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20242026
most citedMarsCode Agent: AI-native Automated Bug Fixing

3 citations · 5 across the 14 of their papers we have counts for

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cs.CV2026

SpatialGuard: Harness-Guided Verifiable Spatial Reasoning for Text-to-Image Generation

Ziyun Qian, Zizhi Chen, Yizhou Liu +3

Complex 3D spatial text to image generation requires models to convert natural language into stable visual geometry, not merely semantic appearance. Existing prompt-driven or layou…

cs.CV2026

OmniFysics: Towards Physical Intelligence Evolution via Omni-Modal Signal Processing and Network Optimization

Minghao Han, Dingkang Yang, Yue Jiang +2

The autonomous evolution of networked AI systems relies heavily on robust environmental perception. However, physical understanding remains brittle in current models because key ph…

cs.CV2025

Forging a Dynamic Memory: Retrieval-Guided Continual Learning for Generalist Medical Foundation Models

Zizhi Chen, Yizhen Gao, Minghao Han +4

Multimodal biomedical Vision-Language Models (VLMs) exhibit immense potential in the field of Continual Learning (CL). However, they confront a core dilemma: how to preserve fine-g…

cs.CV2025

FysicsWorld: A Unified Full-Modality Benchmark for Any-to-Any Understanding, Generation, and Reasoning

Yue Jiang, Dingkang Yang, Minghao Han +6

Despite rapid progress in multimodal large language models (MLLMs) and emerging omni-modal architectures, current benchmarks remain limited in scope and integration, suffering from…

cs.CV2025

MorphSeek: Fine-grained Latent Representation-Level Policy Optimization for Deformable Image Registration

Runxun Zhang, Yizhou Liu, Li Dongrui +2

Deformable image registration (DIR) remains a fundamental yet challenging problem in medical image analysis, largely due to the prohibitively high-dimensional deformation space of…

cs.CV2025

Adaptive Reinforcement for Open-ended Medical Reasoning via Semantic-Guided Reward Collapse Mitigation

Yizhou Liu, Dingkang Yang, Zizhi Chen +5

Reinforcement learning (RL) with rule-based reward functions has recently shown great promise in enhancing the reasoning depth and generalization ability of vision-language models…