@inproceedings{han-etal-2026-w2t,
title = "{W}2{T}: {L}o{RA} Weights Already Know What They Can Do",
author = "Han, Xiaolong and
Neri, Ferrante and
Zijian, Jiang and
Wu, Fang and
Ye, Yanfang and
Yin, Lu and
Wang, Zehong",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.158/",
pages = "3638--3653",
ISBN = "979-8-89176-459-0",
abstract = "Each LoRA checkpoint compactly stores task-specific updates in low-rank weight matrices, offering an efficient way to adapt large language models to new tasks and domains. In principle, these weights already encode what the adapter does and how well it performs. In this paper, we ask whether this information can be read directly from the weights, without running the base model or accessing training data. A key obstacle is that a single LoRA update can be factorized in infinitely many ways. Without resolving this ambiguity, models trained on the factors may fit the particular factorization rather than the underlying update. To this end, we propose W2T (Weight-to-Token), which maps each LoRA update to a provably canonical form via QR decomposition followed by SVD, so that all equivalent factorizations share the same representation. The resulting components are then tokenized and processed by a Transformer to produce a weight-space embedding. Across language and vision LoRA collections, W2T achieves strong results on attribute classification, performance prediction, and adapter retrieval, demonstrating that LoRA weights reliably indicate model behavior once factorization ambiguity is removed."
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<abstract>Each LoRA checkpoint compactly stores task-specific updates in low-rank weight matrices, offering an efficient way to adapt large language models to new tasks and domains. In principle, these weights already encode what the adapter does and how well it performs. In this paper, we ask whether this information can be read directly from the weights, without running the base model or accessing training data. A key obstacle is that a single LoRA update can be factorized in infinitely many ways. Without resolving this ambiguity, models trained on the factors may fit the particular factorization rather than the underlying update. To this end, we propose W2T (Weight-to-Token), which maps each LoRA update to a provably canonical form via QR decomposition followed by SVD, so that all equivalent factorizations share the same representation. The resulting components are then tokenized and processed by a Transformer to produce a weight-space embedding. Across language and vision LoRA collections, W2T achieves strong results on attribute classification, performance prediction, and adapter retrieval, demonstrating that LoRA weights reliably indicate model behavior once factorization ambiguity is removed.</abstract>
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%0 Conference Proceedings
%T W2T: LoRA Weights Already Know What They Can Do
%A Han, Xiaolong
%A Neri, Ferrante
%A Zijian, Jiang
%A Wu, Fang
%A Ye, Yanfang
%A Yin, Lu
%A Wang, Zehong
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F han-etal-2026-w2t
%X Each LoRA checkpoint compactly stores task-specific updates in low-rank weight matrices, offering an efficient way to adapt large language models to new tasks and domains. In principle, these weights already encode what the adapter does and how well it performs. In this paper, we ask whether this information can be read directly from the weights, without running the base model or accessing training data. A key obstacle is that a single LoRA update can be factorized in infinitely many ways. Without resolving this ambiguity, models trained on the factors may fit the particular factorization rather than the underlying update. To this end, we propose W2T (Weight-to-Token), which maps each LoRA update to a provably canonical form via QR decomposition followed by SVD, so that all equivalent factorizations share the same representation. The resulting components are then tokenized and processed by a Transformer to produce a weight-space embedding. Across language and vision LoRA collections, W2T achieves strong results on attribute classification, performance prediction, and adapter retrieval, demonstrating that LoRA weights reliably indicate model behavior once factorization ambiguity is removed.
%U https://aclanthology.org/2026.emnlp-main.158/
%P 3638-3653
Markdown (Informal)
[W2T: LoRA Weights Already Know What They Can Do](https://aclanthology.org/2026.emnlp-main.158/) (Han et al., EMNLP 2026)
ACL
- Xiaolong Han, Ferrante Neri, Jiang Zijian, Fang Wu, Yanfang Ye, Lu Yin, and Zehong Wang. 2026. W2T: LoRA Weights Already Know What They Can Do. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 3638–3653, Budapest, Hungary. Association for Computational Linguistics.