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ECHOES system improves GUI agent reasoning with history compression

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced ECHOES, a novel approach to enhance the long-horizon reasoning capabilities of GUI agents. This method utilizes privileged information available during training to compress textual history more effectively, thereby reducing inference costs and improving reliability. ECHOES employs a Compression-Think-Action (CTA) format for recursive state updates and reasoning, guided by a teacher model that provides on-policy distillation. A new benchmark, ECHOESBMK, has also been developed to specifically evaluate visual perception and cross-modal state retention in GUI agents. AI

IMPACT Enhances long-horizon reasoning in GUI agents, potentially reducing costs and improving performance on complex tasks.

RANK_REASON The cluster contains an academic paper detailing a new method and benchmark for AI research. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

ECHOES system improves GUI agent reasoning with history compression

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The cluster contains an academic paper detailing a new method and benchmark for AI research. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Tengda Guo (Tsinghua University), Jiawang Hu (StepFun), Yifei Tao (Nanyang Technological University), Yin Wang (StepFun), Haotian Hu (Zhejiang Leapmotor Technology Co., Ltd), Fanyi Wang (Honor), Liujuan Tang (StepFun) ·

    ECHOES: Seeing Beyond the Current Image via Recursive History Compression and Privileged On-Policy Distillation

    arXiv:2610.22408v1 Announce Type: new Abstract: Long-horizon GUI agents rely on visual history to maintain task states, but retaining historical screenshots increases visual-token usage and inference costs, while textual summaries can discard critical state information, undermini…

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