AINeutralarXiv – CS AI · 18h ago6/10
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Payoff scaling shapes cooperation in LLM agents across languages
Researchers analyzed how Large Language Models behave in repeated game scenarios, finding that LLMs become more cooperative as financial stakes increase—contrary to evolutionary game theory predictions. The study reveals that alignment training and human reasoning patterns embedded in LLM training data override expected selfish behavior, with implications for designing multi-agent AI systems in high-stakes environments.