arXiv:2606. 15822v1 Announce Type: new Abstract: AI agents increasingly access external models, tools, and services through Agentic Routing Infrastructure (ARI) to manage the overhead of heterogeneous interfaces and fragmented subscriptions.
arXiv:2609.21325v1 Announce Type: new Abstract: Agentic marketplaces are emerging where AI agents with varying capabilities autonomously complete specialized tasks for buyers. A major challenge of su...
The paper proposes a scalable trust discovery architecture for the Internet of Agents, featuring a three‑layer hierarchical design: Agent Root for registry governance, Agent Registry for registration and metadata, and Agent Resolver for capability discovery. It introduces a registry‑suffix‑anchored composite identity scheme and a dual‑certificate, multi‑level authentication mechanism to strengthen agent identity trust. Prototype evaluation shows low latency (58 ms registration, 25 ms discovery) and high throughput (over 19,000 registrations and 29,000 discoveries per second).
arXiv:2608. 04366v1 Announce Type: cross Abstract: While retrieval-augmented generation systems partially address the hallucination issues in large language models, it also introduces new vulnerabilities to knowledge corruption attacks.
arXiv:2606. 15573v1 Announce Type: new Abstract: In agentic systems, human-generated data records anchor the value of AI services.
arXiv:2606. 26028v2 Announce Type: replace-cross Abstract: As autonomous AI agents increasingly transact across organizational boundaries, a fundamental trust challenge emerges: how can an agent assess whether an unknown counterpart is trustworthy?
arXiv:2607. 06612v1 Announce Type: cross Abstract: Federated Learning (FL) enables multiple clients to collaboratively train machine learning models while retaining data locality, thereby enhancing user privacy.
arXiv:2410. 11378v3 Announce Type: replace-cross Abstract: Personalized collaborative learning in federated settings faces a critical trade-off between customization and participant trust.
The paper reviews four studies that combine blockchain and AI to secure data sharing, model integrity, and autonomous decision-making in distributed systems. It highlights how blockchain’s immutability, decentralized consensus, and verifiable provenance can address trust gaps in training data, real‑time monitoring, and automated code remediation. The authors propose a layered architecture integrating hardened models, blockchain‑anchored provenance, AI anomaly detection, and smart‑contract‑governed multi‑agent remediation, and outline open challenges in scalability, privacy‑transparency trade‑offs, and governance.
Federated Learning (FL) enables multiple clients to collaboratively train machine learning models while retaining data locality, thereby enhancing user privacy. However, traditional FL frameworks rely on a centralized aggregation server and assume honest-but-curious clients, making them susceptible to both server-side inference and client-side poisoning attacks.
arXiv:2606. 00962v1 Announce Type: cross Abstract: Foundational agent interoperability standards, notably the Agent-to-Agent (A2A) protocol and the Model Context Protocol (MCP), have advanced multi-agent system communication, and complementary identity frameworks leveraging W3C Decentralised Identifiers (DIDs) and Verifiable Credentials (VCs) provide cryptographic agent authentication.
arXiv:2602. 08290v2 Announce Type: replace-cross Abstract: In federated learning (FL), decentralized model training allows multi-ple participants to collaboratively improve a shared machine learning model without exchanging raw data.