2 citations · 5 across the 14 of their papers we have counts for
13 papers
OmniEdu: Open Foundation Models for Learning and Teaching
Hao Liang, Qihan Lin, Meiyi Qiang +5
Educational foundation models must solve problems, understand curriculum structure, diagnose learner difficulties, and provide appropriate instructional support. Existing education…
Repo2Skill-Evo: Repository Skills Go Stale in Silence
Chenyuan Duan, Ge Shi, Zineng Mao +10
Large language model (LLM) agents increasingly operate over evolving software repositories, where success depends on repository-specific procedural knowledge: which APIs to call, w…
WorkSurface-Bench: Benchmarking Enterprise Agents on Multi-Surface Knowledge Routing
Hao Liang, Meiyi Qiang, Sizhe Qiu +2
Enterprise agents often need to integrate heterogeneous knowledge sources: documents for narrative facts, tables for computation, and dependency graphs for file relationships. Exis…
DataPrep-Bench: Benchmarking LLMs as Training Data Preparators
Hao Liang, Qifeng Cai, Yibo Lin +11
The quality of training data fundamentally determines the capabilities of large language models (LLMs), yet no unified benchmark exists to measure how well LLMs, agents, and data-c…
K12-KGraph: A Curriculum-Aligned Knowledge Graph for Benchmarking and Training Educational LLMs
Hao Liang, Qihan Lin, Zhaoyang Han +7
Large language models are increasingly used in K-12 education, but existing benchmarks mainly test exam question answering rather than understanding how curriculum knowledge is str…
BRACE: A Benchmark for Robust Audio Caption Quality Evaluation
Tianyu Guo, Hongyu Chen, Hao Liang +5
Automatic audio captioning is essential for audio understanding, enabling applications such as accessibility and content indexing. However, evaluating the quality of audio captions…
Rethinking Text-to-SQL: Dynamic Multi-turn SQL Interaction for Real-world Database Exploration
Linzhuang Sun, Tianyu Guo, Hao Liang +6
Recent advances in Text-to-SQL have achieved strong results in static, single-turn tasks, where models generate SQL queries from natural language questions. However, these systems…
MM-Verify: Enhancing Multimodal Reasoning with Chain-of-Thought Verification
Linzhuang Sun, Hao Liang, Jingxuan Wei +5
According to the Test-Time Scaling, the integration of External Slow-Thinking with the Verify mechanism has been demonstrated to enhance multi-round reasoning in large language mod…
BEATS: Optimizing LLM Mathematical Capabilities with BackVerify and Adaptive Disambiguate based Efficient Tree Search
Linzhuang Sun, Hao Liang, Jingxuan Wei +4
Large Language Models (LLMs) have exhibited exceptional performance across a broad range of tasks and domains. However, they still encounter difficulties in solving mathematical pr…
Synth-Empathy: Towards High-Quality Synthetic Empathy Data
Hao Liang, Linzhuang Sun, Jingxuan Wei +5
In recent years, with the rapid advancements in large language models (LLMs), achieving excellent empathetic response capabilities has become a crucial prerequisite. Consequently,…
KeyVideoLLM: Towards Large-scale Video Keyframe Selection
Hao Liang, Jiapeng Li, Tianyi Bai +7
Recently, with the rise of web videos, managing and understanding large-scale video datasets has become increasingly important. Video Large Language Models (VideoLLMs) have emerged…
MathScape: Benchmarking Multimodal Large Language Models in Real-World Mathematical Contexts
Hao Liang, Linzhuang Sun, Minxuan Zhou +7
With the rapid progress of Multimodal LLMs, evaluating their mathematical reasoning capabilities has become an increasingly important research direction. In particular, visual-text…
Efficient-Empathy: Towards Efficient and Effective Selection of Empathy Data
Linzhuang Sun, Hao Liang, Jingxuan Wei +4
In recent years, with the rapid advancements in large language models (LLMs), achieving excellent empathetic response capability has become a crucial prerequisite. Consequently, ma…