Textual user profiling is an effective paradigm for LLM-based recommendation, but existing methods usually treat profile generation as a one-shot summarization problem. This limitation becomes more severe in long-history recommendation, where early preference signals may be overcompressed and recent behaviors may be overemphasized. We propose LUPE, a Long-history User Profile Evolution framework for LLM-based recommendation. Instead of generating a static profile from the full history, LUPE maintains a persistent textual profile and updates it progressively over temporally ordered interaction chunks. This allows the model to preserve long-term preferences while incorporating new behavioral evidence. To learn how profiles should evolve, LUPE optimizes a profile evolution policy via reinforcement learning using feedback from a frozen downstream LLM-based recommender. This allows the model to directly align profile updates with recommendation utility rather than relying on heuristic or supervised profile targets. Experiments on Yelp, Amazon CDs and Vinyl, and Amazon Books show that LUPE consistently outperforms strong baselines on both rating prediction and ranking tasks. Further analyses show that explicit profile evolution is especially helpful for users with long and heterogeneous interaction histories. The code repository: https://github.com/LongHistoryProfile/long_history_profile.

We introduce GUI-360°, a large-scale dataset and benchmark suite for desktop productivity computer-using agents (CUAs). GUI-360° provides an automated pipeline for query sourcing, template-based environment construction, task instantiation, batched execution, and LLM-assisted quality filtering in Windows office applications. The corpus contains 131K validated successful action steps across 17K trajectories for training and evaluation, together with 1.09M failed execution steps for failure analysis and future recovery training. Each step provides screenshots, accessibility metadata, instantiated goals, reasoning traces, and structured GUI/API actions. GUI-360° supports GUI grounding, screen parsing, and action prediction under a hybrid GUI+API action space. Experiments show that current vision–language models struggle with fine-grained grounding and stepwise action prediction, while supervised fine-tuning on GUI-360° brings consistent in-domain and external benchmark gains. Our code and dataset are available at https://github.com/2020-qqtcg/GUI-360 and https://huggingface.co/datasets/vyokky/GUI-360.

We introduce UFO, a UI-Fcused agent designed to fulfill user requests tailored to Windows OS applications by observing and analyzing the GUI and control information of these applications. UFO utilizes a hierarchical dual-agent framework that decomposes user requests using a divide-and-conquer approach, enabling seamless navigation and addressing sub-tasks across multiple applications. It also incorporates a control interaction module tailored for Windows OS, which detects control elements effectively and allows for fully automated execution. As a result, UFO simplifies complex and time-consuming processes into tasks that can be completed with natural language commands.We conducted testing of UFO across 9 popular Windows applications, encompassing a variety of scenarios. The results derived from both quantitative metrics and real-case studies, underscore the superior effectiveness of UFOin fulfilling user requests. To the best of our knowledge, UFO stands as the first UI agent specifically tailored for task completion within the Windows OS.

Recent advances in retrieval-augmented generation (RAG) have substantially improved question-answering systems, particularly for factoid ‘5Ws’ questions. However, significant challenges remain when addressing ‘1H’ questions, specifically how-to questions, which are integral for decision-making and require dynamic, step-by-step responses. The key limitation lies in the prevalent data organization paradigm, chunk, which commonly divides documents into fixed-size segments, and disrupts the logical coherence and connections within the context. To address this, we propose THREAD, a novel data organization paradigm enabling systems to handle how-to questions more effectively. Specifically, we introduce a new knowledge granularity, ‘logic unit’ (LU), where large language models transform documents into more structured and loosely interconnected LUs. Extensive experiments across both open-domain and industrial settings show that THREAD outperforms existing paradigms significantly, improving the success rate of handling how-to questions by 21% to 33%. Additionally, THREAD demonstrates high adaptability across diverse document formats, reducing retrieval information by up to 75% compared to chunk, and also shows better generalizability to ‘5Ws’ questions, such as multi-hop questions, outperforming other paradigms.