On the Semantic and Syntactic Information Encoded in Proto-Tokens for One-Step Text Reconstruction

• URL: http://arxiv.org/abs/2602.18301v1
• Date: Fri, 20 Feb 2026 15:54:10 GMT
• Title: On the Semantic and Syntactic Information Encoded in Proto-Tokens for One-Step Text Reconstruction
• Authors: Ivan Bondarenko, Egor Palkin, Fedor Tikunov,
• Abstract summary: Autoregressive large language models (LLMs) generate text token-by-token, requiring n forward passes to produce a sequence of length n.<n>Recent work shows that frozen LLMs can reconstruct hundreds of tokens from only two learned proto-tokens in a single forward pass.<n>We study what information these proto-tokens encode and how they behave under reconstruction and controlled constraints.
• Score: 0.5097809301149341
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Autoregressive large language models (LLMs) generate text token-by-token, requiring n forward passes to produce a sequence of length n. Recent work, Exploring the Latent Capacity of LLMs for One-Step Text Reconstruction (Mezentsev and Oseledets), shows that frozen LLMs can reconstruct hundreds of tokens from only two learned proto-tokens in a single forward pass, suggesting a path beyond the autoregressive paradigm. In this paper, we study what information these proto-tokens encode and how they behave under reconstruction and controlled constraints. We perform a series of experiments aimed at disentangling semantic and syntactic content in the two proto-tokens, analyzing stability properties of the e-token, and visualizing attention patterns to the e-token during reconstruction. Finally, we test two regularization schemes for "imposing" semantic structure on the e-token using teacher embeddings, including an anchor-based loss and a relational distillation objective. Our results indicate that the m-token tends to capture semantic information more strongly than the e-token under standard optimization; anchor-based constraints trade off sharply with reconstruction accuracy; and relational distillation can transfer batch-level semantic relations into the proto-token space without sacrificing reconstruction quality, supporting the feasibility of future non-autoregressive seq2seq systems that predict proto-tokens as an intermediate representation.

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