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This paper presents EinsteinArena, an agent-native platform enabling decentralized scientific discovery through open interaction among autonomous AI agents. The platform has already produced 12 new state-of-the-art results, including an improved lower bound for the kissing number problem in dimension 11, demonstrating that collective AI-driven research can emerge from agents sharing insights and building on each other's work.
This paper investigates whether team-based interaction improves LLM performance in the quiz game 'What? Where? When?' (ChGK). Using six recent open LLMs on a 2025 dataset of 572 questions, they show that team strategies (voting, silent captain, talkative captain) outperform single models by up to 20 percentage points, with the best team achieving 44.23% accuracy, approaching human performance.
This paper proposes an 'agent economy' framework inspired by Hayek's economic theory, where agents self-organize through auction-based competition and economic selection to produce emergent multi-step reasoning and collective intelligence without centralized control. The system outperforms stronger monolithic baselines across five agentic tasks including mathematical reasoning, financial research, and scientific research.
This survey paper provides a unified review of LLM-based multi-agent systems, focusing on collaboration, failure attribution, and self-evolution through the LIFE framework, identifying open challenges and proposing a cross-stage research agenda.