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#algorithmic-game-theory News & Analysis

4 articles tagged with #algorithmic-game-theory. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.

4 articles
AINeutralarXiv – CS AI · Mar 57/10
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Learning Approximate Nash Equilibria in Cooperative Multi-Agent Reinforcement Learning via Mean-Field Subsampling

Researchers propose ALTERNATING-MARL, a new framework for cooperative multi-agent reinforcement learning that enables a global agent to learn with massive populations under communication constraints. The method achieves approximate Nash equilibrium convergence while only observing a subset of local agent states, with applications in multi-robot control and federated optimization.

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AINeutralarXiv – CS AI · Jun 96/10
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Discovering Expert-Level Nash Equilibrium Algorithms with Large Language Models

Researchers have developed LegoNE, a framework that enables large language models to automatically discover and formally verify polynomial-time algorithms for computing Nash equilibria in games. The system rediscovered existing optimal algorithms and discovered a new three-player algorithm that provably improves upon previous best-known guarantees, demonstrating that LLMs can innovate beyond established human design paradigms when augmented with formal verification tools.

AINeutralarXiv – CS AI · Jun 86/10
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Online Pandora's Box for Contextual LLM Cascading

Researchers propose an online contextual Pandora's Box model for optimizing LLM API cascading, where decision-makers sequentially query multiple APIs and select outputs based on indirect reward feedback. The approach achieves theoretically optimal regret bounds without requiring full distribution estimation, advancing practical optimization strategies for multi-API LLM systems.

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AINeutralarXiv – CS AI · May 126/10
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Toward Optimal Regret in Robust Pricing: Decoupling Corruption and Time

Researchers have resolved a longstanding open problem in robust dynamic pricing by developing a binary search variant that achieves decoupled regret bounds of O(C + log T) when corruption is known and O(C + log² T) when unknown, significantly improving upon the previous O(C log log T) bound from 2025.