In large language model (LLM) systems, reliability is typically framed as a function of model capability. However, our research demonstrates that reliability is significantly influenced by inference-time control—the computational layer governing task framing and context selection. We introduce CogniConsole, an architectural instantiation that externalizes this control into a structured interface combining programmatic coordination with bounded prompt-based reasoning.
Through controllability-oriented probes ($N=489$) in a multi-step interactive environment, we show that increasing structural scaffolding—from unstructured to fully scaffolded—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.
Blogger's Review: The introduction of CogniConsole offers a fresh perspective on LLM reliability by externalizing inference-time control, significantly enhancing model stability and output consistency. This approach not only lays the groundwork for developing more efficient LLM architectures but also opens up new possibilities for future research.