arXiv AI
Making Language the Low-Rate Code of Vision: Successive Refinement from Words to Pixels
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arXiv AI
Jun 26
Reconstruction Alignment Improves Unified Multimodal Models
arXiv:2509. 07295v4 Announce Type: replace-cross Abstract: Unified multimodal models (UMMs) unify visual understanding and generation within a single architecture.
arXiv Computation and Language
3d ago
PixelBench: Can LLMs Understand and Generate Images Pixel by Pixel?
PixelBench is a benchmark that evaluates large language models on their ability to generate and understand images pixel by pixel, without relying on learned image tokenizers or decoders. The benchmark includes text-to-pixel (T2P) and image-to-pixel (I2P) generation tasks across multiple resolutions, as well as a pixel-understanding evaluation. Models produce an RGB palette followed by a grid of indices that map each pixel to a palette color, and the study shows that strong models can generate recognizable images from text and outperform specialized pixelization algorithms at low resolutions, with performance correlating with pixel-understanding ability and improving up to a point before declining at high resolutions. Additionally, Pixel Reconstruction Tuning (PRT) augments visual instruction tuning with caption‑palette‑grid trajectories, and post‑training Molmo2‑4B with PRT data improves performance on 16 visual‑centric benchmarks, demonstrating the utility of explicit pixel reconstruction as auxiliary supervision.
arXiv Machine Learning
Oct 12
Vision2Code: A Multi-Domain Benchmark for Evaluating Image-to-Code Generation
Vision2Code is a new benchmark for image-to-code generation that covers 15 domains and 539 test examples without relying on reference source code. It evaluates generated programs by executing them and comparing rendered outputs to the original images using domain‑specific rubrics, yielding scores that correlate better with human judgments than generic metrics. The study shows domain variability in model performance and demonstrates that the scoring method can improve fine‑tuning of models like Qwen3.5‑9B and Phi‑4‑MM via rejection sampling.
arXiv Computer Vision
Sep 2
A Glance Is All You Need: Single-Pass Fine-Grained Image Captioning with SimLoss
arXiv:2609.00591v1 Announce Type: new Abstract: An image may be worth a thousand words, but most captioning models describe it in only a few. Modern vision-language models produce fluent high-level c...
arXiv AI
Sep 2
Vision Is Not Overhead: One-Pass Block Drafting for Lossless Speculative Decoding in Vision-Language Models
The paper introduces GLANCE, a one‑pass block drafting method that enables lossless speculative decoding for vision‑language models. By using a block‑diffusion head that reads the fused vision‑language state, GLANCE eliminates the need for the drafter to process the image at every step, allowing it to fill an entire block in a single forward pass. Experiments show that GLANCE can decode up to 2.93× faster than autoregressive decoding while maintaining exact greedy decoding results across multiple tasks.
arXiv AI
Oct 2
LensVLM: Selective Context Expansion for Compressed Visual Representation of Text
LensVLM is an inference framework and post‑training recipe that lets Vision‑Language Models (VLMs) process compressed images of text by selectively expanding only the relevant parts back to full resolution. Using Qwen3.5‑9B‑Base, LensVLM achieves accuracy comparable to full‑text models at 4.3× compression and outperforms other compression baselines up to 10.1× across seven text QA benchmarks, while also improving performance on multimodal document and code tasks as compression increases.