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.
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A survey of agent interoperability protocols
16 Pith papers cite this work. Polarity classification is still indexing.
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OMC framework turns multi-agent AI into self-organizing companies with Talents, Talent Market, and E²R search, achieving 84.67% success on PRDBench (15.48 points above prior art).
MPAC defines a multi-principal agent coordination protocol across Session, Intent, Operation, Conflict, and Governance layers, with 21 message types and state machines, delivering 95% lower coordination overhead in a three-agent code review benchmark.
Presents a component-centric PoC dataset of malicious MCP servers and a two-stage behavioral deviation detector Connor achieving 94.6% F1-score.
GRAIL achieves over 79 times lower latency than LLM-parsing baselines and higher Recall@10 than vector search by combining SLM-enhanced prediction, pseudo-document expansion, and MaxSim resonance on the new AgentTaxo-9K dataset of 9,240 agents.
CADMAS-CTX replaces static skill profiles with context-conditioned Beta posteriors and uncertainty-penalized routing, yielding higher accuracy on GAIA (0.442) and SWE-bench (31.4%) than static baselines.
Modality-native routing in A2A networks raises task accuracy from 32% to 52% over text-bottleneck baselines on a 50-task benchmark, but only when paired with capable downstream reasoning.
MCPSHIELD offers a threat taxonomy of 23 attack vectors, a labeled transition system verification model, and a defense-in-depth architecture claiming 91% coverage for MCP-based AI agents.
No existing AI security framework covers a majority of the 193 identified multi-agent system threats in any category, with OWASP Agentic Security Initiative achieving the highest overall coverage at 65.3%.
HarnessAPI derives streaming HTTP endpoints, OpenAPI UI, and MCP tools from a single handler.py plus Pydantic schemas, cutting framework boilerplate by 74%.
A framework structures AI-generated content with prompt-aware metadata and verifiable credentials to support reliable assessment and reuse by agents.
LLM agent progress depends on externalizing cognitive functions into memory, skills, protocols, and harness engineering that coordinates them reliably.
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.
Proposes a DLT-anchored architecture extending the A2A protocol with on-chain AgentCards and x402 micropayments to enable multi-agent economies.
This work provides an empirical comparison of tool integration, multi-agent delegation, and hybrid architectures for LLM task orchestration, measuring response time, context consumption, cost, error recovery, and implementation complexity.
The survey organizes Context Engineering into retrieval, processing, management, and integrated systems like RAG and multi-agent setups while identifying an asymmetry where LLMs handle complex inputs well but struggle with equally sophisticated long outputs.
citing papers explorer
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Beyond Individual Intelligence: Surveying Collaboration, Failure Attribution, and Self-Evolution in LLM-based Multi-Agent Systems
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.
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From Skills to Talent: Organising Heterogeneous Agents as a Real-World Company
OMC framework turns multi-agent AI into self-organizing companies with Talents, Talent Market, and E²R search, achieving 84.67% success on PRDBench (15.48 points above prior art).
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MPAC: A Multi-Principal Agent Coordination Protocol for Interoperable Multi-Agent Collaboration
MPAC defines a multi-principal agent coordination protocol across Session, Intent, Operation, Conflict, and Governance layers, with 21 message types and state machines, delivering 95% lower coordination overhead in a three-agent code review benchmark.
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From Component Manipulation to System Compromise: Understanding and Detecting Malicious MCP Servers
Presents a component-centric PoC dataset of malicious MCP servers and a two-stage behavioral deviation detector Connor achieving 94.6% F1-score.
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GRAIL: A Deep-Granularity Hybrid Resonance Framework for Real-Time Agent Discovery via SLM-Enhanced Indexing
GRAIL achieves over 79 times lower latency than LLM-parsing baselines and higher Recall@10 than vector search by combining SLM-enhanced prediction, pseudo-document expansion, and MaxSim resonance on the new AgentTaxo-9K dataset of 9,240 agents.
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CADMAS-CTX: Contextual Capability Calibration for Multi-Agent Delegation
CADMAS-CTX replaces static skill profiles with context-conditioned Beta posteriors and uncertainty-penalized routing, yielding higher accuracy on GAIA (0.442) and SWE-bench (31.4%) than static baselines.
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Modality-Native Routing in Agent-to-Agent Networks: A Multimodal A2A Protocol Extension
Modality-native routing in A2A networks raises task accuracy from 32% to 52% over text-bottleneck baselines on a 50-task benchmark, but only when paired with capable downstream reasoning.
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A Formal Security Framework for MCP-Based AI Agents: Threat Taxonomy, Verification Models, and Defense Mechanisms
MCPSHIELD offers a threat taxonomy of 23 attack vectors, a labeled transition system verification model, and a defense-in-depth architecture claiming 91% coverage for MCP-based AI agents.
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Security Considerations for Multi-agent Systems
No existing AI security framework covers a majority of the 193 identified multi-agent system threats in any category, with OWASP Agentic Security Initiative achieving the highest overall coverage at 65.3%.
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HarnessAPI: A Skill-First Framework for Unified Streaming APIs and MCP Tools
HarnessAPI derives streaming HTTP endpoints, OpenAPI UI, and MCP tools from a single handler.py plus Pydantic schemas, cutting framework boilerplate by 74%.
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A Prompt-Aware Structuring Framework for Reliable Reuse of AI-Generated Content in the Agentic Web
A framework structures AI-generated content with prompt-aware metadata and verifiable credentials to support reliable assessment and reuse by agents.
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Externalization in LLM Agents: A Unified Review of Memory, Skills, Protocols and Harness Engineering
LLM agent progress depends on externalizing cognitive functions into memory, skills, protocols, and harness engineering that coordinates them reliably.
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Security Threat Modeling for Emerging AI-Agent Protocols: A Comparative Analysis of MCP, A2A, Agora, and ANP
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.
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Towards Multi-Agent Economies: Enhancing the A2A Protocol with Ledger-Anchored Identities and x402 Micropayments for AI Agents
Proposes a DLT-anchored architecture extending the A2A protocol with on-chain AgentCards and x402 micropayments to enable multi-agent economies.
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Empirical Comparison of Agent Communication Protocols for Task Orchestration
This work provides an empirical comparison of tool integration, multi-agent delegation, and hybrid architectures for LLM task orchestration, measuring response time, context consumption, cost, error recovery, and implementation complexity.
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A Survey of Context Engineering for Large Language Models
The survey organizes Context Engineering into retrieval, processing, management, and integrated systems like RAG and multi-agent setups while identifying an asymmetry where LLMs handle complex inputs well but struggle with equally sophisticated long outputs.