vlm

This paper investigates how visual evidence should be represented for predicting item difficulty in mathematics assessments, comparing text-only, visual textualization, and image-native modeling with LLMs and VLMs. It finds image-native modeling is a competitive alternative to textualization, with performance depending on VLM adaptation.

VisionPsy-Nano-460M-Flash is a new 460M vision-language model that uses only 64 visual tokens per image, cutting first-token latency to 0.3s on iPhone while retaining roughly 99% of the full model's benchmark score. The optimization trades off some OCR and fine-detail quality.

A tweet claims Qwen3.8-Max is the best object detection VLM, excelling across satellite, infrared, document, and hand-drawn images, with examples shared.

Hunyuan3D-Buffalo 1.0 is a unified multimodal model for 3D generation, understanding, and editing, trained on an 87M-scale 3D multimodal corpus. It combines Hunyuan3D-VLM and Hunyuan3D-DiT to achieve state-of-the-art performance on text-to-3D generation and 3D editing benchmarks.

This paper highlights that VLMs for chest x-ray report generation can score well on benchmarks while erasing clinically meaningful terms and introducing biased language, and proposes a framework to measure these failures.

This paper identifies a stylistic inconsistency in MLLMs where their comprehension is robust but safety can be bypassed by stylistic triggers. It proposes Adversarial Style Optimization (ASO) using GRPO to fine-tune an image-editing model to enhance jailbreak attacks.

Introduces ASCIITermDraw Bench, a benchmark designed to evaluate vision-language models on ASCII art generation and editing tasks.

HPD-Parsing introduces a hierarchical parallel decoding paradigm for VLM-based document parsing, replacing full-page autoregressive generation to achieve 4,752 tokens per second throughput (2.62x faster than prior models) while maintaining competitive accuracy.

O-VAD introduces a training-free agentic framework for industrial video anomaly detection that tracks object state evolution over time and reasons over temporal trajectories to identify abnormal objects, outperforming existing VLM and VAD methods on three datasets.

This paper formulates the Foundation Model Deployment Portfolio problem to minimize total cost of ownership for deploying LLMs and VLMs across transportation management functions, considering quality, latency, and safety constraints. A case study shows cost savings of 97% compared to all-closed-API baselines.

OvisOCR2 is a 0.8B end-to-end document-parsing VLM that tops the OmniDocBench leaderboard, outperforming pipeline OCR systems on real-world scanned medical documents with higher accuracy and efficiency.

This paper introduces an agentic workflow that uses LLMs and VLMs to iteratively generate and improve high-quality mathematical diagrams for K-12 education, addressing the reliability gap in AI-generated visual aids.

GrAInS is a contrastive gradient-based method that uses Integrated Gradients to identify influential tokens and construct steering vectors for inference-time steering of LLMs and VLMs, improving truthfulness and reducing hallucinations without degrading fluency.

CLAP proposes a method to convert pretrained vision-language models (VLMs) into vision-language-action models (VLAs) by prepending natural-language action descriptions to action token sequences, preserving semantic capabilities without architectural changes. It achieves 90.8% on LIBERO and improves robustness.

Introduces TSRouter, a graph-based dynamic routing framework that selects the optimal modality (LLM or VLM) and model for time-series reasoning tasks, achieving 16-46% relative improvement over baselines and demonstrating zero-shot generalization.

Sakana AI, in collaboration with MIT/NYU, published a study nominated for the GECCO 2026 Best Paper Award, which fully replicated the classic Picbreeder system using VLM agents to explore the key ingredients needed for open-endedness.

This paper presents a comprehensive empirical study on using foundation models (LLMs and VLMs) for automatic CAD generation from natural language, introducing the LLMForge framework with two critique regimes (IterTracer and IterVision) and evaluating seven models on a benchmark of 97 engineering design problems.

The article announces the open-source release of small_vlm_video_analysis, a tool that uses small vision-language models locally to verify if procedural videos comply with predefined SOPs, including frame description, rule-based judging, and a visualization viewer.

This paper introduces PolicyShiftBench, a benchmark for policy-adaptive image guardrails, and PolicyShiftGuard, a compact model trained with a two-stage method that improves performance under shifting safety policies.

mlx-vlm v0.6.4 is released with support for 5 new model families, TTS/STT endpoints, and significant performance improvements including TurboQuant and continuous batching.