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

Researchers introduced CogniConsole, an architecture that externalizes inference-time control for LLMs through a structured interface combining programmatic coordination with bounded prompt-based reasoning. In a study with 489 controllability-oriented probes, they found that increasing structural scaffolding systematically reduces output variance and failure rates, suggesting many LLM failures stem from under-specified control rather than insufficient capability.

arXiv:2607.08774v1 Announce Type: new 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.