paper2-hcma-acm

HCMA-ACM (paper 2): integrates HCMA (Hippocampal-Cortical Memory Architecture) as the governed long-term memory of an agentic context management (ACM) agent. ACM losslessly offloads context but preserves superseded versions of facts; HCMA consolidates them through bi-temporality, negative voting, and version collapse, so retrieval returns only the current version. On a controlled benchmark of superseding API specifications, HCMA reduces stale-value use from 100% to 0% (Llama-3.1-8B) and from 20% to 0% (GPT-4o-mini), lifting task success from 0% to 100% and 80% to 100%, respectively.

Authors

Publication Details

Journal
Open Science Framework
Published
2026-10-29
DOI
https://doi.org/10.17605/osf.io/rdc29
Primary Topic
AI-based Problem Solving and Planning
Type
article
Field-Weighted Citation Impact
0.00
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article

paper2-hcma-acm

Mauro Javier Silva
Open Science Framework
AI-based Problem Solving and Planning
article

paper2-hcma-acm

Mauro Javier Silva
article en

Abstract

HCMA-ACM (paper 2): integrates HCMA (Hippocampal-Cortical Memory Architecture) as the governed long-term memory of an agentic context management (ACM) agent. ACM losslessly offloads context but preserves superseded versions of facts; HCMA consolidates them through bi-temporality, negative voting, and version collapse, so retrieval returns only the current version. On a controlled benchmark of superseding API specifications, HCMA reduces stale-value use from 100% to 0% (Llama-3.1-8B) and from 20% to 0% (GPT-4o-mini), lifting task success from 0% to 100% and 80% to 100%, respectively.

Open Science Framework
Openalex Percentile: Top 14%
AI-based Problem Solving and Planning
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paper2-hcma-acm — Mauro Javier Silva · Open Science Framework (2026) | TGRS Research Map | TGRS