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CFM-Bench reveals that without a unified evaluation framework, the true potential of channel foundation models remains obscured, hindering meaningful comparisons with task-specific architectures.
Small language models can achieve near state-of-the-art Text-to-SQL performance with just a fraction of the computational resources required by large models.
Generative recommenders can slash latency by up to 38% simply by dynamically juggling GPU memory between embedding and KV caches, a feat current systems miss.
