CogniConsole: Externalizing Inference-Time Control as a Formal Abstraction for Reliable LLM Interactions
Quick Answer
CogniConsole introduces a structured interface for inference-time control in LLMs, significantly reducing output variance and failure rates through enhanced scaffolding.
Quick Take
Empirical results from 489 probes indicate that many failure modes stem from under-specified control rather than model capability, suggesting a shift in design focus.
Key Points
- CogniConsole externalizes inference-time control into a structured interface.
- 489 controllability-oriented probes demonstrate reduced output variance.
- Enhanced scaffolding leads to lower failure rates in LLM interactions.
- Failure modes like context drift arise from under-specified control.
- This work advocates for treating inference-time control as a key design abstraction.
Paper Resources
📖 Reader Mode
~2 min readAbstract: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)
— Originally published at arxiv.org
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