AGENTIC-SYSTEMSJuly 13, 20261 min read

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

A new study introduces CogniConsole, an architecture that externalizes inference-time control to systematically reduce output variance and failure rates in LLMs. The research demonstrates that LLM reliability is heavily influenced by structured control layers rather than model capability alone.

Computer Science > Artificial Intelligence

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

View PDF HTML (experimental)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.

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Source: arXiv cs.AI Recent

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