Proposes a levels x laws taxonomy for world models in AI agents, defining L1-L3 capabilities across physical, digital, social, and scientific regimes while reviewing over 400 works to outline a roadmap for advanced agentic modeling.
Game-theoretic llm: Agent workflow for negotiation games
5 Pith papers cite this work. Polarity classification is still indexing.
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CAGE uses common-agency games and an EPEC algorithm to compute equilibrium policies that balance multiple conflicting objectives for test-time LLM alignment.
BLAST combines LLM agents with blockchain for decentralized spectrum trading, where Vickrey auctions achieve up to 71% of theoretical social welfare surplus and outperform non-LLM baselines in competition and efficiency.
Simulations show that cooperative outcomes in network games with personality-driven LLM agents depend on both network connectivity and the placement of pro-social personalities, not just pairwise interaction preferences.
LLM agents cooperate in two standard games due to fairness reasoning instead of converging to Nash equilibria under multi-round prompts.
citing papers explorer
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Agentic World Modeling: Foundations, Capabilities, Laws, and Beyond
Proposes a levels x laws taxonomy for world models in AI agents, defining L1-L3 capabilities across physical, digital, social, and scientific regimes while reviewing over 400 works to outline a roadmap for advanced agentic modeling.
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Common-agency Games for Multi-Objective Test-Time Alignment
CAGE uses common-agency games and an EPEC algorithm to compute equilibrium policies that balance multiple conflicting objectives for test-time LLM alignment.
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BLAST: Blockchain-based LLM-powered Agentic Spectrum Trading
BLAST combines LLM agents with blockchain for decentralized spectrum trading, where Vickrey auctions achieve up to 71% of theoretical social welfare surplus and outperform non-LLM baselines in competition and efficiency.
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NetworkGames: Simulating Cooperation in Network Games with Personality-driven LLM Agents
Simulations show that cooperative outcomes in network games with personality-driven LLM agents depend on both network connectivity and the placement of pro-social personalities, not just pairwise interaction preferences.
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Competition and Cooperation of LLM Agents in Games
LLM agents cooperate in two standard games due to fairness reasoning instead of converging to Nash equilibria under multi-round prompts.