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[2602.23201] Tell Me What To Learn: Generalizing Neural Memory to be Controllable in Natural Language
Summary
This paper presents a generalized neural memory system that allows for flexible updates based on natural language instructions, addressing the limitations of existing models in dynamic environments.
Why It Matters
As machine learning models are increasingly deployed in diverse and evolving contexts, the ability to control memory updates is crucial. This research offers a solution that enhances adaptability, making it relevant for applications in sectors like healthcare and customer service where precise memory management is essential.
Key Takeaways
- Proposes a generalized neural memory system for adaptable learning.
- Enables selective memory updates based on natural language instructions.
- Addresses the limitations of fixed-objective memory in dynamic environments.
- Supports diverse applications, including healthcare and customer service.
- Aims to reduce the costs and brittleness associated with continual fine-tuning.
Computer Science > Machine Learning arXiv:2602.23201 (cs) [Submitted on 26 Feb 2026] Title:Tell Me What To Learn: Generalizing Neural Memory to be Controllable in Natural Language Authors:Max S. Bennett, Thomas P. Zollo, Richard Zemel View a PDF of the paper titled Tell Me What To Learn: Generalizing Neural Memory to be Controllable in Natural Language, by Max S. Bennett and 2 other authors View PDF HTML (experimental) Abstract: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. Comments: Subjects: Machine Learning (cs.LG) Cite as: arXiv:2602.23201 [cs.LG] (or arXiv:2602.23201v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2602.23201 Fo...
