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When Users Change Their Mind: Evaluating Interruptible Agents in Long-Horizon Web Navigation

Henry Peng Zou, Chunyu Miao, Wei-Chieh Huang, Yankai Chen, Yue Zhou, Hanrong Zhang · Apr 1, 2026

Citations: 0

Match reason: Keyword overlap 1/1 across title and protocol fields.

Score: 83% High protocol signal Freshness: Warm Status: Ready
Critique Edit Simulation Env Long Horizon Coding
  • As LLM agents transition from short, static problem solving to executing complex, long-horizon tasks in dynamic environments, the ability to handle user interruptions, such as adding requirement or revising goals, during mid-task execution…
  • In this paper, we present the first systematic study of interruptible agents in long-horizon, environmentally grounded web navigation tasks, where actions induce persistent state changes.
Open paper

Match reason: Keyword overlap 1/1 across title and protocol fields.

Score: 83% High protocol signal Freshness: Warm Status: Ready
Demonstrations Human EvalLlm As Judge Long Horizon General
  • LLM agents fail on the majority of real-world tasks -- GPT-4o succeeds on fewer than 15% of WebArena navigation tasks and below 55% pass@1 on ToolBench (Zhou et al., 2024; Qin et al., 2024) -- yet every failed trajectory is routinely…
  • We introduce AgentHER, a framework that recovers this lost training signal by adapting the Hindsight Experience Replay (HER; Andrychowicz et al., 2017) principle to natural-language agent trajectories for offline data augmentation.
Open paper
World-Model-Augmented Web Agents with Action Correction

Zhouzhou Shen, Xueyu Hu, Xiyun Li, Tianqing Fang, Juncheng Li, Shengyu Zhang · Feb 17, 2026

Citations: 0

Match reason: Keyword overlap 1/1 across title and protocol fields.

Score: 83% Moderate protocol signal Freshness: Warm Status: Ready
Llm As JudgeSimulation Env Multi Agent General
  • To address these challenges, we propose WAC, a web agent that integrates model collaboration, consequence simulation, and feedback-driven action refinement.
  • To overcome the cognitive isolation of individual models, we introduce a multi-agent collaboration process that enables an action model to consult a world model as a web-environment expert for strategic guidance; the action model then…
Open paper
Mobile-Agent-v3.5: Multi-platform Fundamental GUI Agents

Haiyang Xu, Xi Zhang, Haowei Liu, Junyang Wang, Zhaozai Zhu, Shengjie Zhou · Feb 15, 2026

Citations: 0

Match reason: Keyword overlap 1/1 across title and protocol fields.

Score: 83% Moderate protocol signal Freshness: Warm Status: Fallback
Simulation Env Long Horizon General
  • The paper introduces GUI-Owl-1.5, the latest native GUI agent model that features instruct/thinking variants in multiple sizes (2B/4B/8B/32B/235B) and supports a range of platforms (desktop, mobile, browser, and more) to enable cloud-edge…
  • (2) Unified Enhancement of Agent Capabilities: we use a unified thought-synthesis pipeline to enhance the model's reasoning capabilities, while placing particular emphasis on improving key agent abilities, including Tool/MCP use, memory and…
Open paper
Let's Think in Two Steps: Mitigating Agreement Bias in MLLMs with Self-Grounded Verification

Moises Andrade, Joonhyuk Cha, Brandon Ho, Vriksha Srihari, Karmesh Yadav, Zsolt Kira · Jul 15, 2025

Citations: 0

Match reason: Keyword overlap 1/1 across title and protocol fields.

Score: 78% High protocol signal Freshness: Cold Status: Ready
Pairwise Preference Automatic MetricsSimulation Env Long Horizon MathCoding
  • We evaluate MLLM verifiers across web navigation, computer use, and robotics, spanning 13+ models, 28+ designs, and thousands of trajectories from diverse agents.
  • Our methods yield more human-aligned verifiers, improving failure detection by 25pp and accuracy by 14pp.
Open paper
R-WoM: Retrieval-augmented World Model For Computer-use Agents

Kai Mei, Jiang Guo, Shuaichen Chang, Mingwen Dong, Dongkyu Lee, Xing Niu · Oct 13, 2025

Citations: 0

Match reason: Keyword overlap 1/1 across title and protocol fields.

Score: 78% Moderate protocol signal Freshness: Cold Status: Fallback
Simulation Env Long Horizon General
  • Large Language Models (LLMs) can serve as world models to enhance agent decision-making in digital environments by simulating future states and predicting action outcomes, potentially eliminating costly trial-and-error exploration.
Open paper
Go-Browse: Training Web Agents with Structured Exploration

Apurva Gandhi, Graham Neubig · Jun 4, 2025

Citations: 0

Match reason: Keyword overlap 1/1 across title and protocol fields.

Score: 78% High protocol signal Freshness: Cold Status: Fallback
Simulation Env Web Browsing General
  • To address this, we propose Go-Browse, a method for automatically collecting diverse and realistic web agent data at scale through structured exploration of web environments.
  • Fine-tuning a 7B parameter language model on this dataset achieves a success rate of 21.7% on the WebArena benchmark, beating GPT-4o mini by 2.4% and exceeding current state-of-the-art results for sub-10B parameter models by 2.9%.
Open paper

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