The paper proposes a Semantic Gateway and a Zero-Trust security model to formally validate and secure autonomous AI agents operating in enterprise systems, achieving a 100% discovery rate of unauthori…

The paper proves that platform-deterministic inference is a necessary and sufficient condition for trustworthy AI, establishing that AI trust fundamentally relies on consistent arithmetic.

The paper introduces POIROT, a novel protocol that uses the agents within a multi-agent system itself to diagnose and detect failures, demonstrating superior performance over traditional evaluation me…

The paper introduces MolTrust, a production-deployed trust infrastructure built on W3C standards (VCs and DIDs) that provides a verifiable, multi-layered authorization framework for autonomous AI agen…

The paper introduces Agora, a domain-aware multi-agent framework that successfully detects deep, previously unknown logic bugs in complex consensus protocols, outperforming existing LLM-based analysis…

The paper introduces Sovereign Agentic Loops (SAL), a control-plane architecture that decouples LLM reasoning from system execution to enhance safety and reliability in real-world AI agents.

The paper proposes the concept of an Agent Operating System (AOS) to provide a necessary systems foundation for managing the unique, non-deterministic, and goal-directed execution characteristics of m…

The paper proposes the concept of an Agent Operating System (AOS) to provide a rigorous, controllable, and accountable systems foundation for running complex, probabilistic, and goal-directed AI agent…

This paper systematizes the security challenges of open agentic systems, concluding that while attack characterization is mature, the field lacks robust guidelines for operational governance, memory i…

The paper proposes Sovereign 2.0, a control-plane-centric model redefining cloud sovereignty as enforceable governance authority and operational control, rather than mere data location.

The paper proposes Proof-Carrying Agent Actions (PCAA), a runtime-neutral governance model that uses action certificates to consistently track and authorize high-risk actions across diverse and hetero…

This survey provides a comprehensive, practical guide to ensuring the trustworthiness of complex, autonomous agentic AI systems by focusing on safety, robustness, privacy, and system security.

The paper introduces the Reconstructive Authority Model (RAM), a novel framework that proves execution validity by assessing state coverage rather than just state integrity, showing that existing atte…

The paper proposes a declarative, autonomous, self-protecting framework for securing complex 5G/6G networks by leveraging a standardized security ontology and automated graph reasoning to neutralize l…

The paper proposes an evidence-driven protocol combining Deterministic Build Systems and Trusted Execution Environments to provide cryptographically verifiable guarantees of software artifact integrit…

The paper demonstrates that for edge-native SLMs used in decentralized governance, simpler, intuitive reasoning (System 1) is significantly more robust and efficient than complex, iterative deliberati…

The paper proposes the Redpanda Agentic Data Plane (ADP), an architecture that uses out-of-band metadata channels to deterministically enforce security policies and governance for autonomous AI agents…

The paper proposes the Intelligent Computing Architecture Model (ICAM), a six-layer framework that unifies disparate concepts in model-native computing by viewing the LLM stack through a dual-plane ar…

The paper proposes Arbiter-K, a Governance-First execution architecture that treats LLMs as probabilistic units encapsulated by a deterministic kernel, significantly improving the security and reliabi…

The paper systematizes the interaction between autonomous AI agents and blockchain platforms using a bidirectional trust framework, identifying significant gaps in current standards and proposing a ta…