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Large Language Model (LLM) agents are now widely deployed in Ambient Intelligence (AmI) environments, where autonomous agents must sense, act, and coordinate at scale. As agent capabilities and interdependence increase, traditional reliability strategies such as isolated adaptive control, anomaly detection, or trust modeling have proven inadequate due to their fragmented and scenario-specific nature. Comprehensive architectures that enable integrated self-management, collective anomaly response, robust information dissemination, and privacy-preserving adaptation remain scarce. We propose a bio-autonomic framework for decentralized resilience in multi-agent LLM systems where a unified architecture systematically applies principles from biological autonomic systems to LLM-based multi-agent environments. Specifically, each agent implements an autonomic control loop, formally structured as Monitor-Analyze-Plan-Execute over a shared Knowledge base (MAPE-K), for self-regulation. At the system level, the framework integrates immune-inspired anomaly detection using the Dendritic Cell Algorithm, probabilistic computational trust, decentralized gossip for robust information sharing, and federated learning with homomorphic encryption for collaborative, privacy-preserving adaptation. This holistic approach enables LLM agent ecosystems to self-organize, detect and isolate faults, and collectively adapt as system complexity increases. Empirical evaluations show that our framework achieves substantially improved resilience and recovery compared to state-of-the-art multi-agent baselines.
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This content will become publicly available on November 12, 2026
Trust-Aware Task Allocation With Quantum Optimization in Adversarial Multi-Agent Systems
This paper introduces a trust-aware task allocation framework for adversarial multi-agent systems, leveraging quantum optimization to improve decision-making under uncertainty. The approach models agent reliability and adversarial behavior through a trust-aware utility function, enabling robust and adaptive task assignment in dynamic environments. By incorporating quantum-inspired optimization techniques, the framework efficiently explores complex solution spaces that are difficult for classical methods. Experimental results demonstrate improved resilience to adversarial agents, enhanced allocation efficiency, and overall system robustness compared to baseline approaches. The proposed framework contributes to secure and scalable coordination in distributed systems, with applications in cybersecurity, autonomous systems, and networked sensing environments.
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- Award ID(s):
- 2349236
- PAR ID:
- 10674552
- Publisher / Repository:
- IEEE
- Date Published:
- ISBN:
- 979-8-3315-8132-9
- Page Range / eLocation ID:
- 2716 to 2725
- Subject(s) / Keyword(s):
- Trust-aware systems Multi-agent systems Quantum optimization Adversarial AI Task allocation Distributed systems Secure AI
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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