@inproceedings{shao-etal-2026-list,
title = "{L}i{ST}: Local-Simplex Test-Time {L}o{RA} Fusion",
author = "Shao, Yihua and
Li, Jia and
Chen, Siyu and
Luo, Xinyu and
Liu, Yang and
Chen, Kecheng and
Long, Xinwei and
Zhu, Lingyu and
Zeng, Fanhu and
Wang, Maolin and
Yan, Ziyang and
Guo, Jingcai and
Tang, Hao and
Sebe, Nicu and
Wang, Zhenyi",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.906/",
pages = "19193--19205",
ISBN = "979-8-89176-462-0",
abstract = "Task-specific LoRA adapters offer a modular way to specialize large language and vision-language models. However, existing adapter composition methods are mostly static and cannot adapt to individual test inputs. To address these issues, we propose LiST, a label-free test-time LoRA fusion framework that converts an existing LoRA bank into a target-conditioned local simplex and searches sample-specific fusion weights at inference time. LiST builds joint task representations from LoRA parameter anchors and prompt-level behavior vectors, retrieves neighboring adapters as a local search space, and performs branch-preserving fusion without updating the backbone or adapters. Candidate weights are selected by a prompt-level energy with prior, geometric, and stochastic-consistency constraints, and are deployed only when they pass a safe acceptance rule. Otherwise, LiST falls back to a target-conditioned prior. Experiments on multimodal and language benchmarks show that LiST outperforms static LoRA merging and conventional test-time adaptation baselines, while preserving task-specific adapter utility and improving robustness on unseen tasks."
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<abstract>Task-specific LoRA adapters offer a modular way to specialize large language and vision-language models. However, existing adapter composition methods are mostly static and cannot adapt to individual test inputs. To address these issues, we propose LiST, a label-free test-time LoRA fusion framework that converts an existing LoRA bank into a target-conditioned local simplex and searches sample-specific fusion weights at inference time. LiST builds joint task representations from LoRA parameter anchors and prompt-level behavior vectors, retrieves neighboring adapters as a local search space, and performs branch-preserving fusion without updating the backbone or adapters. Candidate weights are selected by a prompt-level energy with prior, geometric, and stochastic-consistency constraints, and are deployed only when they pass a safe acceptance rule. Otherwise, LiST falls back to a target-conditioned prior. Experiments on multimodal and language benchmarks show that LiST outperforms static LoRA merging and conventional test-time adaptation baselines, while preserving task-specific adapter utility and improving robustness on unseen tasks.</abstract>
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%0 Conference Proceedings
%T LiST: Local-Simplex Test-Time LoRA Fusion
%A Shao, Yihua
%A Li, Jia
%A Chen, Siyu
%A Luo, Xinyu
%A Liu, Yang
%A Chen, Kecheng
%A Long, Xinwei
%A Zhu, Lingyu
%A Zeng, Fanhu
%A Wang, Maolin
%A Yan, Ziyang
%A Guo, Jingcai
%A Tang, Hao
%A Sebe, Nicu
%A Wang, Zhenyi
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F shao-etal-2026-list
%X Task-specific LoRA adapters offer a modular way to specialize large language and vision-language models. However, existing adapter composition methods are mostly static and cannot adapt to individual test inputs. To address these issues, we propose LiST, a label-free test-time LoRA fusion framework that converts an existing LoRA bank into a target-conditioned local simplex and searches sample-specific fusion weights at inference time. LiST builds joint task representations from LoRA parameter anchors and prompt-level behavior vectors, retrieves neighboring adapters as a local search space, and performs branch-preserving fusion without updating the backbone or adapters. Candidate weights are selected by a prompt-level energy with prior, geometric, and stochastic-consistency constraints, and are deployed only when they pass a safe acceptance rule. Otherwise, LiST falls back to a target-conditioned prior. Experiments on multimodal and language benchmarks show that LiST outperforms static LoRA merging and conventional test-time adaptation baselines, while preserving task-specific adapter utility and improving robustness on unseen tasks.
%U https://aclanthology.org/2026.findings-emnlp.906/
%P 19193-19205
Markdown (Informal)
[LiST: Local-Simplex Test-Time LoRA Fusion](https://aclanthology.org/2026.findings-emnlp.906/) (Shao et al., Findings 2026)
ACL
- Yihua Shao, Jia Li, Siyu Chen, Xinyu Luo, Yang Liu, Kecheng Chen, Xinwei Long, Lingyu Zhu, Fanhu Zeng, Maolin Wang, Ziyang Yan, Jingcai Guo, Hao Tang, Nicu Sebe, and Zhenyi Wang. 2026. LiST: Local-Simplex Test-Time LoRA Fusion. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 19193–19205, Budapest, Hungary. Association for Computational Linguistics.