2 citations · 2 across the 9 of their papers we have counts for
9 papers
ChartDesign: Towards LLM Designer of Data Visualization
Mohammed Afaan Ansari, Aniruddh Bansal, Tianyi Zhou
Charts are the dominant medium for visualizing data, discovering patterns and trends, and communicating data driven insights, yet designing them still requires expensive human effo…
ChartAB: A Benchmark for Chart Grounding & Dense Alignment
Aniruddh Bansal, Davit Soselia, Dang Nguyen +1
Charts play an important role in visualization, reasoning, data analysis, and the exchange of ideas among humans. However, existing vision-language models (VLMs) still lack accurat…
CaughtCheating: Is Your MLLM a Good Cheating Detective? Exploring the Boundary of Visual Perception and Reasoning
Ming Li, Chenguang Wang, Yijun Liang +6
Recent agentic Multi-Modal Large Language Models (MLLMs) such as GPT-o3 have achieved near-ceiling scores on various existing benchmarks, motivating a demand for more challenging t…
FaSTA: Fast-Slow Toolpath Agent with Subroutine Mining for Efficient Multi-turn Image Editing
Advait Gupta, Rishie Raj, Dang Nguyen +1
We develop a cost-efficient neurosymbolic agent to address challenging multi-turn image editing tasks such as ``Detect the bench in the image while recoloring it to pink. Also, rem…
CoSTA: Cost-Sensitive Toolpath Agent for Multi-turn Image Editing
Advait Gupta, NandaKiran Velaga, Dang Nguyen +1
Text-to-image models like stable diffusion and DALLE-3 still struggle with multi-turn image editing. We decompose such a task as an agentic workflow (path) of tool use that address…
GUI Agents: A Survey
Dang Nguyen, Jian Chen, Yu Wang +27
Graphical User Interface (GUI) agents, powered by Large Foundation Models, have emerged as a transformative approach to automating human-computer interaction. These agents autonomo…
DynaSaur: Large Language Agents Beyond Predefined Actions
Dang Nguyen, Viet Dac Lai, Seunghyun Yoon +9
Existing LLM agent systems typically select actions from a fixed and predefined set at every step. While this approach is effective in closed, narrowly scoped environments, it pres…
Multi-Objective Linguistic Control of Large Language Models
Dang Nguyen, Jiuhai Chen, Tianyi Zhou
Large language models (LLMs), despite their breakthroughs on many challenging benchmark tasks, lean to generate verbose responses and lack the controllability of output complexity,…
RuleR: Improving LLM Controllability by Rule-based Data Recycling
Ming Li, Han Chen, Chenguang Wang +3
Large language models (LLMs) still lack delicate controllability over their responses, which is critical to enhancing their performance and the user experience. However, curating s…