-step retrosynthesis is a central component of computer-aided synthesis planning, yet its intrinsically one-to-many nature is poorly captured by single-answer evaluation and benchmarking protocols. To address this, we introduce Top-K prompting as a robust training and inference paradigm to better capture diverse, plausible reaction predictions. We compile CREED-CCV-2+USPTO-XL, an ultra-large-scale dataset of ~45.6 million verified reactions to train the C3LM (Chemistry Constraint-Consistent Language Model). By integrating fine-tuning with ChemCensor-based and novelty-oriented rewards, our model achieves state-of-the-art performance on the OOD URSA-expert-2026 benchmark. Further analysis of reaction uniqueness shows that LLMs and conventional models explore complementary reaction spaces, motivating ensemble-based retrosynthesis systems. Overall, our results establish Top-K, plausibility-aware training as a practical new direction for robust future LLM-based synthesis planning.", "url": "https://wpnews.pro/news/training-chemical-plausibility-aware-large-language-models-for-single-step", "canonical_source": "https://www.machinebrief.com/news/training-chemical-plausibility-aware-large-language-models-f-u61o", "published_at": "2026-08-20 04:00:00+00:00", "updated_at": "2026-08-20 06:14:22.239590+00:00", "lang": "en", "topics": ["artificial-intelligence", "large-language-models", "machine-learning"], "entities": ["arXiv", "C3LM", "CREED-CCV-2+USPTO-XL", "URSA-expert-2026"], "alternates": {"html": "https://wpnews.pro/news/training-chemical-plausibility-aware-large-language-models-for-single-step", "markdown": "https://wpnews.pro/news/training-chemical-plausibility-aware-large-language-models-for-single-step.md", "text": "https://wpnews.pro/news/training-chemical-plausibility-aware-large-language-models-for-single-step.txt", "jsonld": "https://wpnews.pro/news/training-chemical-plausibility-aware-large-language-models-for-single-step.jsonld"}}