aiqbench analysis
This arXiv preprint offers a formal, interface-based definition of "any-task inference" foundation models, which map data and task descriptions directly to label predictions without task-specific fine-tuning. It decomposes these models into encoder, model, and decoder components and proposes error terms to assess whether a given design can reach optimality. For readers, the value is a shared vocabulary for reasoning about zero-shot-style systems, though the claims remain theoretical until tested against the five cited real-world examples.
A Theoretical Framework for Understanding and Building "Any-task inference" Capable Foundation Models
arXiv:2610.40934v1 Announce Type: new Abstract: In this work, we provide a formal framework and mathematical definition for the subset of foundation models focused on enabling the property of "any-task inference" (ATI) (similar to zero-shot inference), rather than those designed for pre-training fine-tuning. To achieve this mathematical definition, we formalize the notion of a "space of tasks of interest," which we include in the problem domain, then provide an interface-based formal definition for ATI foundation models as those that map samples of data $x$ and tasks $t$ to predictions over the space of labels directly. While simple, this definition allows us to show that all ATI foundation models can be decomposed through a "universal task reduction" lens into encoder, model, and decoder aspects. We can leverage this decomposition to also decompose foundation model error into terms corresponding to different aspects of this reduction, stratified into a collection of "design terms," which are theoretical properties that can inform one of whether a particular proposed ATI foundation model can achieve optimality or not due to the structure of the proposed task reduction, and "realized model excess" terms which capture interactions between learned and programmatic components of foundation model systems. Lastly, we can use this perspective to offer two "prototypical" recipes for realizing ATI foundation models in diverse domains, which we term generative and discriminative variants of ATI foundation models. This exercise is grounded throughout by 5 real-world examples of foundation models in different settings, varying greatly in how these capabilities are realized, showing the applicability of this definition to real-world settings.
Abstract:In this work, we provide a formal framework and mathematical definition for the subset of foundation models focused on enabling the property of "any-task inference" (ATI) (similar to zero-shot inference), rather than those designed for pre-training fine-tuning. To achieve this mathematical definition, we formalize the notion of a "space of tasks of interest," which we include in the problem domain, then provide an interface-based formal definition for ATI foundation models as those that map samples of data $x$ and tasks $t$ to predictions over the space of labels directly. While simple, this definition allows us to show that all ATI foundation models can be decomposed through a "universal task reduction" lens into encoder, model, and decoder aspects. We can leverage this decomposition to also decompose foundation model error into terms corresponding to different aspects of this reduction, stratified into a collection of "design terms," which are theoretical properties that can inform one of whether a particular proposed ATI foundation model can achieve optimality or not due to the structure of the proposed task reduction, and "realized model excess" terms which capture interactions between learned and programmatic components of foundation model systems. Lastly, we can use this perspective to offer two "prototypical" recipes for realizing ATI foundation models in diverse domains, which we term generative and discriminative variants of ATI foundation models. This exercise is grounded throughout by 5 real-world examples of foundation models in different settings, varying greatly in how these capabilities are realized, showing the applicability of this definition to real-world settings.
Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
arXiv:2610.40934 [cs.LG]
(or arXiv:2610.40934v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2610.40934 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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Full text under the publisher's open licence · 350 words
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