Current benchmarks for Large Reasoning Models (LRMs) primarily rely on answer correctness, failing to assess the structural coherence and cognitive soundness of the reasoning process itself. To address this gap, we introduce Cognitive Hierarchy Trace (CHT), a novel evaluation framework grounded in Bloom’s Cognitive Taxonomy (BCT). CHT provides a structured, step-wise mapping of a model’s reasoning trajectory onto hierarchical cognitive levels, enabling the detection of structural anomalies such as hierarchy jumps, breaks, and overthinking. Based on CHT, we present BloomEval, the first large-scale benchmark designed for fine-grained cognitive capability assessment. It comprises 94,602 math problems, each annotated with Bloom’s cognitive levels, CHT trajectories, a three-tier knowledge hierarchy, and problem difficulty. To ensure scalable yet reliable annotation, we develop an Expert-LLM collaborative pipeline with a three-stage reconciliation mechanism. Our comprehensive evaluation reveals a critical finding: models often arrive at correct answers through cognitively flawed or opaque reasoning paths. The CHT-based analysis uncovers prevalent structural inconsistencies that are invisible to outcome-only metrics, demonstrating that answer accuracy is an insufficient proxy for reasoning quality.

Knowledge Tracing (KT) is essential for tracking students’ evolving knowledge states and predicting their future performance. While current graph-based methods focus on exercise-concept relations, they often overlook the inherent group structures among students. Similarly, emerging LLM-based approaches rely on individual histories, lacking the broader context of group references and contrastive evidence. As a result, existing individual-isolation paradigms fail to provide stable predictions and evidence-based explanations. To bridge this gap, we propose Micro-Community Knowledge Tracing (MicroC-KT), a framework that incorporates learning micro-environments to provide social-cognitive anchors for KT. MicroC-KT identifies latent learning communities via hypergraph modeling and generates dual-granular summaries to facilitate community matching and peer retrieval. By extracting contrastive group evidence, the model prompts an LLM to generate both accurate answer predictions and verifiable analysis reports. Experiments on four public datasets demonstrate that MicroC-KT significantly outperforms state-of-the-art baselines in predictive performance while providing more reliable and evidence-based explanations.

Large language models (LLMs) require continual knowledge updates to keep pace with the evolving world. While various model editing methods have been proposed, most face critical challenges in the context of lifelong learning due to two fundamental limitations: (1) Edit Overshooting - parameter updates intended for a specific fact spill over to unrelated regions, causing interference with previously retained knowledge; and (2) Knowledge Entanglement - polysemantic neurons’ overlapping encoding of multiple concepts makes it difficult to isolate and edit a single fact. In this paper, we propose MicroEdit, a neuron-level editing method that performs minimal and controlled interventions within LLMs. By leveraging a sparse autoencoder (SAE), MicroEdit disentangles knowledge representations and activates only a minimal set of necessary neurons for precise parameter updates. This targeted design enables fine-grained control over the editing scope, effectively mitigating interference and preserving unrelated knowledge. Extensive experiments show that MicroEdit outperforms prior methods and robustly handles lifelong knowledge editing across QA and Hallucination settings on LLaM and Mistral.