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Jihao Zhao

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3 papers

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AAAI Conference 2026 Conference Paper

Enhancing the Knowledge Tracing via a Plug-In Guided Diffusion Model

Shuaishuai Zu
Jihao Zhao
Biao Qin

Knowledge tracing (KT) refers to the problem of predicting students' future performance given their past performance. Scrutinizing previous studies, we can summarize a common learn-to-predict paradigm: a KT model first learns the student's latent knowledge states from historical question-solving learning interactions and then directly predicts whether the student could correctly answer new questions. Alongside the paradigm, existing KT models are dedicated to tailoring refinements for improving predictive performance. However, this has led to increasing model complexity and reduced usability. Inspired by the diagnosis process of human teachers, they conduct correctness prediction based on the students' responses, which are further derived from their latent knowledge states. To achieve this, we propose a novel plug-in Guided diffusiOn mODule (GOOD), which reframes the KT problem as a learn-generate-to-predict paradigm. Specifically, we first employ an existing KT backbone to learn the student's evolving latent knowledge states, subsequently feeding these into our GOOD. Next, GOOD employs a person-wise noise scheduling strategy to add noise to the target responses in the diffusion process, thereby exploring the underlying distribution of response space. Then, GOOD designs a flexible transformer-modulated denoising network to generate target responses utilizing the latent knowledge states as conditional guidance in the reverse process. Finally, the generated responses can explicitly reflect the student's performance, thereby facilitating the correctness prediction. Extensive experiments on four datasets have verified the effectiveness of GOOD in boosting existing KT models to achieve state-of-the-art performance, as well as its generalizability as a flexible plugin.

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AAAI Conference 2026 Conference Paper

SEAP: Sparse Expert Activation Pruning Unlocks the Brainpower of Large Language Models

Xun Liang
Hanyu Wang
Huayi Lai
Simin Niu
Shichao Song
Jiawei Yang
Jihao Zhao
Feiyu Xiong

Pruning is a promising approach to reduce the high inference cost of large language models (LLMs), but it often comes at the expense of performance. Motivated by the "functional localization" theory in neuroscience, we hypothesize that LLMs contain task-specific expert activation paths, where specific subsets of neurons are co-activated for particular tasks. This structure allows selective activation to preserve task performance while improving inference efficiency. We introduce Sparse Expert Activation Pruning (SEAP), a training-free pruning method for large language models. SEAP identifies task-relevant activation paths by analyzing the clustering patterns of hidden states and neuron activations on a multi-task calibration dataset. Cross-task transfer evaluations confirm the existence of such expert activation structures. SEAP constructs task-aware pruning masks by leveraging a task-expert calibration dataset, which provides representative samples across diverse tasks to reveal their activation signatures. It then employs a lightweight task router to dynamically select relevant computation paths based on the input task. This design significantly reduces inference cost without compromising accuracy. Experimental results show that SEAP retains model performance with only a 1.5% drop on most tasks at 20% sparsity, and at 50% sparsity, it surpasses strong pruning baselines such as WandA and FLAP by over 20%. These results highlight SEAP as a scalable and effective solution for efficient LLM inference.

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AAAI Conference 2026 Conference Paper

TAdaRAG: Task Adaptive Retrieval-Augmented Generation via On-the-Fly Knowledge Graph Construction

Jie Zhang
Bo Tang
Wanzi Shao
Wenqiang Wei
Jihao Zhao
Jianqing Zhu
Zhiyu Li
Wen Xi

Retrieval-Augmented Generation (RAG) improves large language models by retrieving external knowledge, often truncated into smaller chunks due to the input context window, which leads to information loss, resulting in response hallucinations and broken reasoning chains. Moreover, traditional RAG retrieves unstructured knowledge, introducing irrelevant details that hinder accurate reasoning. To address these issues, we propose TAdaRAG, a novel RAG framework for on-the-fly task-adaptive knowledge graph construction from external sources. Specifically, we design an intent-driven routing mechanism to a domain-specific extraction template, followed by supervised fine-tuning and a reinforcement learning-based implicit extraction mechanism, ensuring concise, coherent, and non-redundant knowledge integration. Evaluations on six public benchmarks and a real-world business benchmark (NowNewsQA) across three backbone models demonstrate that TAdaRAG outperforms existing methods across diverse domains and long-text tasks, highlighting its strong generalization and practical effectiveness.

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