activity
20242026
collaborators

7 papers

cs.AI2026

InA-Probe: Instruction-Aware Active Probing for Time Series Forecasting with LLMs

Peiliang Gong, Emadeldeen Eldele, Chenyu Liu +8

Large Language Models (LLMs) have recently demonstrated impressive potential for time series forecasting. However, existing methods predominantly rely on passive modality alignment…

cs.LG2026

Train Less, Learn More: Adaptive Efficient Rollout Optimization for Group-Based Reinforcement Learning

Zhi Zhang, Zhen Han, Costas Mavromatis +9

Reinforcement learning (RL) plays a central role in large language model (LLM) post-training. Among existing approaches, Group Relative Policy Optimization (GRPO) is widely used, e…

cs.CV2026

VERA: Identifying and Leveraging Visual Evidence Retrieval Heads in Long-Context Understanding

Rongcan Pei, Huan Li, Fang Guo +1

While Vision-Language Models (VLMs) have shown promise in textual understanding, they face significant challenges when handling long context and complex reasoning tasks. In this pa…

cs.AI2026

SQL-Trail: Multi-Turn Reinforcement Learning with Interleaved Feedback for Text-to-SQL

Harper Hua, Zhen Han, Zhengyuan Shen +9

While large language models (LLMs) have substantially improved Text-to-SQL generation, a pronounced gap remains between AI systems and human experts on challenging benchmarks such…

cs.CV2025

CoIDO: Efficient Data Selection for Visual Instruction Tuning via Coupled Importance-Diversity Optimization

Yichen Yan, Ming Zhong, Qi Zhu +3

Multimodal large language models (MLLMs) rely heavily on instruction tuning to align vision and language capabilities, yet the computational cost of training on large-scale dataset…

cs.CL2025

BoundRL: Efficient Structured Text Segmentation through Reinforced Boundary Generation

Haoyuan Li, Zhengyuan Shen, Sullam Jeoung +6

Structured texts refer to texts containing structured elements beyond plain texts, such as code snippets and placeholders. Such structured texts increasingly require segmentation i…

cs.CV2024

Infusion: Preventing Customized Text-to-Image Diffusion from Overfitting

Weili Zeng, Yichao Yan, Qi Zhu +4

Text-to-image (T2I) customization aims to create images that embody specific visual concepts delineated in textual descriptions. However, existing works still face a main challenge…