RONA: ROTATION AND NOISE-AWARE ABSORPTION FOR LOW-BIT UNIVERSAL MULTIMODAL EMBEDDING

Universal multimodal embedding (UME) encodes text, images, video, and visually rich documents into a shared space for cross-modal retrieval. Although reasoning-enhanced methods improve embedding quality, their additional inference-time computation limits low-latency, high-throughput retrieval, leaving direct encoders as the more deployment-friendly choice. Their large backbones, however, still incur substantial serving cost. We study whether post-training quantization (PTQ) can compress UME without sacrificing retrieval accuracy. Across 78 MMEB-V2 tasks, strong PTQ methods remain near full precision at W4A16, W8A16, W4A8, and W8A8, yet collapse at W4A4, a failure not explained by layer-wise reconstruction error. We show that low-bit UME must preserve retrieval geometry and that directions safe under clean activations become harmful when coupled with activation quantization noise. We therefore propose RONA, which combines function-preserving rotations with a group-aligned, quantization-noise-aware safe subspace for weight reparameterization. A per-layer absorption coefficient, calibrated against grouped-GPTQ weight reconstructions and query–target embedding geometry, controls how strongly each layer uses these safe directions while the backbone remains frozen. RONA achieves 73.55 at W4A4, exceeding the strongest PTQ baseline by 3.65 points and retaining 94.4% of full-precision performance. Gains of 6.20 and 2.29 points on two 2B backbones demonstrate its effectiveness across model scales and architectures.