Learners sharing similar implicit cognitive states often display comparable observable problem-solving performances. Leveraging collaborative connections among such similar learners proves valuable in comprehending human learning. Motivated by the success of collaborative modeling in various domains, such as recommender systems, we aim to investigate how collaborative signals among learners contribute to the diagnosis of human cognitive states (i.e., knowledge proficiency) in the context of intelligent education.The primary challenges lie in identifying implicit collaborative connections and disentangling the entangled cognitive factors of learners for improved explainability and controllability in learner Cognitive Diagnosis (CD). However, there has been no work on CD capable of simultaneously modeling collaborative and disentangled cognitive states. To address this gap, we present Coral, a $\underline{Co}$llabo$\underline{ra}$tive cognitive diagnosis model with disentang$\underline{l}$ed representation learning. Specifically, Coral first introduces a disentangled state encoder to achieve the initial disentanglement of learners' states.Subsequently, a meticulously designed collaborative representation learning procedure captures collaborative signals.

The growth of Massive Open Online Courses (MOOCs) presents significant challenges for personalized learning, where concept recommendation is crucial. 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. However, these methods face significant limitations due to their dependence on high-quality structured knowledge graphs, which are often scarce in real-world educational scenarios. To address this fundamental challenge, this paper proposes CLLMRec, a novel framework that leverages Large Language Models through two synergistic technical pillars: Semantic Alignment and Prerequisite Knowledge Distillation. The Semantic Alignment component constructs a unified representation space by encoding unstructured textual descriptions of learners and concepts. The Prerequisite Knowledge Distillation paradigm employs a teacher-student architecture, where a large teacher LLM (implemented as the Prior Knowledge Aware Component) extracts conceptual prerequisite relationships from its internalized world knowledge and distills them into soft labels to train an efficient student ranker. Building upon these foundations, our framework incorporates a fine-ranking mechanism that explicitly models learners' real-time cognitive states through deep knowledge tracing, ensuring recommendations are both structurally sound and cognitively appropriate. Extensive experiments on two real-world MOOC datasets demonstrate that CLLMRec significantly outperforms existing baseline methods across multiple evaluation metrics, validating its effectiveness in generating truly cognitive-aware and personalized concept recommendations without relying on explicit structural priors.

Decoded Neurofeedback (DecNef) is a flourishing non-invasive approach to brain modulation with wide-ranging applications in neuromedicine and cognitive neuroscience. However, progress in DecNef research remains constrained by subject-dependent learning variability, reliance on indirect measures to quantify progress, and the high cost and time demands of experimentation. We present DecNefLab, a modular and interpretable simulation framework that formalizes DecNef as a machine learning problem. Beyond providing a virtual laboratory, DecNefLab enables researchers to model, analyze and understand neurofeedback dynamics. Using latent variable generative models as simulated participants, DecNefLab allows direct observation of internal cognitive states and systematic evaluation of how different protocol designs and subject characteristics influence learning. We demonstrate how this approach can (i) reproduce empirical phenomena of DecNef learning, (ii) identify conditions under which DecNef feedback fails to induce learning, and (iii) guide the design of more robust and reliable DecNef protocols in silico before human implementation. In summary, DecNefLab bridges computational modeling and cognitive neuroscience, offering a principled foundation for methodological innovation, robust protocol design, and ultimately, a deeper understanding of DecNef-based brain modulation.

Abstract--Large Language Models (LLMs) have transformed code auto-completion by generating context-aware suggestions. Y et, deciding when to present these suggestions remains under-explored, often leading to interruptions or wasted inference calls. We propose an adaptive timing mechanism that dynamically adjusts the delay before offering a suggestion based on real-time developer feedback. Our suggested method combines a logistic transform of recent acceptance rates with a bounded delay range, anchored by a high-level binary prediction of the developer's cognitive state. In a two-month deployment with professional developers, our system improved suggestion acceptance from 4.9% with no delay to 15.4% with static delays, and to 18.6% with adaptive timing--while reducing blind rejections (rejections without being read) from 8.3% to 0.36%. T ogether, these improvements increase acceptance and substantially reduce wasted inference calls by 75%, making LLM-based code assistants more efficient and cost-effective in practice. Modern software development increasingly relies on AIpowered code assistants--most prominently LLM-based tools such as GitHub Copilot--which leverage massive pre-trained models to suggest context-aware completions and entire code snippets [1], [2]. These systems aim to boost productivity by reducing boilerplate and aiding API recall. Subsequent work on specialized code models (e.g., CodeBERT and CodeT5) has further improved completion accuracy and relevance [3]-[5]. Despite advances in what content to generate, the timing of suggestion delivery remains an underexplored yet critical factor.

Brain-computer interfaces (BCIs) often suffer from limited robustness and poor long-term adaptability. Model performance rapidly degrades when user attention fluctuates, brain states shift over time, or irregular artifacts appear during interaction. To mitigate these issues, we introduce a user state-aware electroencephalogram (EEG) filtering framework that refines neural representations before decoding user intentions. The proposed method continuously estimates the user's cognitive state (e.g., focus or distraction) from EEG features and filters unreliable segments by applying adaptive weighting based on the estimated attention level. This filtering stage suppresses noisy or out-of-focus epochs, thereby reducing distributional drift and improving the consistency of subsequent decoding. Experiments on multiple EEG datasets that emulate real BCI scenarios demonstrate that the proposed state-aware filtering enhances classification accuracy and stability across different user states and sessions compared with conventional preprocessing pipelines. These findings highlight that leveraging brain-derived state information--even without additional user labels--can substantially improve the reliability of practical EEG-based BCIs.

Large language models (LLMs) face persistent challenges when handling long-context tasks, most notably the lost in the middle issue, where information located in the middle of a long input tends to be underutilized. Some existing methods that reduce input have the risk of discarding key information, while others that extend context windows often lead to attention dispersion. To address these limitations, we propose Tree of Agents (TOA), a multi-agent reasoning framework that segments the input into chunks processed by independent agents. Each agent generates its local cognition, then agents dynamically exchange information for collaborative reasoning along tree-structured paths. TOA enables agents to probe different reasoning orders for multi-perspective understanding, effectively mitigating position bias and reducing hallucinations. To improve processing efficiency, we incorporate prefix-hash caching and adaptive pruning strategies, achieving significant performance improvements with comparable API overhead. Experiments show that TOA, powered by compact LLaMA3.1-8B, significantly outperforms multiple baselines and demonstrates comparable performance to the latest and much larger commercial models, such as Gemini1.5-pro, on various long-context tasks. Code is available at https://github.com/Aireduce952/Tree-of-Agents.

While cognitive diagnosis (CD) effectively assesses students' knowledge mastery from structured test data, applying it to real-world teacher-student dialogues presents two fundamental challenges. Traditional CD models lack a suitable framework for handling dynamic, unstructured dialogues, and it's difficult to accurately extract diagnostic semantics from lengthy dialogues. To overcome these hurdles, we propose DiaCDM, an innovative model. We've adapted the initiation-response-evaluation (IRE) framework from educational theory to design a diagnostic framework tailored for dialogue. We also developed a unique graph-based encoding method that integrates teacher questions with relevant knowledge components to capture key information more precisely. To our knowledge, this is the first exploration of cognitive diagnosis in a dialogue setting. Experiments on three real-world dialogue datasets confirm that DiaCDM not only significantly improves diagnostic accuracy but also enhances the results' interpretability, providing teachers with a powerful tool for assessing students' cognitive states. The code is available at https://github.com/Mind-Lab-ECNU/DiaCDM/tree/main.

Learners sharing similar implicit cognitive states often display comparable observable problem-solving performances.

The pipeline illustrates the full workflow from data collection in VR, through self-annotation and human baseline evaluation, to modeling and analysis of cognitive states. As virtual reality (VR) and augmented reality (AR) continue to gain popularity, head and hand motion data captured by consumer VR systems have become ubiquitous. Prior work shows such telemetry can be highly identifying and reflect broad user traits, often aligning with intuitive "folk theories" of body language. However, it remains unclear to what extent motion kinematics encode more nuanced cognitive states, such as confusion, hesitation, and readiness, which lack clear correlates with motion. To investigate this, we introduce a novel dataset of head and hand motion with frame-level annotations of these states collected during structured decision-making tasks. Our findings suggest that deep temporal models can infer subtle cognitive states from motion alone, achieving comparable performance with human observers. This work demonstrates that standard VR telemetry contains strong patterns related to users' internal cognitive processes, which opens the door for a new gener- To enhance reproducibility and support future work, we will make our dataset and modeling framework publicly available. Virtual Reality (VR) is rapidly evolving from a specialized tool for simulation and entertainment into a mainstream computing platform for work, education, and social interaction. As users spend more time in these immersive environments, the quality of human-computer interaction becomes paramount. The next generation of VR systems must move beyond explicit, command-based interfaces and develop the capacity for implicit, nuanced understanding. This requires an ability to perceive and adapt to a user's cognitive state in real-time, creating experiences that are more intuitive, supportive, and effective. The key to unlocking this capability lies in decoding the rich, continuous, and often subconscious stream of motion data generated by every user.

The conversational capabilities of large language models hold significant promise for enabling scalable and interactive tutoring. While prior research has primarily examined their ability to generate Socratic questions, it often overlooks a critical aspect: adaptively guiding learners in accordance with their cognitive states. This study moves beyond question generation to emphasize instructional guidance capability. We ask: Can LLMs emulate expert tutors who dynamically adjust strategies in response to learners' states? To investigate this, we propose GuideEval, a benchmark grounded in authentic educational dialogues that evaluates pedagogical guidance through a three-phase behavioral framework: (1) Perception, inferring learner states; (2) Orchestration, adapting instructional strategies; and (3) Elicitation, stimulating proper reflections. Empirical results indicate that existing LLMs often fail to provide effective adaptive scaffolding when learners experience confusion or require redirection. To complement the quantitative evaluation, we conduct a detailed failure case analysis, providing an intuitive understanding of these shortcomings. Furthermore, we introduce a behavior-guided finetuning strategy that leverages behavior-prompted instructional dialogues, substantially enhancing guidance performance. By shifting the focus from isolated content evaluation to learner-centered state-aware interaction, our work advocates a more dialogic paradigm for evaluating Socratic LLMs.