The October 31, 2025 paper introduces Continuous Autoregressive Language Models (CALM), a new paradigm designed to overcome the efficiency bottleneck of traditional Large Language Models (LLMs) by shifting from discrete token-by-token prediction to continuous next-vector prediction. This approach compresses a chunk of multiple tokens into a single continuous vector using a high-fidelity autoencoder, thereby reducing the number of generative steps and significantly improving the performance-compute trade-off. To manage the challenges of operating in this continuous, likelihood-free domain, the framework includes a comprehensive toolkit: an energy loss function for training, a novel, sample-based evaluation metric called BrierLM, and likelihood-free algorithms for temperature sampling. Ultimately, the CALM framework establishes semantic bandwidth as a powerful new axis for scaling language models, enabling superior efficiency compared to discrete baselines. Source: October 31, 2025 CONTINUOUS AUTOREGRESSIVE LANGUAGE MODELS https://arxiv.org/pdf/2510.27688