What question did this study set out to answer?

The research aims to enhance the robustness and traceability of AI-generated responses using a multi-agent system.

April 19, 20260 citationsOpen Access

SMCA: Stud Memory Combat Agents

FBFrancesco BULLAMinistry of Education, Universities and Research

Key Points

The research aims to enhance the robustness and traceability of AI-generated responses using a multi-agent system.
Integration of a persistent associative memory (StudSar)
Incorporation of temporal decay and resurrection mechanisms (TMDR and God Protocol)
Deployment of a competitive Arena with 10 agents producing a Champion response
Implementation of an adversarial audit by a Red Agent (Ziora)
Use of a meta-cognitive Judge to combine confidence with Champion Resilience Score (CRS)
Persistent memory significantly improves trust calibration and reduces hallucinations in responses.
Limitations include a small dataset and reliance on auto-resolve mechanisms.
Future work aims to validate findings on a larger scale and improve human interaction in the escalation process.

Abstract

SMCA Phase 3 presents a multi‑agent architecture designed to improve the robustness and traceability of generated responses. It integrates a persistent associative memory (StudSar) with temporal decay and resurrection mechanisms (TMDR and God Protocol), an Arena of 10 competing agents that produce a Champion response, a Red Agent (Ziora) performing adversarial audits, and a meta‑cognitive Judge that combines base confidence with a Champion Resilience Score (CRS). Experimental results highlight the critical role of persistent memory in calibrating trust and reducing hallucinations; limitations include a small dataset and reliance on auto‑resolve instead of human escalation. Future work targets large‑scale validation, human‑in‑the‑loop escalation, and improved robustness metrics.

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