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20242026
most citedUnderstanding Why Large Language Models Can Be Ineffective in Time Series Analysis: The Impact of Modality Alignment

1 citations · 2 across the 11 of their papers we have counts for

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cs.CV2026

Detecting Chain-of-Thought Hijacking in Backdoored Multimodal Large Reasoning Models

Zhifang Zhang, Qi Wei, Weitong Chen +5

Chain-of-thought (CoT) reasoning enables multimodal large reasoning models (MLRMs) to solve complex visual problems, but also exposes them to CoT hijacking: an adversary implants a…

cs.AI2026

Auditing Routing Entropy as an Uncertainty Signal in Attention-Residual Transformers

Wenhao Liang, Lin Yue, Wei Emma Zhang +3

Dynamic architectures leave a per-example routing trace beside each prediction, and diffuse routing is easy to read as a sign that the prediction is unreliable. We audit that readi…

cs.AI2026

Routing Probes Can Improve Without New Information: An Exact-Null Audit of Uncertainty Beyond Model Outputs

Wenhao Liang, Lin Yue, Wei Emma Zhang +3

Routing signals of modern vision transformers -- expert gates, attention-residual weights and halting scores -- often improve probes that predict whether the model is correct, and…

cs.LG2026

Signal-Routed Temperature Scaling: Low-Capacity Risk-Conditioned Calibration for Small Validation Budgets

Wenhao Liang, Liangwei Nathan Zheng, Lin Yue +4

When a classifier is recalibrated from only a few thousand held-out examples, the capacity of the calibration map becomes a statistical design choice rather than a purely architect…

cs.AI2026

How Much Can Reliability Drift Under a Fixed Confidence Distribution?

Wenhao Liang, Lin Yue, Wei Emma Zhang +3

A classifier's conditional accuracy can change while its confidence distribution stays exactly the same. We study the worst-case movement of the reliability relation under covariat…

cs.LG2026

Tackling Multimodal Learning Challenges with Mixture-of-Expert: A Survey

Liangwei Nathan Zheng, Wei Emma Zhang, Olaf Maennel +2

Mixture-of-Experts (MoE) presents a naturally compatible and scalable framework for multimodal learning, demonstrating strong adaptability across diverse modalities and tasks. Desp…

cs.CV2026

Probing Routing-Conditional Calibration in Attention-Residual Transformers

Wenhao Liang, Lin Yue, Wei Emma Zhang +4

Post-hoc calibration is usually evaluated as a function of logits or softmax confidence alone, even as routing-augmented architectures increasingly accompany predictions with sampl…

cs.CV2026

Test-Time Attention Purification for Backdoored Large Vision Language Models

Zhifang Zhang, Bojun Yang, Shuo He +5

Despite the strong multimodal performance, large vision-language models (LVLMs) are vulnerable during fine-tuning to backdoor attacks, where adversaries insert trigger-embedded sam…