arXiv:2606.27397v1 Announce Type: cross Abstract: Evaluating LLM agents requires dynamic environments that go beyond static reasoning and zero-sum games. Real-world economic interaction is often open-ended and mixed-motive: agents must negotiate, create positive-sum surplus, compete for scarce assets, and plan under delayed returns. We introduce SidConArena, a new benchmark framework for evaluating LLM agents in open-ended, positive-sum bargaining. SidConArena formalizes a multi-player economy as a finite-horizon partially observable stochastic game with three coupled phases: natural-language
The rapid advancement and deployment of LLM agents necessitate robust evaluation frameworks that move beyond simplistic metrics to real-world economic interactions.
Sophisticated evaluation environments like SidConArena are crucial for developing truly autonomous and effective AI agents capable of complex, positive-sum interactions.
The focus for AI agent development shifts towards scenarios involving negotiation, surplus creation, and planning under uncertainty, moving beyond zero-sum competitive models.
- · AI agent developers
- · LLM research institutions
- · Companies seeking autonomous workflow solutions
- · Developers of simplistic AI evaluation benchmarks
- · Companies relying on AI agents in zero-sum environments only
This benchmark will enable the creation of more sophisticated and robust LLM agents capable of handling complex economic interactions.
Improved agent negotiation and planning capabilities could lead to autonomous systems taking on more intricate roles in business and finance.
The widespread adoption of positive-sum agentic systems might eventually reshape economic models and increase overall market efficiency and value creation.
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Read at arXiv cs.AI