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The Civilization Framework: Sovereign-Anchored Communication Between Personal Multi-Agent Systems

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The Civilization Framework proposes a new way to structure communication between AI agents by treating the civilization—comprising a human sovereign, a persistent ledger, and interchangeable agents—as the addressable party rather than individual agents. It introduces the Embassy Protocol, an asynchronous, carrier‑agnostic overlay that routes messages to a ledger endpoint where any online agent can process them, with commitment state on ledgers serving as the true record of interaction. The paper also identifies a temporal‑weight effect in AI‑to‑AI communication, demonstrates its impact in a preregistered experiment, and discusses mitigation strategies such as instruction‑level provenance labeling and sealed‑answer accuracy equivalence. whyItMatters":"The framework offers a novel architecture that could reduce context loss and authority bias in multi‑agent AI systems, potentially improving reliability and accountability in AI‑driven interactions."

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arXiv AI
Sep 4

The Civilization Framework: Sovereign-Anchored Communication Between Personal Multi-Agent Systems

The Civilization Framework proposes a new way for AI systems to communicate by treating the entire civilization—one human sovereign, a persistent ledger, and interchangeable agents—as the addressable party, rather than individual agents. It introduces the Embassy Protocol, an asynchronous, carrier‑agnostic overlay that routes messages to a ledger endpoint where any online agent of the receiver can process them, with commitment state on both ledgers serving as the ground truth. The framework also identifies a temporal‑weight effect in AI‑to‑AI communication, demonstrates its impact in a preregistered experiment, and explores mitigation strategies such as instruction‑level provenance labeling and sealed‑answer accuracy equivalence. whyItMatters":"The framework aims to reduce context loss and authority bias in AI interactions by grounding communication in a shared ledger and sovereign oversight, potentially improving reliability and accountability in multi‑agent systems."

By Guangjun Liu
arXiv AI
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The paper proposes five runtime primitives—discovery, identity, governance, attestation, and supply chain—to manage autonomous AI agents in enterprise settings. It argues that traditional control models fail because agents are transient, model-driven, and self‑discoverable, making runtime governance essential. The authors detail an implementation that mediates agent actions against policy, authorizes them via a per‑tenant vocabulary, and records them in a verifiable ledger, noting the associated operational costs and partial deployment status.

By Jiten Oswal, John Cadeddu
Hugging Face Trending Papers
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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? The ERC-8004 protocol addresses this challenge with the first permissionless trust layer for AI agent economies, built around three on-chain registries for Identity, Reputation, and Validation.