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Haotian Zhang 0029

dblp:83/4184-29 · DBLP profile ↗
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3ranked-venue papers
0as first author
3since 2021 · last 2026
0009-0000-1483-6261ORCID · conflict

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 3 · 3 since 2021Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Human-computer interaction and pervasive computing
1 paper
Learning and educational technologies · 100%
Artificial intelligence
1 paper
Language models and text generation · 100%

Topics — the 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Learning and educational technologies › educational assessment
automated grading
0.912025
MathMistake Checker: A Comprehensive Demonstration for Step-by-Step Math Problem Mistake Finding by Prompt-Guided LLMs · AAAI 2025
Natural language and speech › Language models and text generation › mathematical reasoning
mathematical problem solving
0.312025
MathMistake Checker: A Comprehensive Demonstration for Step-by-Step Math Problem Mistake Finding by Prompt-Guided LLMs · AAAI 2025

Methods — techniques the papers use, named apart from their topics

large language model · 1.7computer vision · 1.7chain-of-thought prompting · 1.7
YearPublicationVenuePosition
2026 Towards enterprise-specific question-answering for IT operations and maintenance based on retrieval-augmented generation mechanism
Zhuoxuan Jiang, Tianyang Zhang 0004, Shengguang Bai, Haotian Zhang 0029, Yinong Xun, Wen Si
Expert Syst. Appl.5
2025 MathMistake Checker: A Comprehensive Demonstration for Step-by-Step Math Problem Mistake Finding by Prompt-Guided LLMs
abstract
We propose a novel system, MathMistake Checker, designed to automate step-by-step mistake finding in mathematical problems with lengthy answers through a two-stage process. The system aims to simplify grading, increase efficiency, and enhance learning experiences from a pedagogical perspective. It integrates advanced technologies, including computer vision and the chain-of-thought capabilities of the latest large language models (LLMs). Our system supports open-ended grading without reference answers and promotes personalized learning by providing targeted feedback. We demonstrate its effectiveness across various types of math problems, such as calculation and word problems.
Tianyang Zhang 0004, Zhuoxuan Jiang, Haotian Zhang 0029
AAAI3
2025 LLMA4ITOps: A Lightweight LLM-Based Multi-Agent Framework for IT Operations and Maintenance
Zhuoxuan Jiang, Tianyang Zhang 0004, Haotian Zhang 0029, Yinong Xun, Yang Liu 0218, Dehua Feng, Wen Si
NLPCC (2)3