@inproceedings{wang-etal-2026-cognitively,
title = "Cognitively Decoupled Meta-Induction for Out-of-Distribution Generalization",
author = "Wang, Yuqian and
Wang, Yuanzheng and
Xiao, Chaojun and
Xu, Ke and
He, Bingxiang and
Tan, Jinghan and
Chen, Zijun and
Chen, Lu and
Huo, Yi-Xin and
Sun, Maosong",
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.899/",
pages = "20694--20714",
ISBN = "979-8-89176-459-0",
abstract = "In-context learning (ICL) tackles downstream tasks conditioned on demonstrations. This paradigm relies heavily on neighbor-based matching between queries and demonstrations, limiting out-of-distribution (OOD) performance. For better generalization, rule-based inductive reasoning derives task-level regularities, which we term task-level inductive representations (TIR), before directly answering. However, existing methods lack generalizable inductive ability across diverse tasks. Inspired by meta-learning, we propose Cognitive-Decoupling meta-Induction (CoDI) for TIR-based inductive reasoning on general tasks. CoDI comprises a Cognitive-Decoupling Framework for query-blind TIR induction, and a Dual-Loop Meta-Inductive Learning method for cross-task inducer optimization. Empirically, CoDI achieves substantial performance gains on five ID and OOD benchmarks, and preserves OOD gains without explicit induction at inference. Code is available at https://github.com/Yuqian-Wang2277/codi-meta-induction."
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<abstract>In-context learning (ICL) tackles downstream tasks conditioned on demonstrations. This paradigm relies heavily on neighbor-based matching between queries and demonstrations, limiting out-of-distribution (OOD) performance. For better generalization, rule-based inductive reasoning derives task-level regularities, which we term task-level inductive representations (TIR), before directly answering. However, existing methods lack generalizable inductive ability across diverse tasks. Inspired by meta-learning, we propose Cognitive-Decoupling meta-Induction (CoDI) for TIR-based inductive reasoning on general tasks. CoDI comprises a Cognitive-Decoupling Framework for query-blind TIR induction, and a Dual-Loop Meta-Inductive Learning method for cross-task inducer optimization. Empirically, CoDI achieves substantial performance gains on five ID and OOD benchmarks, and preserves OOD gains without explicit induction at inference. Code is available at https://github.com/Yuqian-Wang2277/codi-meta-induction.</abstract>
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%0 Conference Proceedings
%T Cognitively Decoupled Meta-Induction for Out-of-Distribution Generalization
%A Wang, Yuqian
%A Wang, Yuanzheng
%A Xiao, Chaojun
%A Xu, Ke
%A He, Bingxiang
%A Tan, Jinghan
%A Chen, Zijun
%A Chen, Lu
%A Huo, Yi-Xin
%A Sun, Maosong
%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 wang-etal-2026-cognitively
%X In-context learning (ICL) tackles downstream tasks conditioned on demonstrations. This paradigm relies heavily on neighbor-based matching between queries and demonstrations, limiting out-of-distribution (OOD) performance. For better generalization, rule-based inductive reasoning derives task-level regularities, which we term task-level inductive representations (TIR), before directly answering. However, existing methods lack generalizable inductive ability across diverse tasks. Inspired by meta-learning, we propose Cognitive-Decoupling meta-Induction (CoDI) for TIR-based inductive reasoning on general tasks. CoDI comprises a Cognitive-Decoupling Framework for query-blind TIR induction, and a Dual-Loop Meta-Inductive Learning method for cross-task inducer optimization. Empirically, CoDI achieves substantial performance gains on five ID and OOD benchmarks, and preserves OOD gains without explicit induction at inference. Code is available at https://github.com/Yuqian-Wang2277/codi-meta-induction.
%U https://aclanthology.org/2026.emnlp-main.899/
%P 20694-20714
Markdown (Informal)
[Cognitively Decoupled Meta-Induction for Out-of-Distribution Generalization](https://aclanthology.org/2026.emnlp-main.899/) (Wang et al., EMNLP 2026)
ACL
- Yuqian Wang, Yuanzheng Wang, Chaojun Xiao, Ke Xu, Bingxiang He, Jinghan Tan, Zijun Chen, Lu Chen, Yi-Xin Huo, and Maosong Sun. 2026. Cognitively Decoupled Meta-Induction for Out-of-Distribution Generalization. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 20694–20714, Budapest, Hungary. Association for Computational Linguistics.