# Ceci n'est pas une pipe: AI systems as semantic abstractions

arXiv: https://arxiv.org/abs/2607.09489 · HTML: https://mlsanity.com/paper/2607.09489/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 31 of its 106 references that are in the map.
- First posted on arXiv 10 Jul 2026.

## Builds on (most cited first, 30 of 31)

- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [SWE-bench: Can Language Models Resolve Real-World GitHub Issues?](https://mlsanity.com/paper/2310.06770/) (2023)
- [A Survey on Large Language Model based Autonomous Agents](https://mlsanity.com/paper/2308.11432/) (2023)
- [Survey of Hallucination in Natural Language Generation](https://mlsanity.com/paper/2202.03629/) (2022)
- [Language Models as Knowledge Bases?](https://mlsanity.com/paper/1909.01066/) (2019)
- [Gorilla: Large Language Model Connected with Massive APIs](https://mlsanity.com/paper/2305.15334/) (2023)
- [Enabling Large Language Models to Generate Text with Citations](https://mlsanity.com/paper/2305.14627/) (2023)
- [Identifying the Risks of LM Agents with an LM-Emulated Sandbox](https://mlsanity.com/paper/2309.15817/) (2023)
- [Control Barrier Functions: Theory and Applications](https://mlsanity.com/paper/1903.11199/) (2019)
- [Measuring Attribution in Natural Language Generation Models](https://mlsanity.com/paper/2112.12870/) (2021)
- [AgentSpec: Customizable Runtime Enforcement for Safe and Reliable LLM Agents](https://mlsanity.com/paper/2503.18666/) (2025)
- [Progent: Securing AI Agents with Privilege Control](https://mlsanity.com/paper/2504.11703/) (2025)
- [IsolateGPT: An Execution Isolation Architecture for LLM-Based Agentic Systems](https://mlsanity.com/paper/2403.04960/) (2024)
- [Attributed Question Answering: Evaluation and Modeling for Attributed Large Language Models](https://mlsanity.com/paper/2212.08037/) (2022)
- [Prompting Is Programming: A Query Language for Large Language Models](https://mlsanity.com/paper/2212.06094/) (2022)
- [Introduction to Neural Network Verification](https://mlsanity.com/paper/2109.10317/) (2021)
- [A Review of Formal Methods applied to Machine Learning](https://mlsanity.com/paper/2104.02466/) (2021)
- [Run Time Assurance for Safety-Critical Systems: An Introduction to Safety Filtering Approaches for Complex Control Systems](https://mlsanity.com/paper/2110.03506/) (2021)
- [The Fusion of Large Language Models and Formal Methods for Trustworthy AI Agents: A Roadmap](https://mlsanity.com/paper/2412.06512/) (2024)
- [Prompts Are Programs Too! Understanding How Developers Build Software Containing Prompts](https://mlsanity.com/paper/2409.12447/) (2024)
- [A Survey and Classification of Controlled Natural Languages](https://mlsanity.com/paper/1507.01701/) (2015)
- [AgentGuard: Runtime Verification of AI Agents](https://mlsanity.com/paper/2509.23864/) (2025)
- [Evaluating LLM-driven User-Intent Formalization for Verification-Aware Languages](https://mlsanity.com/paper/2406.09757/) (2024)
- [Formally Specifying the High-Level Behavior of LLM-Based Agents](https://mlsanity.com/paper/2310.08535/) (2023)
- [Intent Formalization: A Grand Challenge for Reliable Coding in the Age of AI Agents](https://mlsanity.com/paper/2603.17150/) (2026)
- [SAIL: Sound Abstract Interpreters with LLMs](https://mlsanity.com/paper/2511.13663/) (2025)
- [Composing Verifiable Conceptual Models via Building Blocks: Towards Design-Time Verification of Agentic AI Workflows](https://mlsanity.com/paper/2606.21565/) (2026)
- [A Logic for Reasoning about Evidence](https://mlsanity.com/paper/1407.7185/) (2014)
- [Formal-Method-Guided Vibe Coding: Closing the Verification Loop on AI-Generated Safety-Critical Software Through Model-Driven Engineering](https://mlsanity.com/paper/2606.22413/) (2026)

## Abstract (arXiv)

An AI system's output is not the fact or world state it appears to describe, but rather an engineered representation. We propose a semantic framework to describe AI systems, to be able to examine the correctness of such representations. To do so, we distinguish what is justified by accepted domain knowledge, what reference sources say, and what the system can currently use. This allows us to give precise definitions to common failures: extrapolation, refuted or unsupported assertion, sources versus knowledge mismatch, stale or refuted source, added hypotheses, unsupported use... We hope our framework gives a useful vocabulary for specifying and checking AI systems whose outputs, citations, tool calls, and world-changing actions must be justified by reliable claims and explicit authority rather than apparent fluency.