#swarm-intelligence News & Analysis

4 articles tagged with #swarm-intelligence. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.

4 articles

AIBearisharXiv – CS AI · May 17/10

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The Inverse-Wisdom Law: Architectural Tribalism and the Consensus Paradox in Agentic Swarms

Researchers challenge the assumption that multi-agent AI systems benefit from the 'Wisdom of the Crowd' by demonstrating the Inverse-Wisdom Law: adding more logical agents to swarms can paradoxically increase the stability of errors rather than improve accuracy. Through 36 experiments across major benchmarks, the study reveals that architectural tribalism causes agents to prioritize internal agreement over external truth, with system integrity ultimately determined by the synthesizer's logic rather than individual agent quality.

🧠 GPT-5🧠 Claude🧠 Sonnet

AI × CryptoNeutralarXiv – CS AI · Apr 77/10

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PolySwarm: A Multi-Agent Large Language Model Framework for Prediction Market Trading and Latency Arbitrage

PolySwarm is a new multi-agent AI framework that uses 50 diverse large language models to trade on prediction markets like Polymarket, combining swarm intelligence with arbitrage strategies. The system outperformed single-model baselines in probability calibration and includes latency arbitrage capabilities to exploit pricing inefficiencies across markets.

AIBullisharXiv – CS AI · Mar 37/103

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Large Language Model-Assisted UAV Operations and Communications: A Multifaceted Survey and Tutorial

Researchers have published a comprehensive survey exploring the integration of Large Language Models (LLMs) with Uncrewed Aerial Vehicles (UAVs), proposing a unified framework for intelligent drone operations. The study examines how LLMs can enhance UAV capabilities including swarm coordination, navigation, mission planning, and human-drone interaction through advanced reasoning and multimodal processing.

AINeutralarXiv – CS AI · Mar 35/107

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SIGMAS: Second-Order Interaction-based Grouping for Overlapping Multi-Agent Swarms

Researchers introduce SIGMAS, a self-supervised AI framework for identifying group structures in multi-agent swarms like drone fleets without ground-truth supervision. The system uses second-order interactions to infer latent group memberships from agent trajectories, demonstrating robust performance across diverse synthetic swarm scenarios.