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TOPIC · ENTITY #3675

long-term memory

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3

RESEARCH · 1 SOURCE · arXiv cs.AI

M-Plan: closed-loop multi-agent framework integrating long- and short-term memory for LLM agents

M-Plan is a closed-loop multi-agent system (MAS) framework that combines long-term and short-term memory via global-local and past-current task-specific guidance. The paper (arXiv:2610.27032v1) describes guidance generation during execution, local hints when agents are confused, post-task memory optimization for future retrieval, and reports improved accuracy and efficiency on multi-turn benchmarks including ALFWorld, WebShop, and PDDL compared to several baselines.

6.0

RESEARCH · 1 SOURCE · arXiv cs.AI

Bringing AI to Autonomous Systems — From Cognition to Collective Intelligence (arXiv:2609.30291v1)

This new arXiv preprint presents a comprehensive framework for designing and evaluating autonomous systems, centering on a generic agent architecture that composes cognitive functions around a long-term memory of evolving knowledge. The paper argues for combining connectionist and symbolic approaches, integrating AI with systems engineering, highlights implementation challenges (sensory-to-structured-data links, goal-driven decision-making, multi-agent coordination), and proposes directions for assessing agent trustworthiness while noting a large gap between the vision and current capabilities.

6.0

RESEARCH · 1 SOURCE · arXiv cs.AI

Memory Has Geometry (arXiv:2609.17969v1): non‑uniform geometric memory for long‑horizon personalization

New arXiv preprint argues that long-term personalization should represent memory as a user-specific dynamical state space with locally heterogeneous geometry rather than as static records in a single latent space. The paper proposes trajectory-conditioned reconstruction (not just nearest-neighbor lookup) to capture stable vs. volatile regions, variable-rate drift, heterogeneous neighborhoods, and uncertainty about current user state.

6.0