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[ARTICLE Ā· art-56799] src=arxiv.org ↗ pub= topic=artificial-intelligence verified=true sentiment=Ā· neutral

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

Researchers propose a semantic framework to evaluate AI system outputs by distinguishing justified claims from unsupported ones, defining failures like extrapolation and refuted assertions. The framework aims to ensure AI outputs are grounded in reliable knowledge rather than apparent fluency.

read1 min views1 publishedJul 13, 2026

arXiv:2607.09489v1 Announce Type: new Abstract: 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.

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