A survey that unifies prior work on multi-agent LLM systems via the LIFE framework, mapping dependencies across collaboration, failure attribution, and autonomous self-evolution while identifying cross-stage challenges.
arXiv preprint arXiv:2410.11905 , year =
7 Pith papers cite this work. Polarity classification is still indexing.
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Identifies concrete attacks from a malicious Provider on SAGA and proposes SAGA-BFT, SAGA-MON, SAGA-AUD, and SAGA-HYB mitigations offering different security-performance trade-offs.
HyLaT proposes a hybrid latent-text communication protocol with two-stage training that reduces overhead while maintaining performance in multi-agent LLM systems.
The paper identifies twelve protocol-level security risks across MCP, A2A, Agora, and ANP and quantifies wrong-provider tool execution risk in MCP via a measurement-driven case study on multi-server composition.
DarwinNet is a tri-layered evolutionary network architecture that synthesizes intents into bytecode via LLM-driven adaptation and tracks maturity with a Protocol Solidification Index to achieve anti-fragility.
Proposes five foundational pillars and architectural patterns for building robust GenAI-native systems by combining AI with software engineering principles.
The agentic web requires new normative infrastructure of laws, norms, and practices to allow user-delegated AI agents to access online properties without being blocked as malicious bots.
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DarwinNet: An Evolutionary Network Architecture for Agent-Driven Protocol Synthesis
DarwinNet is a tri-layered evolutionary network architecture that synthesizes intents into bytecode via LLM-driven adaptation and tracks maturity with a Protocol Solidification Index to achieve anti-fragility.