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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 insufficien","title":"CogniConsole: Externalizing Inference-Time Control as a Formal Abstraction for Reliable LLM Interactions","url":"https://arxiv.org/abs/2607.08774","vendor":"arxiv_cs_ai"},"summary":"arXiv:2607.08774v1 Announce Type: new \nAbstract: 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. 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