CraniMem: Cranial Inspired Gated and Bounded Memory for Agentic Systems

Pearl Mody, Mihir Panchal*, Rishit Kar*, Kiran Bhowmick, Ruhina Karani
Dwarkadas Jivanlal Sanghvi College of Engineering
International Conference on Learning Representations (ICLR 2026)
*Indicates Equal Contribution
Paper PyPI Code arXiv ICLR Virtual Talk
CraniMem Architecture Diagram

CraniMem architecture. The pipeline implements ingestion via RAS inspired gating and utility tagging, short-term storage in a bounded episodic buffer, optimization through replay selection and pruning to Trash, long-term structural storage via a linkage engine that updates a knowledge graph, and dual-path retrieval using both the buffer and the graph.

Abstract

Large language model (LLM) agents are increasingly deployed in long running workflows, where they must preserve user and task state across many turns. Many existing agent memory systems behave like external databases with ad hoc read/write rules, which can yield unstable retention, limited consolidation, and vulnerability to distractor content. We present CraniMem, a neurocognitively motivated, gated and bounded multi-stage memory design for agentic systems. CraniMem couples goal conditioned gating and utility tagging with a bounded episodic buffer for near term continuity and a structured long-term knowledge graph for durable semantic recall. A scheduled consolidation loop replays high utility traces into the graph while pruning low utility items, keeping memory growth in check and reducing interference. On long horizon benchmarks evaluated under both clean inputs and injected noise, CraniMem is more robust than a Vanilla RAG and Mem0 baseline and exhibits smaller performance drops under distraction. Our code is available at https://github.com/PearlMody05/Cranimem and the accompanying PyPI package at https://pypi.org/project/cranimem.

Poster

BibTeX

@article{mody2026cranimem,
  title={CraniMem: Cranial Inspired Gated and Bounded Memory for Agentic Systems},
  author={Mody, Pearl and Panchal, Mihir and Kar, Rishit and Bhowmick, Kiran and Karani, Ruhina},
  journal={arXiv preprint arXiv:2603.15642},
  year={2026}
}