EBind: a practical approach to space binding — mentioned by rahimnathwani

Thu, 20 Nov 2025 20:28:37 GMT

Paper: https://arxiv.org/html/2511.14229v1 Abstract: We simplify space binding by focusing on two core components, a single encoder per modality and high-quality data; enabling training state-of-the-art models on a single GPU in a few hours as opposed to multiple days. We present EBind, an Easy, data-centric, and parameter-efficient method to Bind the embedding spaces of multiple contrastive models. We demonstrate that a simple 1.8B-parameter image-text-video-audio-3D model can outperform models 4 to 17× the size. The key to achieving this is a carefully curated dataset of three complementary…

Paper and discussion history. A mention is not an endorsement.

EBind: a practical approach to space binding · HackerPapers discussions

EBind: a practical approach to space binding — mentioned by rahimnathwani