@inproceedings{liu-etal-2026-eluna,
title = "Eluna: An Agentic {LLM} System for Automating Warehouse Operations with Reasoning and Task Execution",
author = {Liu, Ning and
Sreekar, P Aditya and
Kujanp{\"a}{\"a}, Kalle and
Zhu, Zhaoxuan and
Liu, Kaiwen and
Sun, Chuanneng and
Menendez, Jorge Marchena and
Bales, Matthew J and
Yang, Tianyu and
Alam, Shahnawaz and
Yu, Rose and
Liu, Baoyuan and
Klinkner, Kristina Lisa and
Malmasi, Shervin},
editor = "Atanasova, Pepa and
Haffari, Gholamreza and
Wang, Minghan",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing: Industry Track",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-industry.159/",
pages = "2705--2716",
ISBN = "979-8-89176-460-6",
abstract = "Warehouse operations are governed by Standard Operating Procedures (SOPs) that encode complex, multi-system decision logic, which must be executed reliably under strict time constraints, yet LLM agents lack mechanisms to enforce procedural compliance and degrade under the context overload full SOP specifications introduce. We present Eluna, a production-deployed agentic system for reliable SOP execution. Eluna is a graph-guided, multi-agent framework that encodes SOPs as directed acyclic graphs with progressive disclosure and delegates independent tasks to parallel sub-agents, each with persistent code execution and live data access. To meet production latency and accuracy needs, we use asymmetric episodic distillation where a strong teacher is improved through episodic error memories, then a smaller student is fine-tuned on the corrected trajectories with memory stripped, internalizing corrections without inference-time overhead. On a 13-task benchmark and two production applications, our fine-tuned models match or exceed their teacher, beat all larger off-the-shelf baselines, and reach 94{\%} expert agreement on the ticket processing application."
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<abstract>Warehouse operations are governed by Standard Operating Procedures (SOPs) that encode complex, multi-system decision logic, which must be executed reliably under strict time constraints, yet LLM agents lack mechanisms to enforce procedural compliance and degrade under the context overload full SOP specifications introduce. We present Eluna, a production-deployed agentic system for reliable SOP execution. Eluna is a graph-guided, multi-agent framework that encodes SOPs as directed acyclic graphs with progressive disclosure and delegates independent tasks to parallel sub-agents, each with persistent code execution and live data access. To meet production latency and accuracy needs, we use asymmetric episodic distillation where a strong teacher is improved through episodic error memories, then a smaller student is fine-tuned on the corrected trajectories with memory stripped, internalizing corrections without inference-time overhead. On a 13-task benchmark and two production applications, our fine-tuned models match or exceed their teacher, beat all larger off-the-shelf baselines, and reach 94% expert agreement on the ticket processing application.</abstract>
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%0 Conference Proceedings
%T Eluna: An Agentic LLM System for Automating Warehouse Operations with Reasoning and Task Execution
%A Liu, Ning
%A Sreekar, P. Aditya
%A Kujanpää, Kalle
%A Zhu, Zhaoxuan
%A Liu, Kaiwen
%A Sun, Chuanneng
%A Menendez, Jorge Marchena
%A Bales, Matthew J.
%A Yang, Tianyu
%A Alam, Shahnawaz
%A Yu, Rose
%A Liu, Baoyuan
%A Klinkner, Kristina Lisa
%A Malmasi, Shervin
%Y Atanasova, Pepa
%Y Haffari, Gholamreza
%Y Wang, Minghan
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing: Industry Track
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-460-6
%F liu-etal-2026-eluna
%X Warehouse operations are governed by Standard Operating Procedures (SOPs) that encode complex, multi-system decision logic, which must be executed reliably under strict time constraints, yet LLM agents lack mechanisms to enforce procedural compliance and degrade under the context overload full SOP specifications introduce. We present Eluna, a production-deployed agentic system for reliable SOP execution. Eluna is a graph-guided, multi-agent framework that encodes SOPs as directed acyclic graphs with progressive disclosure and delegates independent tasks to parallel sub-agents, each with persistent code execution and live data access. To meet production latency and accuracy needs, we use asymmetric episodic distillation where a strong teacher is improved through episodic error memories, then a smaller student is fine-tuned on the corrected trajectories with memory stripped, internalizing corrections without inference-time overhead. On a 13-task benchmark and two production applications, our fine-tuned models match or exceed their teacher, beat all larger off-the-shelf baselines, and reach 94% expert agreement on the ticket processing application.
%U https://aclanthology.org/2026.emnlp-industry.159/
%P 2705-2716
Markdown (Informal)
[Eluna: An Agentic LLM System for Automating Warehouse Operations with Reasoning and Task Execution](https://aclanthology.org/2026.emnlp-industry.159/) (Liu et al., EMNLP 2026)
ACL
- Ning Liu, P Aditya Sreekar, Kalle Kujanpää, Zhaoxuan Zhu, Kaiwen Liu, Chuanneng Sun, Jorge Marchena Menendez, Matthew J Bales, Tianyu Yang, Shahnawaz Alam, Rose Yu, Baoyuan Liu, Kristina Lisa Klinkner, and Shervin Malmasi. 2026. Eluna: An Agentic LLM System for Automating Warehouse Operations with Reasoning and Task Execution. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing: Industry Track, pages 2705–2716, Budapest, Hungary. Association for Computational Linguistics.