2 citations · 2 across the 5 of their papers we have counts for
6 papers
PolarMem: A Training-Free Polarized Latent Graph Memory for Verifiable Vision-Language Models
Zhisheng Chen, Tingyu Wu, Zijie Zhou +7
Memory is not merely a storage mechanism for intelligent systems, but a structure for organizing evidence and constraining belief. This is especially important for multimodal reaso…
Training-free Context-adaptive Attention for Efficient Long Context Modeling
Zeng You, Yaofo Chen, Shuhai Zhang +5
Large Language Models (LLMs) have demonstrated remarkable capabilities across a wide range of natural language processing tasks. These capabilities stem primarily from the self-att…
Beyond Memorization: A Multi-Modal Ordinal Regression Benchmark to Expose Popularity Bias in Vision-Language Models
Li-Zhong Szu-Tu, Ting-Lin Wu, Chia-Jui Chang +2
We expose a significant popularity bias in state-of-the-art vision-language models (VLMs), which achieve up to 34% higher accuracy on famous buildings compared to ordinary ones, in…
FlashRecovery: Fast and Low-Cost Recovery from Failures for Large-Scale Training of LLMs
Haijun Zhang, Jinxiang Wang, Zhenhua Yu +20
Large language models (LLMs) have made a profound impact across various fields due to their advanced capabilities. However, training these models at unprecedented scales requires e…
Progress or Regress? Self-Improvement Reversal in Post-training
Ting Wu, Xuefeng Li, Pengfei Liu
Self-improvement through post-training methods such as iterative preference learning has been acclaimed for enhancing the problem-solving capabilities (e.g., mathematical reasoning…
OlympicArena: Benchmarking Multi-discipline Cognitive Reasoning for Superintelligent AI
Zhen Huang, Zengzhi Wang, Shijie Xia +25
The evolution of Artificial Intelligence (AI) has been significantly accelerated by advancements in Large Language Models (LLMs) and Large Multimodal Models (LMMs), gradually showc…