@inproceedings{xie-etal-2026-escaping,
title = "Escaping the Sisyphus Dilemma: Experience Replay for Robust Text-to-Optimization Modeling",
author = "Xie, Wantong and
Chen, Yinghao and
Hu, Yi-Xiang and
Wu, Feng and
Xu, Jieyang and
Zhang, Sijia and
Li, Xiangyang",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-acl.690/",
pages = "14100--14116",
ISBN = "979-8-89176-395-1",
abstract = "Large Language Models have shown promise in translating natural language into executable optimization models, yet they often suffer from the Sisyphus Dilemma: a memoryless cycle where identical errors are repeated across structurally similar problems. Existing retrieval-augmented strategies primarily fetch static problem-model pairs as few-shot demonstrators, failing to capture the dynamic reasoning required to resolve execution failures. To bridge this gap, we propose \textbf{EOM}, a framework that implements Experience Replay to transform transient rectification steps into persistent knowledge. EOM distills interaction histories into Causal Correction Mappings, indexing both diagnostic insights and prohibitive traps. By utilizing a structure-aware retrieval mechanism that aligns semantic intent with abstract syntax trees and solver tracebacks, the system enables models to recall specific correction strategies for isomorphic errors. Extensive experiments across seven benchmarks demonstrate that EOM improves modeling accuracy by 8.45{\%} on complex tasks while reducing token consumption by 28.65{\%} and interaction turns by 25.82{\%}, validating the efficiency of a ``Rectify Once, Solve Many'' paradigm."
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<abstract>Large Language Models have shown promise in translating natural language into executable optimization models, yet they often suffer from the Sisyphus Dilemma: a memoryless cycle where identical errors are repeated across structurally similar problems. Existing retrieval-augmented strategies primarily fetch static problem-model pairs as few-shot demonstrators, failing to capture the dynamic reasoning required to resolve execution failures. To bridge this gap, we propose EOM, a framework that implements Experience Replay to transform transient rectification steps into persistent knowledge. EOM distills interaction histories into Causal Correction Mappings, indexing both diagnostic insights and prohibitive traps. By utilizing a structure-aware retrieval mechanism that aligns semantic intent with abstract syntax trees and solver tracebacks, the system enables models to recall specific correction strategies for isomorphic errors. Extensive experiments across seven benchmarks demonstrate that EOM improves modeling accuracy by 8.45% on complex tasks while reducing token consumption by 28.65% and interaction turns by 25.82%, validating the efficiency of a “Rectify Once, Solve Many” paradigm.</abstract>
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%0 Conference Proceedings
%T Escaping the Sisyphus Dilemma: Experience Replay for Robust Text-to-Optimization Modeling
%A Xie, Wantong
%A Chen, Yinghao
%A Hu, Yi-Xiang
%A Wu, Feng
%A Xu, Jieyang
%A Zhang, Sijia
%A Li, Xiangyang
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F xie-etal-2026-escaping
%X Large Language Models have shown promise in translating natural language into executable optimization models, yet they often suffer from the Sisyphus Dilemma: a memoryless cycle where identical errors are repeated across structurally similar problems. Existing retrieval-augmented strategies primarily fetch static problem-model pairs as few-shot demonstrators, failing to capture the dynamic reasoning required to resolve execution failures. To bridge this gap, we propose EOM, a framework that implements Experience Replay to transform transient rectification steps into persistent knowledge. EOM distills interaction histories into Causal Correction Mappings, indexing both diagnostic insights and prohibitive traps. By utilizing a structure-aware retrieval mechanism that aligns semantic intent with abstract syntax trees and solver tracebacks, the system enables models to recall specific correction strategies for isomorphic errors. Extensive experiments across seven benchmarks demonstrate that EOM improves modeling accuracy by 8.45% on complex tasks while reducing token consumption by 28.65% and interaction turns by 25.82%, validating the efficiency of a “Rectify Once, Solve Many” paradigm.
%U https://aclanthology.org/2026.findings-acl.690/
%P 14100-14116
Markdown (Informal)
[Escaping the Sisyphus Dilemma: Experience Replay for Robust Text-to-Optimization Modeling](https://aclanthology.org/2026.findings-acl.690/) (Xie et al., Findings 2026)
ACL
- Wantong Xie, Yinghao Chen, Yi-Xiang Hu, Feng Wu, Jieyang Xu, Sijia Zhang, and Xiangyang Li. 2026. Escaping the Sisyphus Dilemma: Experience Replay for Robust Text-to-Optimization Modeling. In Findings of the Association for Computational Linguistics: ACL 2026, pages 14100–14116, San Diego, California, United States. Association for Computational Linguistics.