AGENTIC-SYSTEMSJuly 13, 20261 min read1 views

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

A new study proposes a semantic framework to distinguish between actual facts and AI-generated representations. By defining common failures like extrapolation or source mismatch, this framework aims to evaluate AI systems based on reliable claims rather than apparent fluency.

Computer Science > Artificial Intelligence

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

View PDF HTML (experimental)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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Source: arXiv cs.AI Recent

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