@inproceedings{ji-etal-2026-sala,
title = "{SALA}: Semantic-Aware Logical Alignment for Complex Reasoning in In-Context Learning",
author = "Ji, Zhao and
Chen, Wenqing and
Chu, Zhixuan and
Yu, Jianxing and
Liu, Jingping and
Zhao, Shanhe and
Zheng, Zibin",
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.1015/",
pages = "21440--21456",
ISBN = "979-8-89176-462-0",
abstract = "Effective in-context learning (ICL) for complex reasoning relies on selecting the right demonstrations. Traditional retrieval methods based on surface similarity fail to capture the underlying problem-solving logic. Recent logic-based methods address this by matching predefined reasoning steps, but the rigid rules and exact-match criteria is improper to handle flexible or diverse reasoning processes. To address the problem, we propose \textbf{SALA}, a \textbf{S}emantic-\textbf{A}ware \textbf{L}ogical \textbf{A}lignment framework. Instead of relying on a fixed inventory, SALA automatically learns task-specific reasoning operations. It then embeds these operations into a continuous semantic space and uses dynamic time warping (DTW) to align the reasoning sequences. This approach allows for soft, flexible matching of reasoning logic while remaining highly interpretable. Experiments across four reasoning benchmarks and three LLMs demonstrate that SALA outperforms existing demonstration selection methods. Further analysis confirming the roles of the operation induction and the logical semantic alignment."
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<abstract>Effective in-context learning (ICL) for complex reasoning relies on selecting the right demonstrations. Traditional retrieval methods based on surface similarity fail to capture the underlying problem-solving logic. Recent logic-based methods address this by matching predefined reasoning steps, but the rigid rules and exact-match criteria is improper to handle flexible or diverse reasoning processes. To address the problem, we propose SALA, a Semantic-Aware Logical Alignment framework. Instead of relying on a fixed inventory, SALA automatically learns task-specific reasoning operations. It then embeds these operations into a continuous semantic space and uses dynamic time warping (DTW) to align the reasoning sequences. This approach allows for soft, flexible matching of reasoning logic while remaining highly interpretable. Experiments across four reasoning benchmarks and three LLMs demonstrate that SALA outperforms existing demonstration selection methods. Further analysis confirming the roles of the operation induction and the logical semantic alignment.</abstract>
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%0 Conference Proceedings
%T SALA: Semantic-Aware Logical Alignment for Complex Reasoning in In-Context Learning
%A Ji, Zhao
%A Chen, Wenqing
%A Chu, Zhixuan
%A Yu, Jianxing
%A Liu, Jingping
%A Zhao, Shanhe
%A Zheng, Zibin
%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 ji-etal-2026-sala
%X Effective in-context learning (ICL) for complex reasoning relies on selecting the right demonstrations. Traditional retrieval methods based on surface similarity fail to capture the underlying problem-solving logic. Recent logic-based methods address this by matching predefined reasoning steps, but the rigid rules and exact-match criteria is improper to handle flexible or diverse reasoning processes. To address the problem, we propose SALA, a Semantic-Aware Logical Alignment framework. Instead of relying on a fixed inventory, SALA automatically learns task-specific reasoning operations. It then embeds these operations into a continuous semantic space and uses dynamic time warping (DTW) to align the reasoning sequences. This approach allows for soft, flexible matching of reasoning logic while remaining highly interpretable. Experiments across four reasoning benchmarks and three LLMs demonstrate that SALA outperforms existing demonstration selection methods. Further analysis confirming the roles of the operation induction and the logical semantic alignment.
%U https://aclanthology.org/2026.findings-emnlp.1015/
%P 21440-21456
Markdown (Informal)
[SALA: Semantic-Aware Logical Alignment for Complex Reasoning in In-Context Learning](https://aclanthology.org/2026.findings-emnlp.1015/) (Ji et al., Findings 2026)
ACL