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            <namePart type="given">Jiajun</namePart>
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    <abstract>Multimodal GUI agents generally operate on raw visual and textual observations, which creates a fundamental scalability challenge. While current state-of-the-art frameworks predominantly rely on inference-intensive test-time scaling or the accumulation of unbounded raw logs to maintain task coherence, we attribute the underlying bottleneck to insufficient state abstraction.To address this, we propose HiSA, a hierarchical state abstraction approach that actively restructures knowledge rather than passively retaining historical information by organizing raw histories into a three-level hierarchy of abstracted steps, refined contexts, and induced patterns.By synthesizing high-dimensional observations into compact semantic states, HiSA decouples reasoning efficacy from context length, enabling precise and scalable decision-making as interaction histories grow.When evaluating using Spider2-V, our approach establishes a new state-of-the-art, achieving a 40.58% success rate while reducing token consumption by 69.85% and monetary costs by 55.10% compared to the best-performing baseline.</abstract>
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%0 Conference Proceedings
%T HiSA: Hierarchical State Abstraction for Scalable GUI Agents
%A Li, Weiming
%A Paik, Hye-young
%A Sui, Yulei
%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 li-etal-2026-hisa
%X Multimodal GUI agents generally operate on raw visual and textual observations, which creates a fundamental scalability challenge. While current state-of-the-art frameworks predominantly rely on inference-intensive test-time scaling or the accumulation of unbounded raw logs to maintain task coherence, we attribute the underlying bottleneck to insufficient state abstraction.To address this, we propose HiSA, a hierarchical state abstraction approach that actively restructures knowledge rather than passively retaining historical information by organizing raw histories into a three-level hierarchy of abstracted steps, refined contexts, and induced patterns.By synthesizing high-dimensional observations into compact semantic states, HiSA decouples reasoning efficacy from context length, enabling precise and scalable decision-making as interaction histories grow.When evaluating using Spider2-V, our approach establishes a new state-of-the-art, achieving a 40.58% success rate while reducing token consumption by 69.85% and monetary costs by 55.10% compared to the best-performing baseline.
%U https://aclanthology.org/2026.findings-acl.581/
%P 11965-11985

Markdown (Informal)

[HiSA: Hierarchical State Abstraction for Scalable GUI Agents](https://aclanthology.org/2026.findings-acl.581/) (Li et al., Findings 2026)

• HiSA: Hierarchical State Abstraction for Scalable GUI Agents (Li et al., Findings 2026)

ACL

• Weiming Li, Hye-young Paik, and Yulei Sui. 2026. HiSA: Hierarchical State Abstraction for Scalable GUI Agents. In Findings of the Association for Computational Linguistics: ACL 2026, pages 11965–11985, San Diego, California, United States. Association for Computational Linguistics.