CogInstrument: Modeling Cognitive Processes for Bidirectional Human-LLM Alignment in Planning Tasks

2026-04-12

AI Summary

This study addresses the challenge that current large language model (LLM) interfaces fail to explicitly represent users’ internal reasoning structures, leading to cognitive misalignment during collaborative task planning. To bridge this gap, the authors propose a novel graphical approach based on editable “cognitive primitives,” introducing them as fundamental units for human–AI alignment. By integrating natural language understanding with causal graph modeling, the system extracts and visualizes users’ implicit causal reasoning chains from dialogue. This enables structured, revisable, and reusable bidirectional reasoning collaboration. A user study (N=12) demonstrates that, compared to conventional conversational interfaces, the proposed method more effectively surfaces latent reasoning structures, thereby enhancing users’ ability to verify LLM-generated logic, improve revision accuracy, and foster trust in collaborative interactions.

Technology Category

• Humans and AI: Intelligent User Interfaces
• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Although Large Language Models (LLMs) demonstrate proficiency in knowledge-intensive tasks, current interfaces frequently precipitate cognitive misalignment by failing to externalize users' underlying reasoning structures. Existing tools typically represent intent as "flat lists," thereby disregarding the causal dependencies and revisable assumptions inherent in human decision-making. We introduce CogInstrument, a system that represents user reasoning through cognitive motifs-compositional, revisable units comprising concepts linked by causal dependencies. CogInstrument extracts these motifs from natural language interactions and renders them as editable graphical structures to facilitate bidirectional alignment. This structural externalization enables both the user and the LLM to inspect, negotiate, and reconcile reasoning processes iteratively. A within-subjects study (N=12) demonstrates that CogInstrument explicitly surfaces implicit reasoning structures, facilitating more targeted revision and reusability over conventional LLM-based dialogue interfaces. By enabling users to verify the logical grounding of LLM outputs, CogInstrument significantly enhances user agency, trust, and structural control over the collaboration. This work formalizes cognitive motifs as a fundamental unit for human-LLM alignment, providing a novel framework for achieving structured, reasoning-based human-AI collaboration.

Problem

• cognitive alignment
• reasoning structures
• human-LLM collaboration
• causal dependencies
• planning tasks

Innovation

• cognitive motifs
• bidirectional alignment
• reasoning externalization
• human-LLM collaboration
• causal dependency