CogniConsole: Externalizing Inference-Time Control as a Formal Abstraction for Reliable LLM Interactions
A new study challenges the notion that LLM reliability stems solely from model capability, showing that inference-time control plays a key role. The CogniConsole architecture externalizes this control into a structured interface that combines programmatic coordination with bounded prompt-based reasoning. Experiments with 489 probes demonstrate that increasing structural scaffolding systematically reduces output variance and failure rates, suggesting many failures are due to under-specified control.
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[Submitted on 21 Apr 2026]
Title:CogniConsole: Externalizing Inference-Time Control as a Formal Abstraction for Reliable LLM Interactions
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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.
Comments: Revised version focusing on the CogniConsole system architecture and empirical evaluation of inference-time control probes (N=489)
Subjects:
Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC)
ACM classes: H.5; I.2
Cite as: arXiv:2607.08774 [cs.AI]
(or arXiv:2607.08774v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2607.08774
arXiv-issued DOI via DataCite
Submission history
From: Vanessa Figueiredo [view email] [v1] Tue, 21 Apr 2026 17:21:06 UTC (566 KB)
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