Ziqiang Li | Searcharxiv

arXiv2026

.tmu: A Low-Entropy Tree-Structured Representation for LLM-Assisted Scientific Writing

As large language models (LLMs) increasingly assist scientific writing, the limitations and token costs of generating TeX become increasingly visible. 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. As an alternative, we introduce .tmu, a low-entropy tree-structured representation. With its efficient data structure and clear contextual boundaries, .tmu outperforms .tex in the above aspects. Experiments across four LLMs provide evidence for this claim in most evaluated settings. Furthermore, we show that due to its lower information entropy, fine-tuning LLMs on .tmu achieves approximately 43% lower final training loss than on .tex. Our work provides a more scalable and LLM-friendly data representation for LLM-assisted scientific writing.

cs.CL↗

arXiv2026

Robust Coverless Linguistic Steganography via Sentence Embedding Space with Global Resynchronization

Linguistic steganography enables covert communication through natural language. Existing methods heavily rely on token-level operations and struggle to maintain reliability under word- and sentence-level textual perturbations. Moreover, variable-length coding-based schemes are highly susceptible to bit-slippage under minor disturbances, as perturbations cause desynchronization between embedded and extracted bit sequences. To address these issues, we propose a robust coverless steganographic framework that operates in the sentence embedding space rather than the token space. Specifically, secret messages are encoded as hierarchical clustering paths in the sentence embedding space, which enhances decoding stability against word- and sentence-level textual perturbations. To tackle the bit-slippage problem, we introduce a Global Resynchronization Mechanism (GRM) that reframes variable-length bitstreams as discrete symbols anchored to semantic subspaces, decoupling local embedding failures from global message recovery. Experimental results demonstrate that under word- and sentence-level perturbations, our approach achieves substantial improvements in robustness, while maintaining effective embedding capacity and exhibiting strong resistance to statistical analysis.

cs.CR↗