Emergent Misaligned Communication in Long-Horizon Multi-Agent LLM Commerce

• URL: http://arxiv.org/abs/2608.14825v1
• Date: Fri, 14 Aug 2026 18:56:12 GMT
• Title: Emergent Misaligned Communication in Long-Horizon Multi-Agent LLM Commerce
• Abstract summary: We operationalize speech-act misalignment as emails containing false factual claims, manipulation, collusion, or threats.<n>12.6% of emails are labeled misaligned; misalignment appears in all 20 runs and 74.7% of individual agent-runs.<n>We find no evidence that higher-capability models differentially exploit weaker counterparties.
• Score: 39.937871162878785
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Frontier LLM agents increasingly transact on behalf of separate principals, often using natural language rather than structured APIs. Much of the safety literature studies misaligned LLM behavior through adversarial-elicitation evaluations on single agents or stylized tasks. Its prevalence and structure in settings that combine long horizons, separate principals, real operational state, and inter-agent natural-language exchange remain insufficiently measured. We study 2,583 inter-agent emails from 20 one-year simulation runs of Vending-Bench Arena, a competitive vending environment spanning 13 frontier LLMs. We operationalize speech-act misalignment as emails containing false factual claims, manipulation, collusion, or threats, combining message content with ground-truth simulator state and logged reasoning traces to classify and validate such behavior. Under our primary classifier, 12.6% of emails are labeled misaligned; misalignment appears in all 20 runs and 74.7% of individual agent-runs. Both the magnitude and composition of this misalignment are preserved under repeated classification at different sampling temperatures and under full-pipeline replication with judges from two other frontier-model families. Misalignment is also reciprocal and stress-conditioned: receiving a misaligned email from a counterparty raises the odds of a misaligned reply by 1.65x, and low-inventory conditions raise them by 1.58x. Across tests of capability-asymmetric exploitation, we find no evidence that higher-capability models differentially exploit weaker counterparties, and model performance rank does not predict misalignment rates. Together, these results indicate that measurable, state-dependent misalignment can arise in competitive multi-agent environments without engineered elicitation, in patterns associated with operational scarcity and counterparty behavior rather than model capability alone.

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