@inproceedings{zhu-etal-2026-unified,
title = "Unified Context Evolution for {LLM} Agents",
author = "Zhu, Zixuan and
Hu, Yitong and
Dai, Yong and
Fang, Junfeng and
Jiang, Chunyang and
Hu, Senkang and
Zhao, Yuzhi",
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.2188/",
pages = "49735--49763",
ISBN = "979-8-89176-459-0",
abstract = "LLM-based agents can solve multi-step interactive tasks by combining reasoning with environment feedback, yet each episode starts from the same fixed context and any useful strategy discovered along the way is lost once the task ends. Existing approaches either limit learning to the current task or pool all experience into a single untyped store, without distinguishing knowledge types, tracking quality through use, or balancing what the library still lacks. We introduce Unified Context Evolution (UCE), a gradient-free framework that externalizes agent experience into an evolving library of typed Evolvable Context Units (ECUs). UCE decomposes experience into four complementary types (Memory, Strategy, Workflow, and Skill), each generated from trajectories under type-specific conditions, retrieved at decision time, scored through repeated usage outcomes, and pruned when no longer valuable. A scheduling module allocates each cycle{'}s generation budget toward the types where the library is weakest. Across two interactive benchmarks, UCE raises ALFWorld success from 75.4{\%} to 96.3{\%} and WebShop task score from 45.1{\%} to 61.3{\%}, and remains effective across alternative actor reasoning backbones without model retraining."
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<abstract>LLM-based agents can solve multi-step interactive tasks by combining reasoning with environment feedback, yet each episode starts from the same fixed context and any useful strategy discovered along the way is lost once the task ends. Existing approaches either limit learning to the current task or pool all experience into a single untyped store, without distinguishing knowledge types, tracking quality through use, or balancing what the library still lacks. We introduce Unified Context Evolution (UCE), a gradient-free framework that externalizes agent experience into an evolving library of typed Evolvable Context Units (ECUs). UCE decomposes experience into four complementary types (Memory, Strategy, Workflow, and Skill), each generated from trajectories under type-specific conditions, retrieved at decision time, scored through repeated usage outcomes, and pruned when no longer valuable. A scheduling module allocates each cycle’s generation budget toward the types where the library is weakest. Across two interactive benchmarks, UCE raises ALFWorld success from 75.4% to 96.3% and WebShop task score from 45.1% to 61.3%, and remains effective across alternative actor reasoning backbones without model retraining.</abstract>
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%0 Conference Proceedings
%T Unified Context Evolution for LLM Agents
%A Zhu, Zixuan
%A Hu, Yitong
%A Dai, Yong
%A Fang, Junfeng
%A Jiang, Chunyang
%A Hu, Senkang
%A Zhao, Yuzhi
%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 zhu-etal-2026-unified
%X LLM-based agents can solve multi-step interactive tasks by combining reasoning with environment feedback, yet each episode starts from the same fixed context and any useful strategy discovered along the way is lost once the task ends. Existing approaches either limit learning to the current task or pool all experience into a single untyped store, without distinguishing knowledge types, tracking quality through use, or balancing what the library still lacks. We introduce Unified Context Evolution (UCE), a gradient-free framework that externalizes agent experience into an evolving library of typed Evolvable Context Units (ECUs). UCE decomposes experience into four complementary types (Memory, Strategy, Workflow, and Skill), each generated from trajectories under type-specific conditions, retrieved at decision time, scored through repeated usage outcomes, and pruned when no longer valuable. A scheduling module allocates each cycle’s generation budget toward the types where the library is weakest. Across two interactive benchmarks, UCE raises ALFWorld success from 75.4% to 96.3% and WebShop task score from 45.1% to 61.3%, and remains effective across alternative actor reasoning backbones without model retraining.
%U https://aclanthology.org/2026.emnlp-main.2188/
%P 49735-49763
Markdown (Informal)
[Unified Context Evolution for LLM Agents](https://aclanthology.org/2026.emnlp-main.2188/) (Zhu et al., EMNLP 2026)
ACL
- Zixuan Zhu, Yitong Hu, Yong Dai, Junfeng Fang, Chunyang Jiang, Senkang Hu, and Yuzhi Zhao. 2026. Unified Context Evolution for LLM Agents. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 49735–49763, Budapest, Hungary. Association for Computational Linguistics.