{"slug": "emcom-diffusion-probing-visual-reflection-in-emergent-languages-via-image", "title": "EmCom-Diffusion: Probing Visual Reflection in Emergent Languages via Image Generation", "summary": "Researchers propose EmCom-Diffusion, a framework that measures visual reflection in emergent languages by reconstructing images from emergent messages using a finetuned diffusion model and comparing them to originals. Validated on MS-COCO, it captures visual content missed by existing metrics like CBM and TopSim.", "body_md": "arXiv:2607.03752v1 Announce Type: cross\nAbstract: Measuring the extent to which emergent languages encode the visual content of their inputs is an open problem. We refer to this property as visual reflection: the extent to which emergent messages preserve information about their source images that can be recovered without appeal to the speaker-listener pair that produced them. Existing metrics measure it only indirectly, through proxies such as human-defined concept inventories, natural-language captions, structural distance correlations, or Referential Game accuracy, each of which can either miss visual content the message encodes or credit content it does not. We propose EmCom-Diffusion, an evaluation framework that measures visual reflection directly: it reconstructs each input image from its emergent message and compares the reconstruction with the original image itself, rather than with human-defined targets. Concretely, it finetunes a pretrained text-to-image diffusion model on (image, emergent-message) pairs and scores visual reflection as the perceptual similarity between the reconstructed and original images, operating generatively rather than discriminatively. Instantiating it on MS-COCO with a Referential Game, we validate the metric against random and fixed-token baselines under three pretrained visual encoders, and compare it against four existing metrics (CBM, supervised translation, TopSim, and R@1). EmCom-Diffusion captures visual content the other metrics miss.", "url": "https://wpnews.pro/news/emcom-diffusion-probing-visual-reflection-in-emergent-languages-via-image", "canonical_source": "https://www.machinebrief.com/news/emcom-diffusion-probing-visual-reflection-in-emergent-langua-rekg", "published_at": "2026-07-07 04:00:00+00:00", "updated_at": "2026-07-07 19:39:13.372627+00:00", "lang": "en", "topics": ["artificial-intelligence", "machine-learning", "computer-vision", "generative-ai", "natural-language-processing"], "entities": ["EmCom-Diffusion", "MS-COCO", "CBM", "TopSim", "Referential Game"], "alternates": {"html": "https://wpnews.pro/news/emcom-diffusion-probing-visual-reflection-in-emergent-languages-via-image", "markdown": "https://wpnews.pro/news/emcom-diffusion-probing-visual-reflection-in-emergent-languages-via-image.md", "text": "https://wpnews.pro/news/emcom-diffusion-probing-visual-reflection-in-emergent-languages-via-image.txt", "jsonld": "https://wpnews.pro/news/emcom-diffusion-probing-visual-reflection-in-emergent-languages-via-image.jsonld"}}