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20242026
most citedUQ: Assessing Language Models on Unsolved Questions

1 citations · 1 across the 9 of their papers we have counts for

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cs.AI2026

ASCII Attack: Recontextualising Harmful Requests as Artistic Critique in Large Language Models

Da Cheng Gu, Yifei Dong, Xinghao Yang +2

Safety alignment trains large language models to refuse harmful requests stated plainly, but that training is applied mostly to surface form. Requests that only recontextualise the…

cs.CV2026

Seeing Is Believing? A Benchmark for Multimodal Large Language Models on Visual Illusions and Anomalies

Wenjin Hou, Wei Liu, Han Hu +3

Multimodal Large Language Models (MLLMs) have shown remarkable proficiency on general-purpose vision-language benchmarks, reaching or even exceeding human-level performance. Howeve…

cs.AI2026

Hunt Instead of Wait: Evaluating Deep Data Research on Large Language Models

Wei Liu, Peijie Yu, Michele Orini +2

The agency expected of Agentic Large Language Models goes beyond answering correctly, requiring autonomy to set goals and decide what to explore. We term this investigatory intelli…

cs.AI2026

Are We Evaluating the Edit Locality of LLM Model Editing Properly?

Wei Liu, Haomei Xu, Hongkai Liu +5

Model editing has recently emerged as a popular paradigm for efficiently updating knowledge in LLMs. A central desideratum of updating knowledge is to balance editing efficacy, i.e…

cs.CL2026

RM-Distiller: Exploiting Generative LLM for Reward Model Distillation

Hongli Zhou, Hui Huang, Wei Liu +8

Reward models (RMs) play a pivotal role in aligning large language models (LLMs) with human preferences. Due to the difficulty of obtaining high-quality human preference annotation…