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[2602.16424] Verifiable Semantics for Agent-to-Agent Communication
Summary
This paper introduces a certification protocol for agent-to-agent communication in multiagent AI systems, addressing semantic drift and ensuring consistent understanding among agents.
Why It Matters
As AI systems increasingly rely on multiagent communication, ensuring that agents share a common understanding of terms is crucial for effective collaboration. This research proposes a method to verify and maintain semantic consistency, which could enhance the reliability of AI interactions and applications.
Key Takeaways
- Proposes a certification protocol to verify shared understanding among agents.
- Introduces 'core-guarded reasoning' to limit semantic drift and disagreement.
- Demonstrates a significant reduction in disagreement through simulations.
- Outlines mechanisms for recertification and vocabulary renegotiation.
- Provides a foundational step towards verifiable agent communication in AI.
Computer Science > Artificial Intelligence arXiv:2602.16424 (cs) [Submitted on 18 Feb 2026] Title:Verifiable Semantics for Agent-to-Agent Communication Authors:Philipp Schoenegger, Matt Carlson, Chris Schneider, Chris Daly View a PDF of the paper titled Verifiable Semantics for Agent-to-Agent Communication, by Philipp Schoenegger and 3 other authors View PDF HTML (experimental) Abstract:Multiagent AI systems require consistent communication, but we lack methods to verify that agents share the same understanding of the terms used. Natural language is interpretable but vulnerable to semantic drift, while learned protocols are efficient but opaque. We propose a certification protocol based on the stimulus-meaning model, where agents are tested on shared observable events and terms are certified if empirical disagreement falls below a statistical threshold. In this protocol, agents restricting their reasoning to certified terms ("core-guarded reasoning") achieve provably bounded disagreement. We also outline mechanisms for detecting drift (recertification) and recovering shared vocabulary (renegotiation). In simulations with varying degrees of semantic divergence, core-guarding reduces disagreement by 72-96%. In a validation with fine-tuned language models, disagreement is reduced by 51%. Our framework provides a first step towards verifiable agent-to-agent communication. Subjects: Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA) Cite as: arXiv:2602.16424 [cs.AI] ...