Odyssey: Constructing Verifiable Local Truth-Preserving Foundation Models

📰 ArXiv cs.AI

Learn to construct verifiable local truth-preserving foundation models using ODYSSEY, a categorical framework for composing foundries

advanced Published 29 Jun 2026

Action Steps

Construct a foundry by specifying a cover of local contexts and local representation families
Define restriction maps and gluing rules to compose foundries
Implement obstruction policies and update obligations to ensure truth-preservation
Use ODYSSEY to construct a verifiable local truth-preserving foundation model
Evaluate the model's performance and refine it as needed

Who Needs to Know This

AI researchers and engineers can benefit from this framework to develop more reliable and transparent foundation models, which can be used in various applications such as natural language processing and computer vision

Key Insight

💡 ODYSSEY provides a categorical framework for composing foundries to construct verifiable local truth-preserving foundation models

Key Takeaways

Learn to construct verifiable local truth-preserving foundation models using ODYSSEY, a categorical framework for composing foundries

Full Article

Title: Odyssey: Constructing Verifiable Local Truth-Preserving Foundation Models

Abstract:
arXiv:2606.27593v1 Announce Type: new Abstract: We introduce a categorical framework called ODYSSEY for constructing verifiable, local truth-preserving foundation models as compositions of foundries: building-block architectural components that specify a cover of local contexts, local representation families, restriction maps, gluing rules, obstruction policies, update obligations, and human-facing views. A foundry is an organized sheaf of knowledge that carries within it an argumentation compon

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