2 citations · 2 across the 11 of their papers we have counts for
13 papers
Thinking in Depth: Retrospective Inference for Tabular Foundation Models
Hao-Run Cai, Si-Yang Liu, Zi-Jian Cheng +8
Tabular foundation models (TFMs) are pretrained across diverse tabular tasks and make predictions on a new table at inference time using its labeled examples as context. Most recen…
LookStep: Efficient Vision-Language Navigation with Linguistic Foresight and Event Driven Memory
Kun-Yang Yu, Yingzhe Li, Hongyu Xu +8
Vision-Language Navigation (VLN) requires an embodied agent to follow natural-language instructions in unseen environments. Recent progress has been largely driven by Multimodal La…
STEGNav: Spatio-Temporal Event Graph Reasoning for Multimodal Lifelong Object Navigation
Yang Chen, Zhenyu Huang, Wenbo Fu +4
Multimodal lifelong navigation requires an agent to autonomously explore unseen environments while sequentially completing navigation tasks specified by object categories, language…
Self-Evolving Neuro-Symbolic Skills for Tool-Augmented Spatial Reasoning
Shi-Yu Tian, Zhuo-Xia Wang, Xuan-Yi Zhu +6
Large vision-language models have achieved strong performance in multimodal reasoning, but they remain unreliable on fine-grained spatial tasks that demand both precise spatial per…
On the Learnability of Test-Time Adaptation: A Recovery Complexity Perspective
Zhi Zhou, Ming Yang, Shi-Yu Tian +3
Test-time adaptation (TTA) aims to adapt models to maintain reliable performance on non-stationary test streams without requiring labeled data. Despite its empirical success, the l…
VT-Bench: A Unified Benchmark for Visual-Tabular Multi-Modal Learning
Zi-Yi Jia, Zi-Jian Cheng, Xin-Yue Zhang +4
Multi-model learning has attracted great attention in visual-text tasks. However, visual-tabular data, which plays a pivotal role in high-stakes domains like healthcare and industr…
LAST: Leveraging Tools as Hints to Enhance Spatial Reasoning for Multimodal Large Language Models
Shi-Yu Tian, Zhi Zhou, Kun-Yang Yu +5
Spatial reasoning is a cornerstone capability for intelligent systems to perceive and interact with the physical world. However, multimodal large language models (MLLMs) frequently…
Thinking with Tables: Enhancing Multi-Modal Tabular Understanding via Neuro-Symbolic Reasoning
Kun-Yang Yu, Zhi Zhou, Shi-Yu Tian +6
Multimodal Large Language Models (MLLMs) have demonstrated remarkable reasoning capabilities across modalities such as images and text. However, tabular data, despite being a criti…
NeSy-Route: A Neuro-Symbolic Benchmark for Constrained Route Planning in Remote Sensing
Ming Yang, Zhi Zhou, Shi-Yu Tian +3
Remote sensing underpins crucial applications such as disaster relief and ecological field surveys, where systems must understand complex scenes and constraints and make reliable d…
TabularMath: Understanding Math Reasoning over Tables with Large Language Models
Shi-Yu Tian, Zhi Zhou, Wei Dong +5
Mathematical reasoning has long been a key benchmark for evaluating large language models. Although substantial progress has been made on math word problems, the need for reasoning…
LawGPT: Knowledge-Guided Data Generation and Its Application to Legal LLM
Zhi Zhou, Kun-Yang Yu, Shi-Yu Tian +6
Large language models (LLMs), both proprietary and open-source, have demonstrated remarkable capabilities across various natural language processing tasks. However, they face signi…
Fully Test-time Adaptation for Tabular Data
Zhi Zhou, Kun-Yang Yu, Lan-Zhe Guo +1
Tabular data plays a vital role in various real-world scenarios and finds extensive applications. Although recent deep tabular models have shown remarkable success, they still stru…
VCSearch: Bridging the Gap Between Well-Defined and Ill-Defined Problems in Mathematical Reasoning
Shi-Yu Tian, Zhi Zhou, Kun-Yang Yu +4
Large language models (LLMs) have demonstrated impressive performance on reasoning tasks, including mathematical reasoning. However, the current evaluation mostly focuses on carefu…