Research proposes CogniConsole to formalize inference-time control for more reliable LLM interactions

A new paper on arXiv proposes treating inference-time control as a first-class abstraction in LLM systems. The authors argue that reliability is not solely a function of model capability, but is significantly influenced by the computational layer responsible for task framing and context selection.

The paper introduces CogniConsole, an architectural instantiation that externalizes inference-time control into a structured interface. This interface combines programmatic coordination with bounded prompt-based reasoning to govern how an LLM interacts with tasks and context.

To evaluate the approach, the authors conducted controllability-oriented probes (N=489) in a multi-step interactive environment. The experiments varied the level of structural scaffolding—from unstructured to fully scaffolded—and measured its impact on output variance and failure rates under a fixed model architecture.

The results indicate that increasing structural scaffolding systematically reduces output variance and failure rates. The authors attribute this improvement to better-defined control mechanisms, suggesting that many observed failure modes—such as context drift and inconsistent constraint adherence—arise from under-specified control rather than insufficient model capability.

The work provides an empirical basis for formalizing inference-time control as a design and evaluation axis for LLM systems, opening directions beyond model scaling alone.