Reasoning-driven universal multimodal embedding has advanced rapidly by introducing Chain-of-Thought (CoT) reasoning into the embedding pipeline. Despite the strong performance across both general and complex tasks, this paradigm suffers from two core limitations: (i) autoregressive CoT reasoning incurs high computational cost, making it impractical for low-latency retrieval; and (ii) embedding performance is heavily coupled with CoT annotation quality, making large-scale training unreliable. These raise fundamental questions:Is textual CoT the optimal form of reasoning for embedding, and can effective embedding reasoning be accomplished in latent space? To this end, we propose LaME (Latent Reasoning Multimodal Embedding), which formulates embedding-oriented latent reasoning as a weakly supervised information bottleneck. LaME employs K learnable reason tokens as a fixed-capacity bottleneck, completing all reasoning within a single forward pass. The two weak supervision signals structurally decouple contrastive from autoregressive objectives and eliminate dependence on CoT annotations, while a two-stage training pipeline ensures stable convergence. Experiments on MMEB-v2 and MRMR show that LaME achieves competitive performance, surpassing some explicit CoT-based models, while delivering 60× faster inference than explicit CoT methods and 2× faster than latent baselines with throughput comparable to discriminative embedding models. The code is available at https://github.com/PeppaWu/LaME.

Channel prediction can greatly reduce the pilot overhead and is a critical technology in the fifth-generation (5G) and the coming 6G wireless communications systems. Conventional model-based channel prediction methods suffer from limited accuracy due to imperfect temporal modeling, while existing AI-based methods suffer from limited generalization due to inadequate training strategies. Recently, large language models (LLMs) have demonstrated remarkable generalization and generation capabilities across diverse domains such as computer vision, quantitative economics, and bioinformatics, which motivates us to apply LLMs in channel prediction. In this paper, we formulate the ‘channel sentence’ based on channel correlation, where the channel is regarded as a ‘word’. Subsequently, we propose a generative pre-trained language model for channel prediction (CP-GPT). We collect 12M channel data according to the 3GPP 38.901 protocol and train CP-GPT based on the transformer decoder architecture. Moreover, we design two pre-training tasks based on the characteristics of wireless channels to enhance CP-GPT’s understanding of communications channels. We further propose a comprehensive benchmark to rigorously evaluate the capabilities of CP-GPT across multiple dimensions. The simulation results demonstrate that CP-GPT has successfully learned various channel characteristics and exhibits impressive capabilities across numerous downstream tasks.