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20212026
most citedSurvey Descent: A Multipoint Generalization of Gradient Descent for Nonsmooth Optimization

7 citations · 8 across the 7 of their papers we have counts for

collaborators

7 papers

cs.CV2026

Vision as Unified Multimodal Generation

Xiaoyang Han, Jianhua Li, Kewang Deng +14

We formulate computer vision as unified multimodal generation, where heterogeneous visual tasks are expressed in the native text and image generation spaces of a unified multimodal…

cs.CL2026

AlphaEval: Evaluating Agents in Production

Pengrui Lu, Bingyu Xu, Wenjun Zhang +24

The rapid deployment of AI agents in commercial settings has outpaced the development of evaluation methodologies that reflect production realities. Existing benchmarks measure age…

cs.AI2026

StaRPO: Stability-Augmented Reinforcement Policy Optimization

Jinghan Zhang, Fengran Mo, Tharindu Cyril Weerasooriya +5

Reinforcement learning (RL) is effective in enhancing the accuracy of large language models in complex reasoning tasks. Existing RL policy optimization frameworks rely on final-ans…

cs.CV2025

ConsistCompose: Unified Multimodal Layout Control for Image Composition

Xuanke Shi, Boxuan Li, Xiaoyang Han +4

Unified multimodal models that couple visual understanding with image generation have advanced rapidly, yet most systems still focus on visual grounding-aligning language with imag…

cs.CV2025

Scaling Spatial Intelligence with Multimodal Foundation Models

Zhongang Cai, Ruisi Wang, Chenyang Gu +26

Despite remarkable progress, multimodal foundation models still exhibit surprising deficiencies in spatial intelligence. In this work, we explore scaling up multimodal foundation m…

cs.CV2025★ 1 cited

Holistic Evaluation of Multimodal LLMs on Spatial Intelligence

Zhongang Cai, Yubo Wang, Qingping Sun +21

Multimodal models have achieved remarkable progress in recent years. Nevertheless, they continue to exhibit notable limitations in spatial understanding and reasoning, the very cap…

math.OC2021★ 7 cited

Survey Descent: A Multipoint Generalization of Gradient Descent for Nonsmooth Optimization

X. Y. Han, Adrian S. Lewis

For strongly convex objectives that are smooth, the classical theory of gradient descent ensures linear convergence relative to the number of gradient evaluations. An analogous non…