frozen-backbone

Fusion Embedding introduces a family of models that add audio to a frozen vision-language embedding backbone, enabling a unified space for text, image, video, and audio retrieval. The models train only lightweight adapters and achieve audio-image retrieval without paired audio-visual data.

Baseten releases GLM-5.2-Vision, a vision-language model that adds MoonViT vision encoder to GLM-5.2 via a trained PatchMerger projector, keeping the text backbone and vision tower frozen. The model is quantized to NVFP4 for efficient inference on Blackwell hardware.

This paper investigates whether explicit domain adaptation methods are beneficial for sentiment transfer when using frozen pre-trained language model backbones, finding that effectiveness depends on whether the backbone already possesses target-domain knowledge.

This paper introduces a lightweight approach for remaining useful life estimation using frozen embeddings from the Chronos-2 time-series foundation model combined with a simple regression head, achieving superior performance on industrial sensor data compared to baseline methods.

Introduces Orthrus, a method that injects a trainable diffusion attention module into a frozen autoregressive transformer to achieve up to 7.8× tokens per forward pass and ~6× wall-clock speedup on MATH-500, with provably identical output distribution to the base Qwen3-8B model. The approach requires minimal additional parameters and training, and avoids the TTFT penalty of external drafters.