Browser-use agents automate everyday web tasks through structured DOM interfaces or vision-language models operating on page screenshots. However, web pages can change between observation and action execution due to advertisements, asynchronous loading, and timed updates, causing agents to act on stale assumptions. We frame this temporal mismatch as a time-of-check to time-of-use (TOCTOU) vulnerability in browser-use agents, where page changes during planning can alter the target, value, or validity of a planned action before execution. We introduce DYNWEB, a benchmark spanning synthesized and real-world websites, and evaluate 10 open-source agents as well as Codex’s computer-use feature across different observation and action modalities. All 11 evaluated systems exhibit at least one TOCTOU violation, showing that the vulnerability is not limited to a particular agent design. To reduce this exposure, we design a lightweight pre-execution validation mechanism that reduces the exploitable window from approximately 10 s to 0.13 s. Across three agent frameworks, the mechanism reduces the number of vulnerable DYNWEB cases to 0 out of 14. Adaptive attacks further show that stealthy single-mutation attacks succeed in at most 2.2% of trials, while event-driven timing reveals the limits of pre-execution validation.

Recently, long-thought reasoning LLMs, such as OpenAI’s O1, adopt extended reasoning processes similar to how humans ponder over complex problems. This reasoning paradigm significantly enhances the model’s problem-solving abilities and achieves promising results. However, long-thought reasoning process leads to a substantial increase in inference time. A pressing challenge is reducing the inference overhead of long-thought LLMs while ensuring accuracy. In this paper, we identify that long-thought reasoning models struggle to effectively allocate token budgets based on problem difficulty and reasoning redundancies. To address this, we propose Length-Harmonizing Fine-Tuning (O1-Pruner), aiming at minimizing reasoning overhead while maintaining accuracy. This effective fine-tuning method first estimates the LLM’s baseline performance through pre-sampling and then uses RL-style fine-tuning to encourage the model to generate shorter reasoning processes under accuracy constraints. This allows the model to achieve efficient reasoning with lower redundancy while maintaining accuracy. Experiments on various mathematical reasoning benchmarks show that O1-Pruner not only significantly reduces inference overhead but also achieves higher accuracy, providing a novel and promising solution to this challenge.

Graphical User Interface (GUI) agents aim to automate a wide spectrum of human tasks by emulating user interaction. Despite rapid advancements, current approaches are hindered by several critical challenges: data bottleneck in end-to-end training, high cost of delayed error detection, and risk of contradictory guidance. Inspired by the human cognitive loop of Thinking, Alignment, and Reflection, we present D-Artemis—a novel deliberative framework in this paper. D-Artemis leverages a fine-grained, app-specific tip retrieval mechanism to inform its decision-making process. It also employs a proactive Pre-execution Alignment stage, where Thought-Action Consistency (TAC) Check module and Action Correction Agent (ACA) work in concert to mitigate the risk of execution failures. A post-execution Status Reflection Agent (SRA) completes the cognitive loop, enabling strategic learning from experience. Crucially, D-Artemis enhances the capabilities of general-purpose Multimodal large language models (MLLMs) for GUI tasks without the need for training on complex trajectory datasets, demonstrating strong generalization. D-Artemis achieves SOTA among open-source general models on AndroidWorld (75.8%) and ScreenSpot-V2 (96.8%). Extensive ablation studies further demonstrate the significant contribution of each proposed component.

Deep generative modeling of natural languages has achieved many successes, such as producing fluent sentences and translating from one language into another. However, the development of generative modeling techniques for paraphrase generation still lags behind largely due to the challenges in addressing the complex conflicts between expression diversity and semantic preservation. This paper proposes to generate diverse and high-quality paraphrases by exploiting the pre-trained models with instance-dependent prompts. To learn generalizable prompts, we assume that the number of abstract transforming patterns of paraphrase generation (governed by prompts) is finite and usually not large. Therefore, we present vector-quantized prompts as the cues to control the generation of pre-trained models. Extensive experiments demonstrate that the proposed method achieves new state-of-art results on three benchmark datasets, including Quora, Wikianswers, and MSCOCO. We will release all the code upon acceptance.