What question did this study set out to answer?

To develop a memory architecture that prevents goal drift and errors in multi-agent AI systems.

March 23, 2026Open Access

PRISM: A Pointer-Based Provenance Memory Architecture with Architectural Content Firewall for Goal-Coherent Multi-Agent Artificial Intelligence

Key Points

To develop a memory architecture that prevents goal drift and errors in multi-agent AI systems.
Designed a four-layer memory architecture with enforced Content Firewall
Conducted 33 autonomous retrieval optimization runs
Performed 16 multi-agent coordination runs
Empirically validated the system's efficacy
Achieved 100% goal coherence
Obtained 100% cascade containment
Reached 100% adversarial resilience against poisoning
Surpassed baseline poisoning rate by significant margin
Secured 100% MEMTRACK quality compared to previous models

Abstract

Multi-agent AI systems suffer from goal drift, cascading errors, memory poisoning, and context window dependency. PRISM (Provenance-Routed Injection and State Memory) presents a four-layer memory architecture with an enforced Content Firewall that separates content-aware and content-blind components at the training and architecture level. Empirically validated across 33 autonomous retrieval optimization runs and 16 multi-agent coordination runs. Results: 100% goal coherence, 100% cascade containment, 100% adversarial resilience (vs TMCHT published baseline: 52.93% poisoning rate), 100% MEMTRACK quality (vs GPT-5 published baseline: 60% correctness ceiling). Patent pending USPTO March 20, 2026

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Cite This Study

Srivatsav Gopinath (Sat,) studied this question.

synapsesocial.com/papers/69c08bb5a48f6b84677f93ef https://doi.org/https://doi.org/10.5281/zenodo.19142033

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