Learning What to Remember: A Cognitively Grounded Multi-Factor Value Model for Agentic Memory
arXiv:2606.12945v1 Announce Type: new Abstract: Long-running LLM agents accumulate interaction histories far larger than any context window, forcing a standing decision: what to encode deeply, what to forget, and what to retrieve under a fixed memory budget. Production systems answer with semantic similarity or recency -- both mis-specified for the forgetting decision, which is made at consolidation time before the future query is known. We propose a multi-factor memory value function V(m)=\sum_i w_i f_i(m) over seven interpretable factors (emotional intensity, goal relevance, value alignment,
The proliferation of long-running LLM agents necessitates a more sophisticated memory management strategy to overcome context window limitations and enable truly autonomous operation.
This research advances the fundamental capabilities of AI agents, moving beyond simplistic memory models to incorporate cognitively-grounded value functions for more effective long-term operation.
AI agents will be able to manage vast interaction histories more intelligently, deciding what to remember, forget, and retrieve based on a multi-factor value system rather than mere recency or semantic similarity.
- · AI Agent developers
- · Enterprises deploying LLM agents at scale
- · Researchers in cognitive AI
- · Systems heavily reliant on simple retrieval/recency models
More robust, long-term capable AI agents will emerge, reducing the need for constant human supervision in complex tasks.
The cognitive grounding of these memory models could lead to new insights into human memory and learning processes.
This advancement may accelerate the development of truly autonomous systems capable of operating over extended periods with evolving goals and environments.
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Read at arXiv cs.AI