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Latent Core Tokenizer: Compress, but Meaningfully

Paper recorded by Signals 4 on 2026-10-08 in cs.CL. Abstract reproduced from arXiv; link to the original below.

Published 2026-10-08 on arXiv · recorded by Signals 4 on 2026-10-09

Category: cs.CL · 自然语言处理 · first seen 2026-10-09

Abstract

Tokenizers are commonly optimized for compression, but a compact vocabulary does not necessarily distribute its capacity evenly across languages. We introduce the Latent Core Tokenizer (LCT), a language-agnostic approach that separates structural discovery from vocabulary construction. LCT uses Minimum Description Length, entropy-based boundary signals, and morphotactic constraints to identify reu

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#284 most recent of 660 cs.CL papers we have recorded · ↑ newer: SpaceCast-Bench: Evaluating Predictive Spatial Reasoning in Vision-Lan · ↓ older: Which Skill to Distill? SGUID: Selecting a Compact Skill Bank for Mode
Cite this page: Latent Core Tokenizer: Compress, but Meaningfully: the #284 most recent of 660 cs.CL papers we have recorded (as of 2026-10-08). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/latent-core-tokenizer-compress-but-meaningfully.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
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