Odyssey: Constructing Verifiable Local Truth-Preserving Foundation Models

📄 arXiv: 2606.27593v1 📥 PDF

作者: Sridhar Mahadevan

分类: cs.AI, cs.LG

发布日期: 2026-06-25

备注: 34 pages

💡 一句话要点

提出ODYSSEY框架以构建可验证的本地真理保持基础模型

🎯 匹配领域: 支柱九：具身大模型 (Embodied Foundation Models)

关键词: 基础模型 可验证性 局部真理保持 范畴理论 foundry构建 智能决策 科学研究 金融分析

📋 核心要点

  1. 现有方法在构建可验证的基础模型时缺乏有效的框架，导致模型的局部真理保持性不足。
  2. 论文提出ODYSSEY框架，通过foundries的组合来实现可验证的本地真理保持基础模型，增强模型的可靠性和适应性。
  3. ODYSSEY在多种具体foundries上经过全面测试，展示了其在领域构建和工件重放等方面的优越性能。

📝 摘要（中文）

我们介绍了一种名为ODYSSEY的范畴框架，用于构建可验证的、本地真理保持的基础模型。该框架由多个foundries组成，这些构建模块指定了本地上下文的覆盖、本地表示族、限制映射、粘合规则、障碍政策、更新义务和人机交互视图。具体的foundries由通用foundries构建而成，如证据/论证、操作决策、机构/金融、市场意义、科学挑战、研究项目、助手构建和评估工具等。ODYSSEY已在多种具体foundries上全面实施和测试，显示出相同的范畴机制支持领域构建、工件重放、sheaf诊断等多种功能。

🔬 方法详解

问题定义：本论文旨在解决现有基础模型在局部真理保持性和可验证性方面的不足，尤其是在复杂上下文中的应用挑战。

核心思路：ODYSSEY框架通过将foundries作为构建模块，定义了本地上下文的覆盖和表示，从而实现了模型的可验证性和真理保持性。

技术框架：ODYSSEY的整体架构包括foundries的构建、左Kan扩展和右Kan扩展，分别用于整合本地工件和强制执行限制、粘合、障碍及论证条件。

关键创新：最重要的创新在于通过Universal Foundry Learning (UFL)形式化foundry的构建过程，利用范畴理论的工具来增强模型的结构性和可验证性。

关键设计：ODYSSEY使用Foundry SQL (FSQL)作为查询接口，结合TICKET认证机制，确保外部模型的有效整合，提升了系统的灵活性和适应性。

🖼️ 关键图片

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📊 实验亮点

ODYSSEY在多种具体foundries上经过全面测试，展示了其在领域构建和工件重放等方面的优越性能，尤其是在保持局部真理性和可验证性方面，相较于传统方法有显著提升。

🎯 应用场景

该研究的潜在应用领域包括智能决策支持系统、科学研究辅助工具和金融市场分析等。ODYSSEY框架的设计使其能够在多种复杂环境中保持模型的可靠性，未来可能对人工智能领域的基础模型构建产生深远影响。

📄 摘要（原文）

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 component. Concrete foundries are built from generic foundries such as evidence/argument, operational decision, institutional/financial, market meaning, scientific challenge, research-program, assistant-build, and evaluation-harness foundries. Universal Foundry Learning (UFL) formalizes foundry construction as a composition of left and right Kan extensions, with left Kan extension rolling local artifacts into candidate foundries and right Kan extension enforcing the restriction, gluing, obstruction, and argumentation conditions required for promotion. Foundry SQL (FSQL) is a small typed query surface for slicing maintained foundry artifacts that uses TICKET (Topos Integration using Causal Kan Extension Transformers) certification for admitting external or pre-built models into durable ODYSSEY state. ODYSSEY is fully implemented and tested across a wide spectrum of concrete foundries, showing that the same categorical machinery supports domain construction, artifact replay, sheaf diagnostics, grounded Toulmin/local-LLM scrutiny, residual-obstruction ledgers, and optimized TICKET-compatible causal-claim extraction across heterogeneous sources. This paper is to be presented as a 2.5 hour tutorial at ICML 2026. The tutorial home page is at https://bit.ly/4ajS0nA.