ClearFairy: Capturing Creative Workflows through Decision Structuring, In-Situ Questioning, and Rationale Inference

• URL: http://arxiv.org/abs/2509.14537v1
• Date: Thu, 18 Sep 2025 02:11:34 GMT
• Title: ClearFairy: Capturing Creative Workflows through Decision Structuring, In-Situ Questioning, and Rationale Inference
• Authors: Kihoon Son, DaEun Choi, Tae Soo Kim, Young-Ho Kim, Sangdoo Yun, Juho Kim,
• Abstract summary: We present CLEAR framework that structures reasoning into cognitive decision steps-linked units of actions, artifacts, and self-explanations.<n>We introduce ClearFairy, a think-aloud AI assistant for UI design that detects weak explanations, asks lightweight clarifying questions, and infers missing rationales to ease the knowledge-sharing burden.
• Score: 59.65947911667229
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Capturing professionals' decision-making in creative workflows is essential for reflection, collaboration, and knowledge sharing, yet existing methods often leave rationales incomplete and implicit decisions hidden. To address this, we present CLEAR framework that structures reasoning into cognitive decision steps-linked units of actions, artifacts, and self-explanations that make decisions traceable. Building on this framework, we introduce ClearFairy, a think-aloud AI assistant for UI design that detects weak explanations, asks lightweight clarifying questions, and infers missing rationales to ease the knowledge-sharing burden. In a study with twelve creative professionals, 85% of ClearFairy's inferred rationales were accepted, increasing strong explanations from 14% to over 83% of decision steps without adding cognitive demand. The captured steps also enhanced generative AI agents in Figma, yielding next-action predictions better aligned with professionals and producing more coherent design outcomes. For future research on human knowledge-grounded creative AI agents, we release a dataset of captured 417 decision steps.

Related papers

• Intelligent Reasoning Cues: A Framework and Case Study of the Roles of AI Information in Complex Decisions [10.853817540556348]
  We study the role of eight types of reasoning cues in a high-stakes clinical decision.<n>We find that reasoning cues have distinct patterns of influence that can directly inform design.<n>Our results suggest that reasoning cues should prioritize tasks with high variability and discretion, adapt to ensure compatibility with evolving decision needs, and provide complementary, rigorous insights on complex cases.
  arXiv  Detail & Related papers  (2026-01-30T19:22:23Z)
• CogRec: A Cognitive Recommender Agent Fusing Large Language Models and Soar for Explainable Recommendation [4.845415998340928]
  Large Language Models (LLMs) have demonstrated a remarkable capacity in understanding user preferences for recommendation systems.<n>They are constrained by several critical challenges, including their inherent "Black-Box" characteristics, susceptibility to knowledge hallucination, and limited online learning capacity.<n>We propose a novel cognitive recommender agent called CogRec which synergizes the strengths of LLMs with the Soar cognitive architecture.
  arXiv  Detail & Related papers  (2025-12-30T09:50:50Z)
• LLMs for Explainable Business Decision-Making: A Reinforcement Learning Fine-Tuning Approach [9.942833203981069]
  Large language models (LLMs) present an opportunity to generate natural-language explanations.<n>We introduce LEXMA, a reinforcement-learning-based fine-tuning framework that produces audience-appropriate explanations.<n>LEXMA yields significant improvements in predictive performance compared with other LLM baselines.
  arXiv  Detail & Related papers  (2025-12-10T04:16:31Z)
• Cognitive Decision Routing in Large Language Models: When to Think Fast, When to Think Slow [0.0]
  Large Language Models (LLMs) face a fundamental challenge in deciding when to rely on rapid, intuitive responses versus engaging in slower, more deliberate reasoning.<n>Inspired by Daniel Kahneman's dual-process theory and his insights on human cognitive biases, we propose a novel Cognitive Decision Routing framework.
  arXiv  Detail & Related papers  (2025-08-17T01:07:58Z)
• Two Experts Are All You Need for Steering Thinking: Reinforcing Cognitive Effort in MoE Reasoning Models Without Additional Training [86.70255651945602]
  We introduce a novel inference-time steering methodology called Reinforcing Cognitive Experts (RICE)<n>RICE aims to improve reasoning performance without additional training or complexs.<n> Empirical evaluations with leading MoE-based LRMs demonstrate noticeable and consistent improvements in reasoning accuracy, cognitive efficiency, and cross-domain generalization.
  arXiv  Detail & Related papers  (2025-05-20T17:59:16Z)
• Cognitive LLMs: Towards Integrating Cognitive Architectures and Large Language Models for Manufacturing Decision-making [51.737762570776006]
  LLM-ACTR is a novel neuro-symbolic architecture that provides human-aligned and versatile decision-making. Our framework extracts and embeds knowledge of ACT-R's internal decision-making process as latent neural representations. Our experiments on novel Design for Manufacturing tasks show both improved task performance as well as improved grounded decision-making capability.
  arXiv  Detail & Related papers  (2024-08-17T11:49:53Z)
• Rational Decision-Making Agent with Internalized Utility Judgment [88.01612847081677]
  Large language models (LLMs) have demonstrated remarkable advancements and have attracted significant efforts to develop LLMs into agents capable of executing intricate multi-step decision-making tasks beyond traditional NLP applications.<n>This paper proposes RadAgent, which fosters the development of its rationality through an iterative framework involving Experience Exploration and Utility Learning.<n> Experimental results on the ToolBench dataset demonstrate RadAgent's superiority over baselines, achieving over 10% improvement in Pass Rate on diverse tasks.
  arXiv  Detail & Related papers  (2023-08-24T03:11:45Z)
• Inverse Online Learning: Understanding Non-Stationary and Reactionary Policies [79.60322329952453]
  We show how to develop interpretable representations of how agents make decisions. By understanding the decision-making processes underlying a set of observed trajectories, we cast the policy inference problem as the inverse to this online learning problem. We introduce a practical algorithm for retrospectively estimating such perceived effects, alongside the process through which agents update them. Through application to the analysis of UNOS organ donation acceptance decisions, we demonstrate that our approach can bring valuable insights into the factors that govern decision processes and how they change over time.
  arXiv  Detail & Related papers  (2022-03-14T17:40:42Z)
• Leveraging Expert Consistency to Improve Algorithmic Decision Support [62.61153549123407]
  We explore the use of historical expert decisions as a rich source of information that can be combined with observed outcomes to narrow the construct gap. We propose an influence function-based methodology to estimate expert consistency indirectly when each case in the data is assessed by a single expert. Our empirical evaluation, using simulations in a clinical setting and real-world data from the child welfare domain, indicates that the proposed approach successfully narrows the construct gap.
  arXiv  Detail & Related papers  (2021-01-24T05:40:29Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.