29 citations · 90 across the 18 of their papers we have counts for
19 papers
FARS: A Fully Automated Research System Deployed at Scale
Qiong Tang, Tianxiang Sun, Xiangkun Hu +54
Recent automated research systems show that language-model agents can generate hypotheses, run experiments, and write complete manuscripts, but most evidence still comes from selec…
Position Bias Correction is Insufficient for One-Pass Attention Sorting
Qiong Tang, Xiangkun Hu, Xiangyang Liu +2
Long-context language models suffer from position bias, where information in middle positions is underutilized. Attention Sorting addresses this by iteratively reordering documents…
NLL-Guided Full-Attention Layer Selection for Training-Free Sliding-Window Adaptation
Qiong Tang, Xiangkun Hu, Xiangyang Liu +2
Hybrid attention models that mix full and sliding-window attention across layers offer a promising approach to efficient long-context inference, but the critical question of \emph{…
Output-Space Allocation Costs for Calibration-Guided LLM Compression: An Empirical Study
Qiong Tang, Xiangkun Hu, Xiangyang Liu +2
Training-free compression methods for large language models (LLMs) often use calibration data to guide compression decisions. ROCKET, a recent method combining sparse-dictionary fa…
UnitCoder: Scalable Iterative Code Synthesis with Unit Test Guidance
Yichuan Ma, Yunfan Shao, Peiji Li +5
Large Language Models (LLMs) have demonstrated remarkable capabilities in various tasks, yet code generation remains a major challenge. Current approaches for obtaining high-qualit…
FastMCTS: A Simple Sampling Strategy for Data Synthesis
Peiji Li, Kai Lv, Yunfan Shao +5
Synthetic high-quality multi-step reasoning data can significantly enhance the performance of large language models on various tasks. However, most existing methods rely on rejecti…