CogniConsole: Externalizing Inference-Time Control as a Formal Abstraction for Reliable LLM Interactions
06:00 · July 13, 2026 · arXiv cs.AI RSS
Reliability in large language model (LLM) systems is typically framed as a function of model capability. We challenge this by demonstrating that reliability is significantly influenced by \emph{inference-time control} -- the computational layer governing task framing and context selection. We introduce \emph{CogniConsole}, an architectural instantiation that externalizes this control into a structured interface combining programmatic coordination with bounded prompt-based reasoning. Through \emph{controllability-oriented probes} ($N=489$) in a multi-step interactive environment, we show that increasing structural scaffolding -- from unstructured to fully scaffolded -- \textbf{systematically reduces output variance and failure rates under a fixed model architecture}. Our results indicate that many observed failure modes, such as context drift and inconsistent constraint adherence, arise from under-specified control rather than insufficient capability. This work provides an empirical basis for treating inference-time control as a first-class abstraction, opening new directions for designing and evaluating LLM systems beyond scaling alone.
Summary
The article challenges the prevailing view that LLM reliability stems primarily from model scale, training data, or alignment. Instead, it identifies inference-time control—the mechanisms that frame tasks, select context, and coordinate reasoning steps—as a distinct and under-specified computational layer. Failures such as context drift, inconsistent constraint adherence, and output variance often arise when this layer remains implicit inside monolithic prompts, forcing the model to arbitrate competing objectives within a single probabilistic generation process.
To address these issues, the authors introduce CogniConsole, an architectural framework that externalizes inference-time control. The system decomposes interactions into task-scoped components defined by explicit specifications: behavioral roles, salient inputs, decision protocols, and output contracts. Programmatic structure defines the overall decision space, while prompts supply bounded reasoning within those bounds. This separation allows control logic to be varied independently of any particular prompt realization.
Empirical support comes from controllability-oriented probes conducted in a multi-step interactive environment. The results show that progressively increasing structural scaffolding—from unstructured prompts to fully scaffolded configurations—systematically lowers both output variance and failure rates, even when the underlying model architecture remains fixed. The work therefore supplies concrete evidence that many observed instabilities reflect deficiencies in control design rather than limits of model capacity.
By formalizing inference-time control as an explicit abstraction, the paper opens a path for more systematic design and evaluation of LLM systems that does not rely solely on further scaling.
Why it matters
This research is highly relevant for Dutch AI researchers and engineers building enterprise LLM systems, as it offers a concrete methodology to improve AI reliability and predictability. This aligns strongly with the Netherlands' and EU's regulatory focus on transparent, trustworthy, and controllable AI systems without requiring massive computational resources for model scaling.
