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Paper#llm🔬 ResearchAnalyzed: Jan 3, 2026 16:57

Yggdrasil: Optimizing LLM Decoding with Tree-Based Speculation

Published:Dec 29, 2025 20:51
1 min read
ArXiv

Analysis

This paper addresses the performance bottleneck in LLM inference caused by the mismatch between dynamic speculative decoding and static runtime assumptions. Yggdrasil proposes a co-designed system to bridge this gap, aiming for latency-optimal decoding. The core contribution lies in its context-aware tree drafting, compiler-friendly execution, and stage-based scheduling, leading to significant speedups over existing methods. The focus on practical improvements and the reported speedup are noteworthy.
Reference

“Yggdrasil achieves up to $3.98\times$ speedup over state-of-the-art baselines.”

PermalinkArXiv
Research#llm🔬 ResearchAnalyzed: Jan 4, 2026 07:00

Latency-Optimal Cache-aided Multicast Streaming via Forward-Backward Reinforcement Learning

Published:Dec 26, 2025 10:00
1 min read
ArXiv

Analysis

This article likely presents a novel approach to optimizing multicast streaming, focusing on minimizing latency using reinforcement learning techniques. The use of cache-aiding suggests an attempt to improve efficiency by leveraging cached content. The 'Forward-Backward' aspect of the reinforcement learning likely refers to the algorithm's structure, potentially involving both forward and backward passes to refine its learning process. The source being ArXiv indicates this is a research paper, likely detailing the methodology, results, and implications of this approach.

Key Takeaways

    Reference

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    PermalinkArXiv