ScreenAnnotator introduces a unified annotation atom schema, on-policy Bayesian verification loop, and template-driven synthesis to generate reusable multi-task reasoning data for VLMs, reporting nearly 100% accept rate on flowcharts, 77% on GUI screenshots, and a 35.1-point accuracy gain after fine
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CRAFT is a Pareto-front prompt optimizer that allocates scarce LLM validation calls to candidates near the current front using accuracy- and cost-oriented generators plus NSGA-II retention.
POCA combines Pareto optimization with curriculum alignment to improve multi-reward reinforcement learning for visual text generation without relying on weighted sums.
MOSAIC is a scaling-aware data selection framework that outperforms baselines in training end-to-end autonomous driving planners, achieving comparable or better EPDMS scores with up to 80% less data.
Localization uncertainty visualization in AI predictions improves human annotation quality and speed by redirecting effort toward high-uncertainty boxes.
citing papers explorer
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From Bounding Boxes to Visual Reasoning: An On-Policy Data Annotation Tool for Vision-Language Models
ScreenAnnotator introduces a unified annotation atom schema, on-policy Bayesian verification loop, and template-driven synthesis to generate reusable multi-task reasoning data for VLMs, reporting nearly 100% accept rate on flowcharts, 77% on GUI screenshots, and a 35.1-point accuracy gain after fine
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CRAFT: Cost-aware Refinement And Front-aware Tuning of Prompts
CRAFT is a Pareto-front prompt optimizer that allocates scarce LLM validation calls to candidates near the current front using accuracy- and cost-oriented generators plus NSGA-II retention.
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POCA: Pareto-Optimal Curriculum Alignment for Visual Text Generation
POCA combines Pareto optimization with curriculum alignment to improve multi-reward reinforcement learning for visual text generation without relying on weighted sums.
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Scaling-Aware Data Selection for End-to-End Autonomous Driving Systems
MOSAIC is a scaling-aware data selection framework that outperforms baselines in training end-to-end autonomous driving planners, achieving comparable or better EPDMS scores with up to 80% less data.
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From Model Uncertainty to Human Attention: Localization-Aware Visual Cues for Scalable Annotation Review
Localization uncertainty visualization in AI predictions improves human annotation quality and speed by redirecting effort toward high-uncertainty boxes.