GENSTRAT: Toward a Science of Strategic Reasoning in Large Language Models

arXiv:2605.23238v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly deployed as economic agents in marketplaces, auctions, and bidding settings. Anticipating their behavior in any specific deployment is hard. Existing strategic-reasoning benchmarks evaluate models on fixed canonical games. These benchmarks may saturate as the frontier improves, and they do not allow evaluators to generalize with confidence from benchmark performance to the varied and messy strategic environments that actual deployments involve. We introduce GENSTRAT, which uses procedurally generate

Source: arXiv cs.LG — read the full report at the original publisher.

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