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[2602.18301] On the Semantic and Syntactic Information Encoded in Proto-Tokens for One-Step Text Reconstruction
Summary
This paper explores the semantic and syntactic information encoded in proto-tokens for one-step text reconstruction, challenging traditional autoregressive models in LLMs.
Why It Matters
Understanding how proto-tokens can encapsulate semantic and syntactic information is crucial for advancing non-autoregressive text generation methods. This research could lead to more efficient language models, enhancing performance in various NLP applications.
Key Takeaways
- Proto-tokens can reconstruct multiple tokens in a single forward pass, moving beyond autoregressive methods.
- The m-token captures semantic information more effectively than the e-token.
- Regularization techniques can impose semantic structure on proto-tokens without compromising reconstruction quality.
- Attention patterns during reconstruction provide insights into the behavior of proto-tokens.
- Future seq2seq systems may leverage proto-tokens as intermediate representations for improved efficiency.
Computer Science > Machine Learning arXiv:2602.18301 (cs) [Submitted on 20 Feb 2026] Title:On the Semantic and Syntactic Information Encoded in Proto-Tokens for One-Step Text Reconstruction Authors:Ivan Bondarenko, Egor Palkin, Fedor Tikunov View a PDF of the paper titled On the Semantic and Syntactic Information Encoded in Proto-Tokens for One-Step Text Reconstruction, by Ivan Bondarenko and 2 other authors View PDF HTML (experimental) 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 in...
