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TreeSpark introduces calibrated, load-adaptive draft trees for semi-autoregressive speculative decoding in language models, increasing draft token acceptance by 15-25% and speeding up decoding by 8-14%.
DSpark is a speculative decoding framework that combines semi-autoregressive draft generation with confidence-scheduled verification to accelerate LLM inference and improve throughput in high-concurrency settings.
Introduces a semi-autoregressive framework that combines latent block diffusion with temporal point processes for generating asynchronous event sequences, reducing error accumulation while enabling variable-length output.