新闻

一个模型，四种模态：文本、图像、音频、视频。业界领先的 1.6B 和 0.9B 全能型向量模型。

We introduce frozen-encoder model composition, a novel approach to multimodal embedding models. We build on the VLM-style architecture, in which non-text encoders are adapted to produce input for a language model, which in turn generates embeddings for all varieties of input. The backbone text embedding models and the added non-text media encoders remain frozen. We only trained the connecting components, representing 0.35% of the total weights. The resulting jina-embeddings-v5-omni suite encodes text, image, audio, and video into a single semantic embedding space, producing competitive results with models 5-7x its size.

将任何多模态大模型转化为小型音频向量模型，仅需 1/25 的数据量即可超越 CLAP。

一个通过读取原始数字来为向量模型提取指纹的微型 Transformer。无需特征工程。

两款性能领先的 1B 以下多语言向量模型，现已在 Elastic Inference Service、Llama.cpp 和 MLX 上可用。

Text embedding models are widely used for semantic similarity tasks, including information retrieval, clustering, and classification. General-purpose models are typically trained with single- or multi-stage processes using contrastive loss functions. We introduce a novel training regimen that combines model distillation techniques with task-specific contrastive loss to produce compact, high-performance embedding models. Our findings suggest that this approach is more effective for training small models than purely contrastive or distillation-based training paradigms alone. Benchmark scores for the resulting models, jina-embeddings-v5-text-small and jina-embeddings-v5-text-nano, exceed or match the state-of-the-art for models of similar size. jina-embeddings-v5-text models additionally support long texts (up to 32k tokens) in many languages, and generate embeddings that remain robust under truncation and binary quantization. Model weights are publicly available, hopefully inspiring further advances in embedding model development.

We frame embedding inversion as conditional masked diffusion, recovering all tokens in parallel through iterative denoising rather than sequential autoregressive generation. A masked diffusion language model is conditioned on the target embedding via adaptive layer normalization, requiring only 8 forward passes through a 78M parameter model with no access to the target encoder. On 32-token sequences across three embedding models, the method achieves 81.3% token accuracy and 0.87 cosine similarity.

We present a compression method for unit-norm embeddings that achieves 1.5x compression, 25% better than the best prior lossless method. The method exploits that spherical coordinates of high-dimensional unit vectors concentrate around pi/2, causing IEEE 754 exponents to collapse to a single value and high-order mantissa bits to become predictable, enabling entropy coding of both. Reconstruction error is below 1e-7, under float32 machine epsilon. Evaluation across 26 configurations spanning text, image, and multi-vector embeddings confirms consistent improvement. The method requires no training.

Vision encoders have remained comparatively small while language models scaled from billions to hundreds of billions of parameters. This survey analyzes vision encoders across 70+ vision-language models from 2023–2025 and finds that training methodology matters more than encoder size: improvements in loss functions, data curation, and feature objectives yield larger gains than scaling by an order of magnitude. Native resolution handling improves document understanding, and multi-encoder fusion captures complementary features no single encoder provides. We organize encoders into contrastive, self-supervised, and LLM-aligned families, providing a taxonomy and practical selection guidance for encoder design and deployment.

全新 2B 视觉语言模型在多语言 VQA 上实现 SOTA，在纯文本任务上没有灾难性遗忘。