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This paper analyzes TeX's architectural mismatch with LLM workflows, stemming from its lack of an explicit structural representation, to illustrate its limitations on generated semantics and error localization, and introduces .tmu, a low-entropy tree-structured representation, a more scalable and LLM-friendly data repr...
DSpark is introduced, a speculative decoding framework that unifies high-throughput parallel generation with adaptive, load-aware verification, and enables performance tiers that were previously unattainable, shifting the Pareto frontier of the DeepSeek-V4 serving system.
This work proposes a new framework that integrates structured reasoning and geometric precision through a teacher-student architecture and outperforms classical reasoning-based baselines in zero-shot reasoning, waypoint accuracy, and inference efficiency.
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