MEMIG 提出自进化记忆框架优化抗体设计
MEMIG: Self-evolving Memory Construction for Antibody Optimization
让 AI 智能体做科学实验时不再靠写日记记经验，改成结构化查表，抗体优化任务里多数场景都涨分了。
arXiv 论文 MEMIG 针对自进化 LLM 智能体在科学优化任务中的记忆问题提出新方案。现有方法用自由文本存储经验，而 MEMIG 改用可索引、量化、结构化的记忆表示，通过领域标识符实现精确检索，并用属性 oracle 累积定量效应。该框架还能连接同时施加的干预并度量其上位性效应。在 in silico 抗体优化任务中，MEMIG 在多数设置下取得性能提升。
MEMIG: Self-evolving Memory Construction for Antibody Optimization
Memory has become central to self-evolving LLM agents, which store and retrieve experience from past rollouts to guide future decisions. Existing systems represent this experience as free-form natural language and maintain it by rewriting, which fits tasks whose experience is narratable. However, in scientific discovery and optimization, experience is often a quantity measured by an oracle under a specific context. Rewriting it as text leaves no index for where a record should be, replaces a measurement with a description, and cannot express how interventions interact. To address this, we argue that agent memory for biomolecule optimization should instead be indexable, quantitative, and structured, and we introduce MEMIG. This memory construction framework realizes these three properties. In detail, MEMIG addresses each insight with a domain-grounded identifier and accumulates the quantitative effect measured by a property oracle, so that updates remain local and retrieval becomes an exact lookup. Next, MEMIG connects insights whose interventions are applied together and measures their epistasis by decomposing each evaluated set. Our results show that MEMIG improves performance in most settings across in silico antibody optimization tasks.