Tag
InstanceControl enables multi-instance controllable image generation without manual instance labeling by leveraging a vision-language model to establish instance-level correspondences between text prompts and visual conditions, with adaptive mask refinement for improved accuracy.
This technical report presents Xiaomi-GUI-0, a native multimodal GUI agent trained and evaluated in real-device environments using a hybrid infrastructure and a progressive training pipeline, achieving high success rates and improved stability on real-world mobile tasks.
Aloe-Vision introduces a family of open medical Vision-Language Models trained on a quality-filtered mixture of medical and general data, along with a new benchmark CareQA-Vision for reliable evaluation. The models demonstrate competitive performance while highlighting vulnerabilities to adversarial inputs.
Chunkr is an open-source document intelligence service that converts PDFs, PPTs, Word docs, and images into structured chunks for RAG and LLM pipelines. It features layout analysis with OCR, structured HTML/Markdown output, vision-language model processing, and self-hosted deployment via Docker Compose with configurable LLM providers.
A developer shares their experience using a local Qwen VL model on an RTX 3060 to parse Japanese receipts into JSON, replacing Google Vision, with results showing accurate extraction of key fields at ~31 seconds per receipt.
This paper presents ZooClaw-FashionSigLIP2, a fashion-specialized vision-language model that achieves superior retrieval performance through full fine-tuning with knowledge distillation and weight interpolation, outperforming larger backbones and LoRA. It also introduces a new high-quality benchmark, ZooClaw-Fashion, and analyzes structural biases in existing datasets.
The paper introduces the Latent Bridge, a trainable continuous channel that couples a slow reasoning VLM (Qwen3-VL-8B-Thinking) and a fast reactive VLM (MiniCPM-o 4.5) for real-time game agents. Experiments on Atari games and MetaDrive show it matches or outperforms the text-based bridge while avoiding destructive interference when used alone.
Physics Question Scene Graph (PQSG) is a hierarchical question-based pipeline using VLMs to evaluate video generation models' physical plausibility with fine-grained violation detection. It introduces the FinePhyEval dataset and shows higher correlation with human judgments than prior work.
This paper introduces CF-World, a counterfactual benchmark to evaluate whether text-to-image models rely on causal reasoning or mere pattern matching. Experiments show all models degrade sharply in counterfactual settings, suggesting their understanding is limited to tightly coupled visual-textual patterns rather than genuine causal reasoning.
ABACUS is a unified vision-language model that handles multiple counting tasks and count-faithful image generation without benchmark-specific training, achieving state-of-the-art results across seven benchmarks.
Semantic Browsing introduces a method for controlled diversity in text-to-image generation by using a Vision Language Model with an agentic workflow to generate structured, interpretable variations based on semantic decisions.
Loft Orbital's YAM-9 satellite runs Google's Gemma 3 vision-language model onboard for real-time image analysis, reducing downlink bandwidth and latency by deciding what data to send to Earth.
Researchers trained a vision-language model without a vision encoder for only $100, inspired by Gemma 4 12B, achieving a 30% reduction in end-to-end latency on an M3 Pro MacBook.
NAVI-Orbital demonstrates the first in-orbit deployment of a zero-shot vision-language model (Gemma 3) on a LEO satellite, enabling autonomous scene classification and semantic compression of Earth observation data without fine-tuning.
FinAcumen is a framework that accumulates reasoning experience from prior trajectories into a persistent memory bank for financial multimodal reasoning, improving performance across four benchmarks while maintaining a frozen 8B vision-language model.
A test of the open-weight MiniMax M3 model using MLX-VLM on a Mac Studio shows it can autonomously fill out a US customs form from a driver's license photo and a scanned document, using tool calls for fields, checkboxes, and signature.
A satellite called Yam-9 used Google DeepMind's Gemma 3 vision-language model in orbit to autonomously identify areas of interest based on natural language queries, marking the first reported use of a VLM in space and signaling a shift toward more autonomous satellite operations.
LLaVA-OneVision-2 introduces codec-stream tokenization for efficient video understanding, significantly outperforming Qwen3-VL-8B on temporal and spatial benchmarks. The model, data, and code are open-sourced.
SciOrch presents an 8B vision-language model trained with MCTS to coordinate multiple expert LLMs for multimodal scientific reasoning, achieving superior performance while reducing API costs.
This paper from Meta and Carnegie Mellon presents a multi-modal vision-language model pipeline for detecting AI-generated content on social media, achieving state-of-the-art performance and positive downstream impacts on user engagement.