[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-82038-en":3,"doc-seo-82038-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":13,"seo_description":14,"update_tm":28,"read_time":29},82038,7971461740909,"Levi","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","CogniConsole Externalizing Inference-Time Control as a Formal Abstraction for Reliable LLM Interactions","Reliability in large language model (LLM) systems is typically attributed to model capability, but this work shows that inference-time control plays a decisive role. It introduces CogniConsole, an architectural approach that externalizes inference-time control into a structured interface combining programmatic coordination with bounded prompt-based reasoning. Controllability-oriented probes (N=489) in multi-step interactive settings demonstrate that stronger structural scaffolding reduces output variance and failure rates, pointing to control under-specification—rather than limited capability—as a driver of issues such as context drift and inconsistent constraint adherence.","CogniConsole: Externalizing Inference-Time Control as a Formal Abstraction for Reliable LLM Interactions  \nVanessa Figueiredo∗†  \nDepartment of Computer Science University of Regina Regina, SK, Canada [vanessa.figueiredo@uregina.ca](vanessa.figueiredo@uregina.ca)  \nWilter Franceschi∗†  \nOrbital Sea Regina, SK, Canada [contact@orbitalsea.com](contact@orbitalsea.com)  \narXiv :2607 .08774v1 [ cs .AI] 21 Apr 2026  \nAbstract  \nReliability 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 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.  \nThrough 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 providesan empirical basis for treating inference-time control as a first-class abstraction, opening new directions for designing and evaluating LLM systems beyond scaling alone.  \n1 Introduction  \nA central assumption in modern language model research is that reliability is a function of model capability. When large language models (LLMs) fail through hallucination, instability, or inconsistency, the explanation is typically sought in insufficient scale, imperfect training data, or incomplete alignment [3, 4, 6, 14] . This assumption has driven a dominant paradigm: improving models by making them larger, better trained, and more aligned.  \nWe argue that this framing is incomplete. In practice, interacting with LLMs reveals a different pattern where the same model can exhibit dramatically different behaviors under small changes in prompt structure, context ordering, or interaction history [16, 18] . Even highly capable models remain sensitive, unstable, and difficult to debug, particularly in long-context and multi-step settings [8, 11, 19, 20] . These failures persist despite improvements in scale, suggesting that reliability is not solely a property of what the model knows, but of how it is guided to decide.  \nThis points to a missing abstraction. Across both academic and industrial systems, a consistent set of design patterns has emerged: prompts define roles, encode reasoning procedures, impose constraints, and act as executable policies [12, 21, 19] . Agent-based systems decompose tasks into smaller units, while structured prompting introduces intermediate reasoning steps. Even being currently applied separately, these practices implicitly construct a layer of inference-time control on top of pretrained models. Yet, this layer remains informal, under-specified, and largely untheorized.  \n∗ Equal contribution.  \n†Implementation available at: [https://github.com/Cogniconsole/cogniconsole](https://github.com/Cogniconsole/cogniconsole).  \nPreprint.  \nWe consider that many observed failure modes (e.g., instability, context sensitivity, and lack of reproducibility) are consequences of deficiencies in this control layer rather than limitations of model capacity. In current systems, control is typically embedded in monolithic prompts, where multiple tasks, reasoning strategies, and decision rules are co-located [15] . As a result, models must internally arbitrate between competing objectives within a probabilistic next-token process, leading to variability in reasoning trajectories.  \nFundamentally, effective control arises from the coordinated interaction between prompt structure and program","cbCaih0Oh8ort7pQ","https://ap.wps.com/l/cbCaih0Oh8ort7pQ","pdf",993821,2,1,16,"English","en",105,"# Introduction\n## Failure Modes in Current LLM Control Paradigms","[{\"question\":\"What does the paper argue is the main driver of reliability in LLM systems?\",\"answer\":\"It argues that reliability is significantly influenced by inference-time control, not only by model capability.\"},{\"question\":\"What is CogniConsole?\",\"answer\":\"CogniConsole is an architectural instantiation that externalizes inference-time control into a structured interface with programmatic coordination and bounded prompt-based reasoning.\"},{\"question\":\"How do structural scaffolding changes affect outcomes in the experiments?\",\"answer\":\"Increasing structural scaffolding from unstructured to fully scaffolded systematically reduces output variance and failure rates under a fixed model 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