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Trust-Weighted Adaptive Consensus: A Resilient Coordination Framework for Next-Generation Large Language Model Multi-Agent Systems
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 187 - Number 145 |
| Year of Publication: 2026 |
| Authors: J. Robert Adaikalaraj, J. Jegathesh Amalraj, M. Sivakumar |
10.5120/ijcaee078c422a35
|
J. Robert Adaikalaraj, J. Jegathesh Amalraj, M. Sivakumar . Trust-Weighted Adaptive Consensus: A Resilient Coordination Framework for Next-Generation Large Language Model Multi-Agent Systems. International Journal of Computer Applications. 187, 145 ( Sep 2026), 18-24. DOI=10.5120/ijcaee078c422a35
Abstract
Large language model (LLM) agents have moved from single-turn assistants to persistent, tool-using systems that plan, delegate, and cooperate. This shift has produced an explosion of multi-agent system (MAS) frameworks, interoperability protocols, and orchestration patterns, yet production deployments continue to report failure rates far higher than single-agent baselines. This paper synthesizes the current state of AI agents and multi-agent systems as of mid-2026, organizes the coordination design space into a four-part topology taxonomy, and reviews the emerging empirical literature on why multi-agent systems fail. Building on documented gaps, particularly the absence of adaptive, trust-aware coordination in widely used frameworks. We propose Trust-Weighted Adaptive Consensus (TWAC), a coordination-layer mechanism that combines capability-aware sparse shortlisting, an exponentially-weighted per-agent trust ledger, and uncertainty-gated verification. We evaluate TWAC against round-robin and centralized-auction baselines in a controlled simulation across 500 tasks and 30 independent trials. TWAC improves task success rate while reducing communication overhead relative to full-broadcast coordination and lowering cascading-failure rates by more than an order of magnitude. We conclude with future research implications for trust-based coordination in agentic AI systems.
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