#benchmark

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86 papers match

cs.CV2026

EEG-EditBench: Probing Visual Information in EEG-Image Retrieval Models with Controlled Image Edits

Kaifan Zhang, Lihuo He, Yuqi Ji +4

The paper presents EEG-EditBench, a benchmark that uses controlled image edits to evaluate how EEG-to-image retrieval models capture fine-grained visual information.

#eeg decoding#image retrieval#visual perception#benchmark
cs.AI2026

OSReward: Instituting Standardized Evaluation for Cross-Platform Computer-Use Reward Models

Qiushi Sun, Kanzhi Cheng, Yian Wang +20

The paper introduces OSReward, a benchmark for evaluating vision-language model judges that assess computer-using agent trajectories, and presents open reward models (OS‑Shepherd)…

#computer-use agents#vision-language models#reward modeling#benchmark
cs.CL2026

Can LVLMs Uncover the Truth Behind Visual Illusions? An Analysis of Perceptual and Reasoning Capabilities

Liangjie Zhao, Jiaqing Lyu, Kexin Tang +5

The paper introduces IllusionReasoning, a benchmark that uses visual illusion images to jointly assess perception and reasoning abilities of large vision‑language models, revealing…

#visual illusions#vision-language models#reasoning evaluation#benchmark
cs.AI2026

BlueprintRepair: Typed Local Edits for Failed Lean Proof Blueprints

Ruslan Khrulev

The paper introduces BlueprintRepair, an interface that lets large language models make typed, local edits to Lean proof blueprints, and evaluates it on a benchmark of controlled p…

#theorem proving#lean#large language models#proof repair
cs.CL2026

From Single- to Cross-Document: Benchmarking Multi-Granularity Event Analysis of Large Language Models

Tao Wen, Shuai Shao, Pei Ke +7

The paper introduces MiGUE-Bench, a benchmark that evaluates large language models on multi‑granularity event analysis tasks ranging from single‑document event detection to cross‑d…

#event detection#cross-document analysis#large language models#benchmark
cs.AI2026

IFHierBench: Hierarchical Instruction Following for Large Language Models

Yuetian Mao, Chunyang Chen

The paper introduces IFHierBench, a benchmark for evaluating how well large language models follow hierarchical, nested constraints in instruction prompts, and shows current models…

#instruction following#hierarchical constraints#large language models#benchmark