@inproceedings{chen-etal-2026-folding,
title = "Folding Context into Executable States: Structured Strategy Folding for Long-Horizon Reasoning Agent",
author = "Chen, Zhengyu and
Zhang, Luan and
Li, Zhaoyi and
Yang, Jinluan and
Lv, Zheqi and
Xiao, Teng and
Wang, Wei and
Wang, Jingang",
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.1331/",
pages = "28192--28209",
ISBN = "979-8-89176-462-0",
abstract = "Current Large Language Model (LLM) agents struggle with long-horizon reasoning tasks, such as competition-level mathematics, where sustaining precision over hundreds of steps is critical. A primary bottleneck is the reliance on natural language context or summaries to track progress; these linguistic representations are inherently ``lossy,'' introducing semantic drift and accumulating errors that derail the reasoning chain. To address this, we propose Structured Strategy Folding (SSF), a novel paradigm that shifts the agent{'}s memory mechanism from linguistic description to computational verification. Instead of compressing history into text, SSF ``folds'' the reasoning trajectory into a sequence of Executable Symbolic States (ESS). Each ESS serves as a verifiable anchor{---}comprising either executed code artifacts or formally judged symbolic lemmas{---}generated through a dual-pathway validation mechanism. By interacting with these rigorous state artifacts rather than raw textual history, the agent effectively eliminates ambiguity and prevents error propagation. Experiments on challenging mathematical benchmarks demonstrate that SSF significantly outperforms standard Chain-of-Thought and existing summarization baselines, achieving substantial accuracy improvements while maintaining robust performance over extended reasoning horizons."
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<abstract>Current Large Language Model (LLM) agents struggle with long-horizon reasoning tasks, such as competition-level mathematics, where sustaining precision over hundreds of steps is critical. A primary bottleneck is the reliance on natural language context or summaries to track progress; these linguistic representations are inherently “lossy,” introducing semantic drift and accumulating errors that derail the reasoning chain. To address this, we propose Structured Strategy Folding (SSF), a novel paradigm that shifts the agent’s memory mechanism from linguistic description to computational verification. Instead of compressing history into text, SSF “folds” the reasoning trajectory into a sequence of Executable Symbolic States (ESS). Each ESS serves as a verifiable anchor—comprising either executed code artifacts or formally judged symbolic lemmas—generated through a dual-pathway validation mechanism. By interacting with these rigorous state artifacts rather than raw textual history, the agent effectively eliminates ambiguity and prevents error propagation. Experiments on challenging mathematical benchmarks demonstrate that SSF significantly outperforms standard Chain-of-Thought and existing summarization baselines, achieving substantial accuracy improvements while maintaining robust performance over extended reasoning horizons.</abstract>
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%0 Conference Proceedings
%T Folding Context into Executable States: Structured Strategy Folding for Long-Horizon Reasoning Agent
%A Chen, Zhengyu
%A Zhang, Luan
%A Li, Zhaoyi
%A Yang, Jinluan
%A Lv, Zheqi
%A Xiao, Teng
%A Wang, Wei
%A Wang, Jingang
%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 chen-etal-2026-folding
%X Current Large Language Model (LLM) agents struggle with long-horizon reasoning tasks, such as competition-level mathematics, where sustaining precision over hundreds of steps is critical. A primary bottleneck is the reliance on natural language context or summaries to track progress; these linguistic representations are inherently “lossy,” introducing semantic drift and accumulating errors that derail the reasoning chain. To address this, we propose Structured Strategy Folding (SSF), a novel paradigm that shifts the agent’s memory mechanism from linguistic description to computational verification. Instead of compressing history into text, SSF “folds” the reasoning trajectory into a sequence of Executable Symbolic States (ESS). Each ESS serves as a verifiable anchor—comprising either executed code artifacts or formally judged symbolic lemmas—generated through a dual-pathway validation mechanism. By interacting with these rigorous state artifacts rather than raw textual history, the agent effectively eliminates ambiguity and prevents error propagation. Experiments on challenging mathematical benchmarks demonstrate that SSF significantly outperforms standard Chain-of-Thought and existing summarization baselines, achieving substantial accuracy improvements while maintaining robust performance over extended reasoning horizons.
%U https://aclanthology.org/2026.findings-emnlp.1331/
%P 28192-28209
Markdown (Informal)
[Folding Context into Executable States: Structured Strategy Folding for Long-Horizon Reasoning Agent](https://aclanthology.org/2026.findings-emnlp.1331/) (Chen et al., Findings 2026)
ACL
- Zhengyu Chen, Luan Zhang, Zhaoyi Li, Jinluan Yang, Zheqi Lv, Teng Xiao, Wei Wang, and Jingang Wang. 2026. Folding Context into Executable States: Structured Strategy Folding for Long-Horizon Reasoning Agent. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 28192–28209, Budapest, Hungary. Association for Computational Linguistics.