Jia Zhu

High-quality data is the cornerstone of advancing large language models. However, the field currently faces a critical dilemma: the supply of premium data is nearing depletion, while vast stale corpora remain underutilized. Our empirical analysis reveals that training models on such data directly...

Existing video summarization methods mainly compress content for gist browsing, but they often break the prerequisite logic in instructional videos and induce logical inversions (e.g., conclusions before premises). We formalize this problem as Structure-Pedagogical Reconstruction (SPR). SPR raise...

Large language models (LLMs) have achieved strong performance on standard benchmarks, yet their performance is not robust across different task manifestations. It remains unclear how performance changes under controlled task rewrites that preserve the original solution structure, while varying th...

Large Language Models (LLMs) have shown remarkable performance in multi-turn dialogue. However, in multi-turn dialogue, models still struggle to stay aligned with what has been established earlier, follow dependencies across many turns, and avoid drifting into incorrect facts as the interaction g...

Existing video understanding benchmarks mainly emphasize general visual recognition and reasoning, but do not adequately capture the pedagogical logic embedded in instructional videos. To address this gap, we present PedagogyBench, a multimodal benchmark for instructional video understanding grou...

Accurately identifying student misconceptions is crucial for personalized education but faces three challenges: (1) data scarcity with long-tail distribution, where authentic student reasoning is difficult to synthesize; (2) fuzzy boundaries between error categories with high annotation noise; (3...