Visual-token compression improves the efficiency of large vision-language models, but can expose failures that full-token evaluation misses. We study adversarial images that preserve full-token correctness yet induce errors after compression, even when both inference paths succeed on the clean image. Creating such fail...
Shilinlu Yan, Bo-Wen Chen, Yuechen Zhang et al.· 0 citations
Large reasoning models (LRMs) improve performance on complex tasks through extended reasoning, yet the same process can degenerate into redundant verification and persistent generation loops. Such uncontrolled reasoning increases inference cost and creates risks of resource exhaustion and service degradation. However,...
Yuan-He Zhang, Ziwei Wang, Jie Ren et al.· 0 citations
Large language models (LLMs) exhibit strong general capabilities that mechanistic interpretability has attributed to sparse computational circuits. However, existing circuit studies emphasize preserving functionality or explaining safety, leaving the mechanisms underlying failures across a broader range of tasks largel...
Chuan-Pu-Zou-Sheng-Li-Guo-Jing-Zhong-Gu-Yue-Shu-Ca Liu, Miao Yu, Yi-Kai Cai et al.· 0 citations
Visual token compression reduces the inference cost of Large Vision-Language Models (LVLMs). However, aggregate robustness measures do not reveal whether a particular adversarial failure is induced by compression or inherited from the underlying model. We define a compression-specific failure (CSF) as an adversarial in...
Qian-Kun Li, Yuechen Zhang, Bo-Wen Chen et al.· 0 citations
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