Memory System for Long-Context LLMs
This project develops a novel flexible memory system that enables AI assistants to maintain truly coherent, long-term conversations by preserving raw conversational data and supporting multi-resolution retrieval without information loss.
Project details
Description:
Current long-context LLMs suffer from context rot, where reasoning quality degrades as context windows expand. Existing memory-based solutions are inherently lossy, discarding portions of the original dialogue. To address these challenges, the system employs two foundational design principles. First, an indexed architecture ensures original conversation content remains accessible on demand rather than being compressed or abstracted away. Second, adaptive retrieval mechanisms enable access to information at varying levels of abstraction and granularity depending on task requirements. The integration of structured indexing with dynamic retrieval allows LLMs to perform reliable reasoning over extended conversational histories while maintaining complete access to the original interaction record, eliminating the information-fidelity trade offs inherent in current approaches.
Who’s involved?
Mingtian Zhang, University College London
Pasquale Minervini, University of Edinburgh
Expertise sought
This project is seeking collaborators
Collaborators are expected to contribute to:
Conceptual development of the memory system design
Guidance on experimental design and evaluation methodology
Analysis of model behaviour under long-context settings Interpretation of results and co-authoring research outputs
Relevant expertise includes large language models, retrieval-augmented generation, reasoning evaluation, and experimental ML research.
Get in touch:
If you can offer the expertise needed for this project and would like to collaborate, please email us
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