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Multi-source AI news clustered, deduplicated, and scored 0–100 across authority, cluster strength, headline signal, and time decay.

  1. TOOL · Together AI blog English(EN) ·

    Introducing AutoJudge: Streamlined inference acceleration via automated dataset curation

    Researchers at Together AI have developed AutoJudge, a novel method to accelerate large language model inference. This technique automates the curation of task-specific datasets, enabling lossy speculative decoding without manual annotation. AutoJudge identifies critical tokens that impact downstream quality, achieving up to a 2x speedup over standard speculative decoding with minimal accuracy loss. AI

    IMPACT Accelerates LLM inference by automating dataset curation for speculative decoding, potentially reducing operational costs.

  2. RESEARCH · arXiv cs.CL English(EN) · · [8 sources]

    FlexDraft: Flexible Speculative Decoding via Attention Tuning and Bonus-Guided Calibration

    Two new research papers, Graft and FlexDraft, introduce advanced techniques for speculative decoding to accelerate large language model inference. Graft combines pruning and retrieval to fill gaps left by pruned branches, achieving significant speedups without training. FlexDraft employs attention tuning and bonus-guided calibration to adapt flexibly across different batch sizes, mitigating draft verification mismatches and improving throughput. These methods aim to overcome the latency-cost trap in LLM deployment by allowing high-quality responses at speeds closer to smaller models. AI

    FlexDraft: Flexible Speculative Decoding via Attention Tuning and Bonus-Guided Calibration

    IMPACT These advancements in speculative decoding could significantly reduce LLM inference latency and cost, enabling faster and more efficient deployment of AI applications.