arXiv AI By Andrea Ferrario

A Category Theory Account of AI Identity

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arXiv:2607. 00220v1 Announce Type: cross Abstract: Artificial intelligence (AI) systems are routinely modified after deployment through retraining and changes in their environments.

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arXiv AI
Sep 18

When AI Agents Commit: Cognitive Serializability Across Data, Evidence, Policy, and Authority

The paper introduces the concept of Cognitive Serializability for autonomous AI agents, ensuring that mutations derived from dynamic inputs—such as database reads, evidence, policy, beliefs, and delegated authority—are committed in a serial, logically consistent order. It presents a framework called Trusted Cognitive Transaction (TCT) that combines immutable executable definitions, sealed envelopes, guard-first commits, and receipt-driven reconciliation to enforce serializability and prevent anomalies. Experimental results show that the prototype implementation incurs minimal overhead while eliminating injected anomalies.

By Jun He, Deying Yu
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