English

CogniFold: Always-On Proactive Memory via Cognitive Folding

Artificial Intelligence 2026-05-26 v2 Computation and Language

Abstract

Existing agent memory remains predominantly reactive and retrieval-based, lacking the capacity to autonomously organize experience into persistent cognitive structure. Toward genuinely autonomous agents, we introduce CogniFold, a brain-inspired "always-on" agent memory designed for the next generation of proactive assistants. CogniFold continuously folds fragmented event streams into self-emerging cognitive structures, bootstrapping progressively higher-level cognition from incoming events and accumulated knowledge. We ground this by extending Complementary Learning Systems (CLS) theory from two layers (hippocampus, neocortex) to three, adding a prefrontal intent layer. Emulating the prefrontal cortex as the locus of intentional control and decision-making, CogniFold achieves this through graph-topology self-organization: cognitive structures proactively assemble under the stream, merge when semantically similar, decay when stale, relink through associative recall, and surface intents when concept-cluster density crosses a threshold. We evaluate structural formation using CogEval-Bench, demonstrating that CogniFold uniquely produces memory structures that match cognitive expectations and concept emergence. Furthermore, across 7 broad-coverage benchmarks spanning five cognitive domains, we validate that CogniFold simultaneously performs robustly on conventional memory benchmarks.

Keywords

Cite

@article{arxiv.2605.13438,
  title  = {CogniFold: Always-On Proactive Memory via Cognitive Folding},
  author = {Suli Wang and Yiqun Duan and Yu Deng and Rundong Zhao and Dai Shi and Xinliang Zhou},
  journal= {arXiv preprint arXiv:2605.13438},
  year   = {2026}
}

Comments

Code is available at https://github.com/OpenNorve/CogniFold