Ethical Coordination of LLM Multi-Agent Systems [* Cross-Reference *]

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J. de Curtò; I. de Zarzà; Carlos T. Calafate, 2026, "Ethical Coordination of LLM Multi-Agent Systems [* Cross-Reference *]", https://lore.list.lu/citation?persistentId=perma:LIST.BXO8BT, LIST Open Repository, V1
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Description
Embedding large language model (LLM) coordinators in production electronic systems, connected vehicles, multi-robot fabrics, IoT control loops, telecommunications orchestration, demands a pre-delivery filter stage that preserves ethical guarantees under adversarial influence at deployment scale. We present a constitutional governance layer that filters compiled influence policies before they reach a heterogeneous population of grounded LLM agents whose hybrid decision model combines a game-theoretic base probability with an LLM-evaluated narrative shift attenuated by per-agent resistance. Four experiments on a Barabási–Albert scale-free network of 30 agents powered by Llama-3.3-70B-Instruct show that the filter holds an Ethical Cooperation Score (ECS) of (Formula presented.) (multi-seed mean (Formula presented.), 95% confidence interval (CI) (Formula presented.) ) against an unconstrained baseline of (Formula presented.), enforced by a hard integrity gate ( (Formula presented.) vs. (Formula presented.) ). We surface an autonomy paradox in which unconstrained agents resist manipulation more forcefully ( (Formula presented.) vs. (Formula presented.) ) yet collapse to (Formula presented.), establishing that system-level integrity cannot be delegated to agent-level defence. The advantage is monotonic in resistance ( (Formula presented.) to (Formula presented.) ), seed-stable (Cliff’s (Formula presented.), complete separation), topology- and backbone-invariant across five contemporary LLMs, robust to alternative ECS formulations, and reproduces at N = 100. Against constitutional artificial intelligence (CAI) critique-revise and LlamaGuard-style safety-classifier baselines, the framework matches the integrity floor and adds a measurable margin on the secondary risk surface (burst timing, composite manipulation risk). The filter runs at 0.78 μs/call ( (Formula presented.) decisions/s/core), supporting always-on deployment as a stateless, model-agnostic component of LLM agent pipelines in adversarially contested electronic systems. (2026-05-25)
***This entry has been automatically imported via OpenAlex by LIST harvest scripts. Please refer to https://doi.org/10.3390/electronics15112278 for the original and latest version of the publication*** (2026-07-01)
Subject Computer and Information Science; Engineering; Physics; Social Sciences
Keyword Software deployment, Corporate governance, Autonomy, Adversarial system, Component (thermodynamics), Population, Filter (signal processing)
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