PRIMA: Operational Patterns for Resilient Multi-Agent Research with Verifiable Identity and Convergent Feedback
The paper titled 'PRIMA: Operational Patterns for Resilient Multi-Agent Research with Verifiable Identity and Convergent Feedback' presents a framework for managing multi-agent systems in artificial intelligence. It introduces three operational patterns aimed at enhancing resilience and recovery during long-running tasks. The study also includes a case study on Graph Isomorphism, demonstrating the practical application of the proposed methods.
- ▪PRIMA addresses failure modes in coordinated multi-agent research systems that single-shot evaluations cannot capture.
- ▪The framework includes a resilience-and-recovery layer, a sub-agent operating discipline, and a multi-phase application pattern.
- ▪Agent identities are derived from prime powers, ensuring collision-free identifiers and verifiable cluster membership.
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Computer Science > Artificial Intelligence arXiv:2605.24775 (cs) [Submitted on 23 May 2026] Title:PRIMA: Operational Patterns for Resilient Multi-Agent Research with Verifiable Identity and Convergent Feedback Authors:Sasank Annapureddy View a PDF of the paper titled PRIMA: Operational Patterns for Resilient Multi-Agent Research with Verifiable Identity and Convergent Feedback, by Sasank Annapureddy View PDF HTML (experimental) Abstract:Operating LLMs as coordinated multi-agent research systems over multi-hour runs surfaces failure modes that single-shot evaluation cannot: upstream providers throttle without warning, sub-agents drift the task to fit accessible tools, narrate machinery instead of using it, open revision iterations with self-apology, or treat upstream context as executable…
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