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ClawGUI: A Unified Framework for Training, Evaluating, and Deploying GUI Agents On the Robustness of Watermarking for Autoregressive Image Generation Revisiting Compositionality in Dual-Encoder Vision-Language Models: The Role of Inference Anthropogenic Regional Adaptation in Multimodal Vision-Language Model From Redaction to Restoration: Deep Learning for Medical Image Anonymization and Reconstruction The Salami Slicing Threat: Exploiting Cumulative Risks in LLM Systems BoxTuning: Directly Injecting the Object Box for Multimodal Model Fine-Tuning Semantic-Geometric Dual Compression: Training-Free Visual Token Reduction for Ultra-High-Resolution Remote Sensing Understanding Lightweight Low-Light Image Enhancement via Distribution-Normalizing Preprocessing and Depthwise U-Net Back to the Barn with LLAMAs: Evolving Pretrained LLM Backbones in Finetuning Vision Language Models Pseudo-Unification: Entropy Probing Reveals Divergent Information Patterns in Unified Multimodal Models QShield: Securing Neural Networks Against Adversarial Attacks using Quantum Circuits Evaluating the Impact of Medical Image Reconstruction on Downstream AI Fairness and Performance Retinal Cyst Detection from Optical Coherence Tomography Images LoViF 2026 The First Challenge on Weather Removal in Videos STORM: End-to-End Referring Multi-Object Tracking in Videos Data-Efficient Surgical Phase Segmentation in Small-Incision Cataract Surgery: A Controlled Study of Vision Foundation Models Rethinking the Diffusion Model from a Langevin Perspective Zero-shot World Models Are Developmentally Efficient Learners Edu-MMBias: A Three-Tier Multimodal Benchmark for Auditing Social Bias in Vision-Language Models under Educational Contexts VGA-Bench: A Unified Benchmark and Multi-Model Framework for Video Aesthetics and Generation Quality Evaluation Degradation-Consistent Paired Training for Robust AI-Generated Image Detection FREE-Switch: Frequency-based Dynamic LoRA Switch for Style Transfer Demographic and Linguistic Bias Evaluation in Omnimodal Language Models FlowPalm: Optical Flow Driven Non-Rigid Deformation for Geometrically Diverse Palmprint Generation Cross-Cultural Value Awareness in Large Vision-Language Models I Walk the Line: Examining the Role of Gestalt Continuity in Object Binding for Vision Transformers GLEaN: A Text-to-image Bias Detection Approach for Public Comprehension From UAV Imagery to Agronomic Reasoning: A Multimodal LLM Benchmark for Plant Phenotyping Not Your Stereo-Typical Estimator: Combining Vision and Language for Volume Perception
Enhanced Polarization Locking in VCSELs
Zifeng Yuan, Dewen Zhang, Lei Shi, Yutong Liu, Aaron Danner · 2026-04-02 · via cs.CV updates on arXiv.org

While optical injection locking (OIL) of vertical-cavity surface-emitting lasers (VCSELs) has been widely studied in the past, the polarization dynamics of OIL have received far less attention. Recent studies suggest that polarization locking via OIL could enable novel computational applications such as polarization-encoded Ising computers. However, the inherent polarization preference and limited polarization switchability of VCSELs hinder their use for such purposes. To address these challenges, we fabricate VCSELs with tailored oxide aperture designs and combine these with bias current tuning to study the overall impact on polarization locking. Experimental results demonstrate that this approach reduces the required injection power (to as low as 3.6 μW) and expands the locking range. To investigate the impact of the approach, the spin-flip model (SFM) is used to analyze the effects of amplitude anisotropy and bias current on polarization locking, demonstrating strong coherence with experimental results.