← 研踪 PaperTrailarXiv:2602.23201 · v3
Tell Me What To Learn: Generalizing Neural Memory to be Controllable in Natural Language
首次提交 2026-02-27 00:50
版本更新 2026-11-06 01:12
雷达观测 2026-11-06 12:09
摘要
Modern machine learning models are deployed in diverse, non-stationary environments where they must continually adapt to new tasks and evolving knowledge. Continual fine-tuning and in-context learning are costly and brittle, whereas neural memory methods promise lightweight updates with minimal forgetting. However, existing neural memory models typically assume a single fixed objective and homogeneous information streams, leaving users with no control over what the model remembers or ignores over time. To address this challenge, we propose a generalized neural memory system that performs flexible updates based on learning instructions specified in natural language. Our approach enables adaptive agents to learn selectively from heterogeneous information sources, supporting settings, such as healthcare and customer service, where fixed-objective memory updates are insufficient.
作者备注：47 Pages, Code at https://github.com/maxbennett/Generalized-Neural-Memory
观测到的版本
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v3 · 2026-11-06 01:12 · 最新观测版本
Tell Me What To Learn: Generalizing Neural Memory to be Controllable in Natural Language
Modern machine learning models are deployed in diverse, non-stationary environments where they must continually adapt to new tasks and evolving knowledge. Continual fine-tuning and in-context learning are costly and brittle, whereas neural memory methods promise lightweight updates with minimal forgetting. However, existing neural memory models typically assume a single fixed objective and homogeneous information streams, leaving users with no control over what the model remembers or ignores over time. To address this challenge, we propose a generalized neural memory system that performs flexible updates based on learning instructions specified in natural language. Our approach enables adaptive agents to learn selectively from heterogeneous information sources, supporting settings, such as healthcare and customer service, where fixed-objective memory updates are insufficient.
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