🤖 AI Summary
This work addresses a critical limitation in current AI interaction paradigms, which treat prompts as the primary unit of exchange while overlooking users’ underlying source intentions. The paper introduces Intent Signal Theory (IST), the first formal framework to articulate the multi-layered structure of user intent, distinguishing between source intent, intent proxies, encoded carriers, and model outputs, and establishes the Irreversible Intent Loss Theorem. By reframing prompt engineering as intent protocol design, IST reveals a missing computational layer in contemporary systems. Empirical validation across four studies, six large language models, three languages, and three task domains confirms core theoretical predictions, including structure–fidelity decoupling, metric disentanglement, and weight tolerance.
📝 Abstract
Current AI interaction models treat the prompt as the primary object of exchange, omitting a critical layer: the user's latent source intent, the goal state preceding and motivating the prompt. Here we introduce Intent Signal Theory (IST), a computational framework that formalises this missing intent layer. IST distinguishes four objects routinely conflated: latent source intent (I*), observable intent proxy (I-hat), encoded carrier (P), and model output (O). It formalises dimensional weights, encoding masks, structural and fidelity recovery scores, and public-private intent decomposition. The Theorem of Irreversible Intent Loss establishes that private intent absent from the carrier cannot be recovered beyond generic substitution. Evidence from four companion studies spanning six LLMs, three languages and three task domains shows structural-fidelity splits, human-validated metric dissociation, and weight-tolerance plateaus consistent with IST's predictions. IST reframes prompt engineering as intent-protocol design and identifies a computational layer that current AI systems lack.