@inproceedings{yang-etal-2026-batch,
title = "Batch-of-Thought: Cross-Instance Learning for Enhanced {LLM} Reasoning",
author = "Yang, Xuan and
Jia, Furong and
Xie, Roy and
Xiong, Xi and
Bian, Hengwei and
LI, Jian and
Agrawal, Monica",
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.810/",
pages = "18873--18892",
ISBN = "979-8-89176-459-0",
abstract = "Current Large Language Model reasoning systems process queries independently, discarding valuable cross-instance signals such as shared reasoning patterns and consistency constraints. We introduce Batch-of-Thought (BoT), a training-free method that processes related queries jointly to enable cross-instance learning. By performing comparative analysis across queries, BoT identifies high-quality reasoning templates, detects errors through consistency checks, and amortizes computational costs. We instantiate BoT within a multi-agent reflection architecture (BoT-R), where a Reflector performs joint evaluation to unlock mutual information gain unavailable in isolated processing. Experiments across three model families and six benchmarks demonstrate that BoT-R consistently improves accuracy and confidence calibration while reducing inference costs by up to 61{\%}. Our theoretical and experimental analysis reveals when and why batch-aware reasoning benefits LLM systems."
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%0 Conference Proceedings
%T Batch-of-Thought: Cross-Instance Learning for Enhanced LLM Reasoning
%A Yang, Xuan
%A Jia, Furong
%A Xie, Roy
%A Xiong, Xi
%A Bian, Hengwei
%A LI, Jian
%A Agrawal, Monica
%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 yang-etal-2026-batch
%X Current Large Language Model reasoning systems process queries independently, discarding valuable cross-instance signals such as shared reasoning patterns and consistency constraints. We introduce Batch-of-Thought (BoT), a training-free method that processes related queries jointly to enable cross-instance learning. By performing comparative analysis across queries, BoT identifies high-quality reasoning templates, detects errors through consistency checks, and amortizes computational costs. We instantiate BoT within a multi-agent reflection architecture (BoT-R), where a Reflector performs joint evaluation to unlock mutual information gain unavailable in isolated processing. Experiments across three model families and six benchmarks demonstrate that BoT-R consistently improves accuracy and confidence calibration while reducing inference costs by up to 61%. Our theoretical and experimental analysis reveals when and why batch-aware reasoning benefits LLM systems.
%U https://aclanthology.org/2026.emnlp-main.810/
%P 18873-18892
Markdown (Informal)
[Batch-of-Thought: Cross-Instance Learning for Enhanced LLM Reasoning](https://aclanthology.org/2026.emnlp-main.810/) (Yang et al., EMNLP 2026)
ACL