[2604.09308] Constraint-Aware Corrective Memory for Language-Based Drug Discovery Agents

Computer Science > Artificial Intelligence arXiv:2604.09308 (cs) [Submitted on 10 Apr 2026] Title:Constraint-Aware Corrective Memory for Language-Based Drug Discovery Agents Authors:Maochen Sun, Youzhi Zhang, Gaofeng Meng View a PDF of the paper titled Constraint-Aware Corrective Memory for Language-Based Drug Discovery Agents, by Maochen Sun and 2 other authors View PDF HTML (experimental) Abstract:Large language models are making autonomous drug discovery agents increasingly feasible, but reliable success in this setting is not determined by any single action or molecule. It is determined by whether the final returned set jointly satisfies protocol-level requirements such as set size, diversity, binding quality, and developability. This creates a fundamental control problem: the agent plans step by step, while task validity is decided at the level of the whole candidate set. Existing language-based drug discovery systems therefore tend to rely on long raw history and under-specified self-reflection, making failure localization imprecise and planner-facing agent states increasingly noisy. We present CACM (Constraint-Aware Corrective Memory), a language-based drug discovery framework built around precise set-level diagnosis and a concise memory write-back mechanism. CACM introduces protocol auditing and a grounded diagnostician, which jointly analyze multimodal evidence spanning task requirements, pocket context, and candidate-set evidence to localize protocol violations, ...