A comprehensive index of artificial intelligence and machine-learning research with AI-generated summaries, citation metrics, and direct links to papers and code.
B.‐C. CHEN, Zhaofeng Zhang, Nicolas Langrené, et al.
This review surveys foundational and advanced prompt engineering techniques for LLMs and VLMs, evaluates them with subjective and objective metrics, and examines prompt engineering's role in AI security.
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VLM-3R couples 3D reconstructive instruction tuning with scalable data curation to augment Vision-Language Models with instruction-aligned 3D reconstruction.
Juan S. Santillana
VectraYX-Vision-1B is a sub-2B Spanish/LATAM cybersecurity VLM with structured reasoning and tool use, but current vision SFT yields near-zero visual grounding.
Unknown
This paper describes standard recipes for building and training vision-language-action models (VLAs) that adapt VLMs to output robot actions, enabling fast training, fast inference, and better generalization.
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SpatialVLM endows vision-language models with spatial reasoning capabilities by generating and training on a large-scale spatial VQA dataset.
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Introduces GPT4Scene, a framework for understanding 3D scenes from videos using vision-language models, and ScanAlign, a multimodal dataset with 165K aligned pairs for training smaller VLMs.
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SHARP is a representation-level intervention framework that modulates hallucination in LVLMs by steering internal representations.
Unknown
Proposes MRRE, a training-free inference-time method using representation engineering to enhance multilingual reasoning in LLMs and LVLMs without additional training data.
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This paper explores how generative world models can provide priors to vision-language models (VLMs) for improved understanding of world dynamics in common vision tasks.
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FastVLM introduces an efficient vision encoding method that reduces computational cost while maintaining high performance in text-rich image understanding tasks.
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This paper introduces LVLM-eHub, a comprehensive evaluation benchmark for Large Vision-Language Models, systematically assessing their capabilities across diverse multimodal tasks.
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This paper assesses the effectiveness of recent large vision-language models (LVLMs) in achieving artificial general intelligence, focusing on their performance across various tasks.