CONSCIENTIA: Can LLM Agents Learn to Strategize? Emergent Deception and Trust in a Multi-Agent NYC Simulation
As large language models (LLMs) are increasingly deployed as autonomous agents, understanding how strategic behavior emerges in multi-agent environments has become an important alignment challenge. We take a neutral empirical stance and construct a controlled environment in which strategic behavior can be directly observed and measured. We introduce a large-scale multi-agent simulation in a simplified model of New York City, where LLM-driven agents interact under opposing incentives. Blue agents
Record details
Published: 10 April 2026
Source: arXiv
Category: Research
Topics: Safety & alignment · Agents & autonomy · Environment
Retrieved: 14 July 2026
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ethics.ai (10 April 2026), “CONSCIENTIA: Can LLM Agents Learn to Strategize? Emergent Deception and Trust in a Multi-Agent NYC Simulation,” evidence record 6079, https://ethics.ai/record/6079 (originally published by arXiv).
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