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Introduces PoVisLE, a Polish vision-language evaluation benchmark with 1,117 images and 2,366 manually annotated VQA pairs, designed to assess culturally grounded multimodal understanding beyond surface-level recognition.
An empirical attribution study of failures in multi-page visually rich document understanding (MP-VRDU), isolating representation, selection, and reasoning failure modes and providing guidance for building such systems under fixed compute budgets.
This paper introduces M3R-Bench, a unified evidence-grounded benchmark for multimodal metaphor understanding with 1,000 image-text instances, and proposes M3R-Reasoner, an 8B-parameter model combining curriculum-based reasoning supervision and reinforcement learning that outperforms larger proprietary models.
This paper proposes a commutation theory for label-free reliability in vision-language figure reading, showing that consistency-based methods have a computable blind spot and introducing an Equivariance-Consistency Score enhanced by cyclic relabeling.
NVIDIA releases Nemotron Parse 2.0, a document image parsing model that converts scanned PDFs and images into structured text with layout, bounding boxes, and reading order, adding multilingual OCR improvements and chart-aware parsing.
Introduces CARGO-VL, a group-relative optimization framework for vision-language models that improves handling of conflicting image-text evidence and unsupported-answer avoidance via counterfactual consistency and risk-constrained control, along with the XMC conflict training resource.
ChronoVision is a multimodal framework that improves temporal reasoning in vision-language models by aligning visual logic with latent imagery, using a Reconstructive Visual Head, ROI Attention Locating module, and reinforcement learning. It introduces the Vbvr-VQA dataset and achieves SOTA accuracy on temporal tracking benchmarks.
This repository provides INT8 ConvRot-quantized ComfyUI safetensors of Qwen3-VL-32B, including a MiniMax-H3 conditioning encoder with layers 0-49 and an optional prompt-enhancement tail for layers 50-63, designed for use in ComfyUI on 32GB GPUs.
This paper introduces an information-asymmetric spot-the-difference task to measure epistemic vigilance in vision-language models, finding that models often overlook private evidence to agree with partners. Model steering to reduce sycophancy improves reliability in cooperative tasks.
This paper describes a two-stage vision-language adaptation system for Nepali meme classification, using Qwen3-VL-8B-Instruct with LoRA fine-tuning and contrastive learning. The system achieved 2nd place in hate speech detection and 4th in sentiment analysis at the CHiPSAL 2026 shared task.
Introduces CAPEval, a caption evaluation framework that decouples coverage and precision, showing that coverage better predicts vision-language understanding performance while precision better predicts text-to-image generation performance.
DeepVoyager-VL proposes a long-horizon multimodal deep-search framework that integrates visual evidence into intermediate reasoning, using a multimodal event graph for data synthesis and fine-tuning without reinforcement learning, achieving strong performance across ten benchmarks.
3DZip is a three-stage token compression framework for 3D vision-language models that uses voxelization, diversity-guided anchor selection, and spatial constraint merging to reduce tokens while preserving spatial reasoning. It retains 94.7% of original performance with only 128 tokens and achieves 1.92x faster inference on 3DQA benchmarks.
This paper presents ReBA (Relax Within, Balance Across), a geometry-guided load-balancing method for vision-language mixture-of-experts models. It addresses modality-specific routing imbalance by balancing image and text separately and grouping correlated visual tokens, improving robustness across resolution and composition shifts.
Introduces Poplar, a scalable Specify-Render-Inspect pipeline for synthesizing human-centric image datasets, and releases Poplar-9K, a curated dataset of 9,401 image-text pairs with auditable inspection records.
Mage-VL is introduced as a multimodal AI model that handles images, text, and video understanding in a single model, enabling richer interactive applications.
This paper presents VLD-RAG, an agentic multimodal retrieval-augmented generation framework for question answering over long, visually-rich documents. It uses a page-preserving index and a verifier-guided agent workflow to improve cross-page evidence retrieval and reasoning, outperforming prior vision-based baselines on benchmarks like LongDocURL and MMLongBench-Doc.
RRS-10K is a benchmark dataset for evaluating vision-language models on rare remote sensing image interpretation, containing over 10,000 military-related images and multiple task formats. Evaluation of 52 models reveals moderate zero-shot performance and weaknesses in visual grounding and complex reasoning.
ProcAgent is a fully on-device, agentic, vision-based procedural assistant that uses a propose-and-verify architecture for real-time adaptive guidance on an NVIDIA Jetson AGX Orin. It supports both reactive and proactive modes with human-in-the-loop confirmation, achieving responsive interaction and positive user study ratings.
This paper investigates why multimodal LLMs fail on vision-centric tasks when visual evidence conflicts with language priors, introducing the WhatIfVis benchmark and showing that models often encode visual evidence but cannot reliably control their reliance on it.