Adaptivity Under Realizability Constraints: Comparing In-Context and Agentic Learning
A new theoretical framework reveals how adaptive querying in agentic systems compares to fixed in-context learning when constrained by neural network implementability. The work identifies four distinct scenarios where ReLU realizability can either preserve or eliminate adaptivity advantages, suggesting that practical deployment constraints fundamentally reshape the learning-efficiency tradeoffs that appear optimal in unconstrained settings. This matters for practitioners building production agents, as it implies that theoretical gains from adaptive strategies may vanish once compiled into actual neural architectures.
Modelwire context
ExplainerThe paper's core finding is not just that constraints matter, but that they matter asymmetrically: four specific scenarios exist where implementability either preserves or eliminates adaptive advantages. This is narrower and more actionable than the usual 'theory-practice gap' observation.
This connects directly to the position paper from early May arguing that agentic systems should embed Bayesian decision theory in control layers rather than LLM inference. That work assumed principled reasoning under uncertainty was architecturally feasible; this new paper asks what remains feasible once you compile that reasoning into actual ReLU networks. The constraint-guided execution work (RunAgent) also trades expressiveness for determinism, but that's a deliberate design choice. Here, the loss of adaptivity is involuntary, driven by what neural networks can actually represent. The gap between what Bayesian orchestration promises and what ReLU networks can implement is now quantified.
If the paper's four scenarios are validated empirically on a production agent codebase (e.g., does adaptive tool selection actually collapse to fixed routing once compiled into a deployed model?), that confirms the theory has teeth. If practitioners report no such collapse, the realizability constraints may be loose enough in practice that the theoretical result doesn't matter for real systems.
Coverage we drew on
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MentionsReLU neural networks · in-context learning · agentic learning
Modelwire Editorial
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