Adaptivity Under Realizability Constraints: Comparing In-Context and Agentic Learning

2026-05-06 · Source: Takara TLDR - Daily AI Papers · Field: Technology & Digital — Artificial Intelligence & Machine Learning · Depth: Expert, medium

Summary

A study published on May 6, 2026, by Anastasis Kratsios, A. Martina Neuman, and Philipp Petersen, compares in-context learning with fixed queries against agentic learning with adaptive queries for uniform approximation of task families. The research investigates two distinct settings: an unrestricted regime where querying and approximation functions are arbitrary, and a realizable regime where these operations must be implemented by ReLU neural networks. The authors found that adaptivity consistently benefits approximation performance in both regimes. However, the nature of this advantage changes significantly when moving from the unrestricted to the realizable regime. The paper identifies four specific approximation scenarios, each demonstrated by an explicit task family, illustrating how representational constraints profoundly interact with the effects of adaptivity.

Key takeaway

For research scientists designing or evaluating adaptive learning systems, you should critically assess how representational constraints, particularly those imposed by specific neural network architectures like ReLU, impact the benefits of adaptivity. Your choice of learning paradigm (in-context vs. agentic) and its implementation details can lead to vastly different performance outcomes, even for the same task family, necessitating careful scenario-specific analysis.

Key insights

Representational constraints fundamentally alter the benefits of adaptivity in learning systems.

Principles

• Adaptivity always improves approximation performance.
• Realizability constraints can shift adaptive advantages.

Method

The study compares fixed-query in-context learning with adaptive-query agentic learning for uniform approximation, across unrestricted and ReLU neural network-realizable regimes, identifying four distinct approximation scenarios.

In practice

• Consider ReLU network constraints in adaptive learning.
• Evaluate adaptivity benefits across different task families.

Topics

• In-Context Learning
• Agentic Learning
• Realizability Constraints
• ReLU Neural Networks
• Uniform Approximation

Best for: Research Scientist, AI Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by Takara TLDR - Daily AI Papers.