CogniConsole: Externalizing Inference-Time Control as a Formal Abstraction for Reliable LLM Interactions

• URL: http://arxiv.org/abs/2607.08774v1
• Date: Tue, 21 Apr 2026 17:21:06 GMT
• Title: CogniConsole: Externalizing Inference-Time Control as a Formal Abstraction for Reliable LLM Interactions
• Abstract summary: Inference-time control is the computational layer governing task framing and context selection.<n>We introduce emphCogniConsole, an architectural instantiation that externalizes this control into a structured interface.<n>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.
• Score: 3.8673630752805437
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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