Cognitive-Level Adaptive Generation via Capability-Aware Retrieval and Style Adaptation

• URL: http://arxiv.org/abs/2509.19336v1
• Date: Mon, 15 Sep 2025 10:11:25 GMT
• Title: Cognitive-Level Adaptive Generation via Capability-Aware Retrieval and Style Adaptation
• Authors: Qingsong Wang, Tao Wu, Wang Lin, Yueying Feng, Gongsheng Yuan, Chang Yao, Jingyuan Chen,
• Abstract summary: Large Language Models (LLMs) have demonstrated strong performance in open-ended generation tasks.<n>They often struggle to adapt content to users with differing cognitive capacities, leading to a phenomenon we term cognitive misalignment.<n>We propose the Cognitive-Level Alignment Framework (CLAF) to align knowledge complexity and presentation style with user cognition.
• Score: 36.622949794875076
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large Language Models (LLMs) have demonstrated strong performance in open-ended generation tasks. However, they often struggle to adapt content to users with differing cognitive capacities, leading to a phenomenon we term cognitive misalignment. This issue arises in two forms: knowledge-level misalignment, where content is too complex or too simplistic relative to user understanding, and presentation-style misalignment, where the structure or tone hinders effective comprehension. To address these challenges, we propose the Cognitive-Level Alignment Framework (CLAF), a general-purpose generation framework that aligns both knowledge complexity and presentation style with user cognition. CLAF integrates a capability-aware retrieval module based on a hierarchical knowledge graph and a style optimization module guided by Bloom's taxonomy and preference learning. Additionally, a knowledge-controllable generation component ensures consistency and relevance throughout the output. To support training and evaluation, we construct SCALE, a cognitively annotated dataset containing responses at multiple comprehension levels per query. Empirical results show that CLAF enhances the adaptability and informativeness of LLM outputs across a range of user profiles, offering a robust solution to cognitive-level alignment in real-world applications.

Related papers

• Towards LLM-Empowered Knowledge Tracing via LLM-Student Hierarchical Behavior Alignment in Hyperbolic Space [24.868649493405528]
  Knowledge Tracing (KT) diagnoses students' concept mastery through continuous learning state monitoring in education.<n>Existing methods rely on ID-based sequences or shallow textual features.<n>This paper proposes a Large Language Model Hyperbolic Aligned Knowledge Tracing framework.
  arXiv  Detail & Related papers  (2026-02-26T11:17:31Z)
• Consistency-Aware Editing for Entity-level Unlearning in Language Models [53.522931419965424]
  We introduce a novel consistency-aware editing (CAE) framework for entity-level unlearning.<n>CAE aggregates a diverse set of prompts related to a target entity, including its attributes, relations, and adversarial paraphrases.<n>It then jointly learns a low-rank update guided by a consistency regularizer that aligns the editing directions across prompts.
  arXiv  Detail & Related papers  (2025-12-19T15:18:07Z)
• Representation Interventions Enable Lifelong Unstructured Knowledge Control [54.86207134539453]
  Large language models (LLMs) often produce incorrect or outdated content. Updating their knowledge efficiently and accurately without costly retraining is a major challenge.<n>We introduce RILKE, a robust and scalable method that treats knowledge control as interventions within the model's representation space.<n>During training, RILKE learns paraphrase-robust and edit-localized modules that limit each update to a low-dimensional subspace to minimize cross-edit interference.<n>In inference, a query-adaptive router selects the appropriate module to guide the model's generation.
  arXiv  Detail & Related papers  (2025-11-25T22:15:00Z)
• CLLMRec: LLM-powered Cognitive-Aware Concept Recommendation via Semantic Alignment and Prerequisite Knowledge Distillation [3.200298153814017]
  The growth of Massive Open Online Courses (MOOCs) presents significant challenges for personalized learning, where concept is crucial.<n>Existing approaches typically rely on heterogeneous information networks or knowledge graphs to capture conceptual relationships, combined with knowledge tracing models to assess learners' cognitive states.<n>This paper proposes CLLMRec, a novel framework that leverages Large Language Models to generate personalized concept recommendations.
  arXiv  Detail & Related papers  (2025-11-21T08:37:39Z)
• CAM: A Constructivist View of Agentic Memory for LLM-Based Reading Comprehension [55.29309306566238]
  Current Large Language Models (LLMs) are confronted with overwhelming information volume when comprehending long-form documents.<n>This challenge raises the imperative of a cohesive memory module, which can elevate vanilla LLMs into autonomous reading agents.<n>We draw inspiration from Jean Piaget's Constructivist Theory, illuminating three traits of the agentic memory -- structured schemata, flexible assimilation, and dynamic accommodation.
  arXiv  Detail & Related papers  (2025-10-07T02:16:30Z)
• Federated Cross-Modal Style-Aware Prompt Generation [2.4472081831862655]
  FedCSAP produces context-aware prompt tokens that are both distinct and non-redundant.<n>Our framework harnesses low, mid, and high-level features from CLIP's vision encoder alongside client-specific style indicators.
  arXiv  Detail & Related papers  (2025-08-17T15:23:45Z)
• An Empirical Study of Federated Prompt Learning for Vision Language Model [89.2963764404892]
  This paper systematically investigates the behavioral differences between language prompt learning (VPT) and vision prompt learning (VLM)<n>We evaluate the impact of various FL and prompt configurations, such as client scale, aggregation strategies, and prompt length, to assess the robustness of Federated Prompt Learning (FPL)
  arXiv  Detail & Related papers  (2025-05-29T03:09:15Z)
• DesCLIP: Robust Continual Learning via General Attribute Descriptions for VLM-Based Visual Recognition [27.429425225892675]
  Continual learning of vision-language models (VLMs) focuses on leveraging cross-modal pretrained knowledge to incrementally adapt to expanding downstream tasks and datasets.<n>Existing research often focuses on connecting visual features with specific class text in downstream tasks, overlooking the latent relationships between general and specialized knowledge.<n>We propose DesCLIP, which leverages general attribute (GA) descriptions to guide the understanding of specific class objects.
  arXiv  Detail & Related papers  (2025-02-02T01:06:02Z)
• Compositional Zero-Shot Learning with Contextualized Cues and Adaptive Contrastive Training [17.893694262999826]
  This paper introduces a novel framework, Understanding and Linking Attributes and Objects (ULAO) in Compositional Zero-Shot Learning (CZSL)<n>ULAO comprises two innovative modules. The Understanding Attributes and Objects (UAO) module improves primitive understanding by sequential primitive prediction and leveraging recognized objects as contextual hints for attribute classification.<n>The Linking Attributes and Objects (LAO) module improves the attribute-object linkage understanding through a new contrastive learning strategy that incorporates tailored hard negative generation and adaptive loss adjustments.
  arXiv  Detail & Related papers  (2024-12-10T03:41:20Z)
• Composite Learning Units: Generalized Learning Beyond Parameter Updates to Transform LLMs into Adaptive Reasoners [0.0]
  We introduce Composite Learning Units (CLUs) designed to transform reasoners into learners capable of continuous learning. CLUs are built on an architecture that allows a reasoning model to maintain and evolve a dynamic knowledge repository. We demonstrate CLUs' effectiveness through a cryptographic reasoning task, where they continuously evolve their understanding through feedback to uncover hidden transformation rules.
  arXiv  Detail & Related papers  (2024-10-09T02:27:58Z)
• HPT++: Hierarchically Prompting Vision-Language Models with Multi-Granularity Knowledge Generation and Improved Structure Modeling [39.14392943549792]
  We propose a novel approach called Hierarchical Prompt Tuning (HPT), enabling simultaneous modeling of both structured and conventional linguistic knowledge. We introduce a relationship-guided attention module to capture pair-wise associations among entities and attributes for low-level prompt learning. By incorporating high-level and global-level prompts modeling overall semantics, the proposed hierarchical structure forges cross-level interlinks and empowers the model to handle more complex and long-term relationships.
  arXiv  Detail & Related papers  (2024-08-27T06:50:28Z)
• A Comprehensive Study of Knowledge Editing for Large Language Models [82.65729336401027]
  Large Language Models (LLMs) have shown extraordinary capabilities in understanding and generating text that closely mirrors human communication. This paper defines the knowledge editing problem and provides a comprehensive review of cutting-edge approaches. We introduce a new benchmark, KnowEdit, for a comprehensive empirical evaluation of representative knowledge editing approaches.
  arXiv  Detail & Related papers  (2024-01-02T16:54:58Z)

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.