8 citations · 18 across the 43 of their papers we have counts for
47 papers
Inside VLM Chart Reading: Tracing Value Reading from Vertical Bar Charts Across Space and Depth
Tianhao Niu, Qingfu Zhu, Wanxiang Che
Vision--language models (VLMs) can answer chart questions accurately, but output accuracy does not show how they combine the evidence needed to recover an exact value. We study ver…
FormalTCS: Benchmarking End-to-End Frontier Formal Theoretical Computer Science Research of Large Language Models
Dingzirui Wang, Xuanliang Zhang, Keyan Xu +2
Large language models (LLMs) have shown growing potential for automated theoretical computer science (TCS) research, yet existing benchmarks remain far from realistic research sett…
SDAM: Structure-Difference-Aware Memory Evolution for Complex Text-to-SQL
Keyan Xu, Dingzirui Wang, Xuanliang Zhang +2
Text-to-SQL aims to convert natural language questions into executable SQL queries. While memory-based agent system improves complex SQL generation, existing memory design neglect…
CurateEvo: Data-Curation Evolving for Agentic Post-Training
Dingzirui Wang, Xuanliang Zhang, Keyan Xu +2
Large language model (LLM) agents require post-training methods that can improve long-horizon decision making from environment feedback. However, existing agentic post-training pip…
Dashboard2Code: Evaluating Multimodal Models on Reconstructing Interactive Dashboards
Tianhao Niu, Ziyu Han, Qiguang Chen +5
Automatic data visualization generation has advanced rapidly with multi-modal large language models, yet existing efforts largely focus on static charts and overlook the interactiv…
What You See Is What You Get: Observation-Aligned Supervision for Chart-to-Code Generation
Tianhao Niu, Qingfu Zhu, Wanxiang Che
Chart-to-code generation is commonly trained through supervised fine-tuning on reference plotting scripts, implicitly treating the gold code as a fully observable target. However,…
Scaling Laws for Agent Harnesses via Effective Feedback Compute
Xuanliang Zhang, Dingzirui Wang, Keyan Xu +2
Agent harnesses shape language-model performance by controlling tool use, feedback, verification, memory, and repair. Yet raw test-time expenditure, such as tokens, tool calls, wal…
When Does Language Matter? Multilingual Instructions Reveal Step-wise Language Sensitivity in Vision-Language-Action Models
Xuan Dong, Zhe Han, Tianhao Niu +2
Vision-Language-Action (VLA) models have shown strong performance in language-conditioned robotic manipulation, yet their robustness to linguistic variation remains poorly understo…
ChartREG++: Towards Benchmarking and Improving Chart Referring Expression Grounding under Diverse referring clues and Multi-Target Referring
Tianhao Niu, Ziyu Han, Xuan Dong +2
Referring expression grounding is a core problem in visual grounding and is widely used as a diagnostic of spatial grounding and reasoning in vision and language models, yet most p…
EpiBench: Benchmarking Multi-turn Research Workflows for Multimodal Agents
Xuan Dong, Huanyang Zheng, Tianhao Niu +7
Scientific research follows multi-turn, multi-step workflows that require proactively searching the literature, consulting figures and tables, and integrating evidence across paper…
How Do Language Models Understand Tables? A Mechanistic Analysis of Cell Location
Xuanliang Zhang, Dingzirui Wang, Keyan Xu +2
While Large Language Models (LLMs) are increasingly deployed for table-related tasks, the internal mechanisms enabling them to process linearized two-dimensional structured tables…
When Does Context Help? Error Dynamics of Contextual Information in Large Language Models
Dingzirui Wang, Xuanliang Zhang, Keyan Xu +3
Contextual information at inference time, such as demonstrations, retrieved knowledge, or interaction history, can substantially improve large language models (LLMs) without parame…
CVE-Factory: Scaling Expert-Level Agentic Tasks for Code Security Vulnerability
Xianzhen Luo, Jingyuan Zhang, Shiqi Zhou +8
Evaluating and improving the security capabilities of code agents requires high-quality, executable vulnerability tasks. However, existing works rely on costly, unscalable manual r…
MEnvAgent: Scalable Polyglot Environment Construction for Verifiable Software Engineering
Chuanzhe Guo, Jingjing Wu, Sijun He +10
The evolution of Large Language Model (LLM) agents for software engineering (SWE) is constrained by the scarcity of verifiable datasets, a bottleneck stemming from the complexity o…
Automated Snippet-Alignment Data Augmentation for Code Translation
Zhiming Zhang, Qingfu Zhu, Xianzhen Luo +3
Code translation aims to translate the code from its source language to the target language and is used in various software development scenarios. Recent developments in Large Lang…
How Many Code and Test Cases Are Enough? Evaluating Test Cases Generation from a Binary-Matrix Perspective
Xianzhen Luo, Jinyang Huang, Wenzhen Zheng +5
Evaluating test cases automatically generated by Large Language Models (LLMs) is a critical yet challenging task. Existing benchmarks often evaluate the exclusion ratio on large, u…
Scaling Laws for Code: A More Data-Hungry Regime
Xianzhen Luo, Wenzhen Zheng, Qingfu Zhu +5
Code Large Language Models (LLMs) are revolutionizing software engineering. However, scaling laws that guide the efficient training are predominantly analyzed on Natural Language (…
Bounds of Chain-of-Thought Robustness: Reasoning Steps, Embed Norms, and Beyond
Dingzirui Wang, Xuanliang Zhang, Keyan Xu +3
Existing research indicates that the output of Chain-of-Thought (CoT) is significantly affected by input perturbations. Although many methods aim to mitigate such impact by optimiz…
CommonKV: Compressing KV Cache with Cross-layer Parameter Sharing
Yixuan Wang, Haoyu Qiao, Lujun Li +2
Large Language Models (LLMs) confront significant memory challenges due to the escalating KV cache with increasing sequence length. As a crucial technique, existing cross-layer KV…
Multi-Layer Attention is the Amplifier of Demonstration Effectiveness
Dingzirui Wang, Xuangliang Zhang, Keyan Xu +3
Numerous studies have investigated the underlying mechanisms of in-context learning (ICL) effectiveness to inspire the design of related methods. However, existing work predominant…
Learning-to-Context Slope: Evaluating In-Context Learning Effectiveness Beyond Performance Illusions
Dingzriui Wang, Xuanliang Zhang, Keyan Xu +3
In-context learning (ICL) has emerged as an effective approach to enhance the performance of large language models (LLMs). However, its effectiveness varies significantly across mo…
Format-Adapter: Improving Reasoning Capability of LLMs by Adapting Suitable Format
Dingzirui Wang, Xuanliang Zhang, Rongyu Cao +8
Generating and voting multiple answers is an effective method to mitigate reasoning inconsistencies of large language models (LLMs). Prior works have shown that multiple reasoning…
Advancing Tool-Augmented Large Language Models via Meta-Verification and Reflection Learning
Zhiyuan Ma, Jiayu Liu, Xianzhen Luo +3
Empowering large language models (LLMs) with effective tool utilization capabilities is crucial for enabling AI agents to solve complex problems. However, current models face two m…
AlignEvoSkill: Towards Knowledge-Aware and Task-Aligned Agent Skill Evolution
Dingzirui Wang, Xuanliang Zhang, Keyan Xu +3
Reusable skills play a key role in improving LLM-based agents, but existing skill-evolution methods often fail to ensure that evolved skills both cover the knowledge required by th…