#agent-networks News & Analysis

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

4 articles

AI × CryptoBullisharXiv – CS AI · May 287/10

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SwarmHarness: Skill-Based Task Routing via Decentralized Incentive-Aligned AI Agent Networks

SwarmHarness proposes a decentralized protocol enabling unused computing resources across personal devices and servers to be shared through a self-organizing network of AI agents without central authority. The system combines peer discovery via DHT, intelligent task routing based on capability and trust metrics, and a Shapley-value-based credit mechanism to align incentives and create a self-regulating participation economy.

AIBearisharXiv – CS AI · May 277/10

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Behind EvoMap: Characterizing a Self-Evolving Agent-to-Agent Collaboration Network

A large-scale empirical study of EvoMap, an agent-to-agent collaboration network, reveals critical structural flaws: 98% of assets go unused despite incentive mechanisms, quality scoring systems are easily manipulated through self-reported metadata, and over 84% of assets bypass quality checks through vacuous validation. The findings highlight fundamental challenges in designing trustworthy decentralized AI ecosystems that balance scalability with verifiable execution.

AIBullisharXiv – CS AI · Mar 47/104

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OpenClaw, Moltbook, and ClawdLab: From Agent-Only Social Networks to Autonomous Scientific Research

Researchers introduced ClawdLab, an open-source platform for autonomous AI scientific research, following analysis of OpenClaw framework and Moltbook social network that revealed security vulnerabilities across 131 agent skills and over 15,200 exposed control panels. The platform addresses identified failure modes through structured governance and multi-model orchestration in fully decentralized AI systems.

AINeutralarXiv – CS AI · Apr 156/10

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Modality-Native Routing in Agent-to-Agent Networks: A Multimodal A2A Protocol Extension

Researchers demonstrate that MMA2A, a multimodal routing protocol for agent-to-agent networks, achieves 52% task accuracy versus 32% for text-only baselines by preserving native modalities (voice, image, text) across agent boundaries. The 20-percentage-point improvement requires both protocol-level native routing and capable downstream reasoning agents, establishing routing as a critical design variable in multi-agent systems.

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