A Survey on Self-Improving Test-Time Intelligence: Feedback-Driven Adapting, Learning, and Scaling at Inference
This survey proposes feedback-driven Test-Time Intelligence (TTI), a unified framework for understanding how AI systems improve during deployment. It relates test-time adaptation, test-time learning, and test-time scaling, surveys both state-modifying and computation-scaling approaches, and reviews applications across vision, language, multimodal learning, generative models, robotics, and healthcare, as well as open challenges.
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[Submitted on 1 Sep 2026]
Title:A Survey on Self-Improving Test-Time Intelligence: Feedback-Driven Adapting, Learning, and Scaling at Inference
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Abstract:The ability of AI systems to improve their behavior during deployment is becoming increasingly important. As inference moves beyond the static execution of a fixed trained model, a growing body of work studies how models can refine their behavior on the fly by exploiting test-time information and additional computation. These developments have largely evolved along two directions: methods that modify the model's state using test-time signals, and methods that improve predictions through extra inference-time resources such as more sampling and tool use. However, these directions are often studied in separate communities with different terminology, making their connections harder to see. In this survey, we present feedback-driven Test-Time Intelligence (TTI) as a unified perspective for understanding such deployment-time improvement. We use this view to relate test-time adaptation, test-time learning, and test-time scaling, highlighting both their distinctions and their growing overlap in hybrid systems. This unified framework helps connect previously fragmented ideas and provides a clearer conceptual foundation for studying inference-time self-improvement. We review major methodological paradigms, representative applications, and open challenges across vision, language, multimodal learning, generative models, robotics, and healthcare. Our goal is to provide a coherent foundation and research roadmap for the study of self-improving AI systems at test time.
Comments: accepted by Machine Intelligence Research
Subjects:
Machine Learning (cs.LG)
Cite as: arXiv:2609.01679 [cs.LG]
(or arXiv:2609.01679v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2609.01679
arXiv-issued DOI via DataCite
Related DOI:
https://doi.org/10.1007/s11633-026-1694-1
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From: Shuaicheng Niu [view email] [v1] Tue, 1 Sep 2026 11:41:39 UTC (4,054 KB)
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