mlsanityData to 14 Oct 2026

Odyssey: Constructing Verifiable Local Truth-Preserving Foundation Models

arXiv 2606.27593 ↗First posted 25 Jun 2026cs.AI cs.LGcited by 1

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Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 17 of its 34 references that are in the map. First posted on arXiv 25 Jun 2026.

Builds on

17 of 34 references.

References · 17

  1. 2021On the Opportunities and Risks of Foundation Modelscited by 4,033
  2. 2025The AI Scientist-v2: Workshop-Level Automated Scientific Discovery via Agentic Tree Searchcited by 422
  3. 2023Causal Reasoning and Large Language Models: Opening a New Frontier for Causalitycited by 353
  4. 2021Causal Inference in Natural Language Processing: Estimation, Prediction, Interpretation and Beyondcited by 178
  5. 2020The IKEA ASM Dataset: Understanding People Assembling Furniture through Actions, Objects and Posecited by 95
  6. 2024Agent Design Pattern Catalogue: A Collection of Architectural Patterns for Foundation Model based Agentscited by 35
  7. 2024Multi-Agent Causal Discovery Using Large Language Modelscited by 26
  8. 2021A Survey on Extraction of Causal Relations from Natural Language Textcited by 25
  9. 2024An overview of domain-specific foundation model: key technologies, applications and challengescited by 19
  10. 2024Training and Serving System of Foundation Models: A Comprehensive Surveycited by 11
  11. 2025Large Causal Models from Large Language Modelscited by 8
  12. 2024A Survey of Event Causality Identification: Taxonomy, Challenges, Assessment, and Prospectscited by 6
5 more
  1. 2026Latent Confounded Causal Discovery via Lie Bracket Geometrycited by 4
  2. 2026PROMETHEUS: Automating Deep Causal Research Integrating Text, Data and Modelscited by 3
  3. 2026Kan Extension Transformers: A Categorical Unification of Attention, Diffusion, and Predict-Detach Self-Conditioningcited by 3
  4. 2026How Non-Linguistic Is the Indus Sign System? A Synthetic-Baseline Scorecardcited by 1
  5. 2026TRACE: Toulmin-based Reasoning Assessment through Constructive Elements for LLM CoT Evaluationcited by 1

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