The field of information retrieval has significantly transformed with the integration of AI technologies. AI agents, especially those leveraging LLMs and vast computational power, have revolutionized in- formation retrieval, processing, and presentation. LLM agents, with advanced memory, reasoning, and planning capabilities, can perform complex tasks, engage in coherent conversations, and provide personalized responses. Despite these advancements, challenges such as ensuring relevance and accuracy, mitigating biases, providing real-time responses, and maintaining data security remain. This workshop aims to explore these challenges, share innovative solutions, and discuss future directions. It will provide a platform to bring together researchers and practitioners to discuss the latest theoretical advancements and practical implementations of AI agents in information retrieval. Topics include AI in search, recommendation, and personalization systems. By gathering a diverse group of experts, the workshop seeks to deepen the understanding of AI agents in information retrieval, advance the field, and enhance its societal impact. Participants will gain insights into cutting-edge research and emerging trends, and foster knowledge exchange and collaboration within the community.
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The digital advertising field has always had challenging ML problems, learning from petabytes of data that is highly imbalanced, reactivity times in the milliseconds, and more recently compounded with the complex user's path to purchase across devices, across platforms, and even online/real-world behavior. The AdKDD workshop continues to be a forum for researchers in advertising, during and after KDD. Our website, which hosts slides and abstracts, continues to receive a large number of monthly visits and active users. In surveys during AdKDD 2019 and 2020, over 60% agreed that AdKDD is the reason they attended KDD, and over 90% indicated they would attend next year. The 2025 edition is particularly timely because of the increasing application of Graph-based NN and Generative AI models in advertising. Coupled with privacy-preserving initiatives, such as those enforced by GDPR or CCPA, the future of computational advertising is at an interesting crossroads. For this edition, we plan to solicit papers that span the spectrum of deep user understanding while remaining privacy-preserving. In addition, we will seek papers that discuss fairness in the context of advertising, to what extent does hyper-personalization work, and whether the ad industry as a whole needs to think through more effective business models such as incrementality. We have hosted several academic and industry luminaries as keynote speakers and have found our invited speaker series hosting expert practitioners to be an audience favorite. We will continue fielding a diverse set of keynote speakers and invited talks for this edition as well. As with past editions, we hope to motivate researchers in this space to think not only about the ML aspects but also to spark conversations about the societal impact of online advertising.
A Survey on Unifying Large Language Models and Knowledge Graphs for Biomedicine and Healthcare
PDF ↗In recent years, the landscape of digital biomedicine and healthcare has been reshaped due to the disruptive breakthroughs in AI-facilitated by tremendous data and high-performance computers, large language models (LLMs) have transformed information technology from accessing data to performing analytical tasks. While demonstrating unprecedented capabilities, LLMs have been found unreliable in tasks requiring factual knowledge and rigorous reasoning. Biomedicine and healthcare, as an important vertical domain rapidly benefitting from progress in AI, necessitates strict requirements on the accuracy, controllability, and interpretability of analytical models, posing critical challenges for LLMs. Despite recent studies addressing the hallucination problem of LLMs, research on empowering LLMs with the ability to plan, reason, and ground with explicit knowledge has also started to prosper, especially in the biomedicine and healthcare domain. On the other hand, biomedical data are enormous and notoriously complex, coming from various sources (e.g., biomedical knowledge bases, online literature, and hospitals) and bearing various modalities (e.g., tables, texts, images and time-series). Healthcare professionals have spent decades collecting, cleaning, and curating various types of data. The processes are extremely costly, producing various datasets with different data schemas, coding systems, and quality standards, many privately owned by the creators, making their integrative analysis and utilization through unified AI techniques still rather challenging. The generalizability of LLMs across different types of data endow them strong promises in automating the processing of large-scale complex healthcare data such as into unified knowledge graphs (KGs). Our goal in this survey is to systematically investigate and summarize recent studies on the unification of LLMs and KGs, towards fully utilizing the value of complex data, unleashing the power of generative AI, and expediting next-generation AI for biomedicine and healthcare applications.
Path planning is a critical challenge for Autonomous Underwater Vehicles (AUVs) due to complex underwater environments, including ocean currents, dynamic obstacles, and limited sensing capabilities. The lack of a standardized benchmarking framework has hindered direct comparisons between algorithms, slowing progress in the field. To address this, we introduce an open-source benchmarking platform for underwater AUV path planning, designed to provide a unified evaluation environment, automated performance assessment, and reproducible experiments. Built on the HoloOcean simulation platform, our benchmark incorporates realistic underwater dynamics, such as ocean currents, static and dynamic obstacles, and sensor models. It supports a range of path planning tasks, from basic obstacle avoidance to complex scenarios with current disturbances. The platform is compatible with classical algorithms (e.g., A*, RRT), evolutionary methods (e.g., GA, ACO), and deep reinforcement learning (e.g., Soft Actor-Critic, SAC). We define key evaluation metrics, including path efficiency (length, smoothness, energy consumption), task success rate, collision rate, and computational cost. Automated tools enable systematic algorithm comparisons across scenarios, generating standardized performance results and visualizations. This open-source, extensible framework aims to advance underwater path planning research by enabling fair comparisons and guiding future algorithm development. It provides a scalable foundation for evaluating AUV path planning methods under simulated real-world conditions, fostering innovation in the field. All source code and experimental configurations will be available on the GitHub Page: https://github.com/IoET-y/UP-bench.
On-demand ride-hailing services like DiDi, Uber, and Lyft have transformed urban transportation, offering unmatched convenience and flexibility. In this paper, we introduce DiMA, an LLM-powered ride-hailing assistant deployed in DiDi Chuxing. Its goal is to provide seamless ride-hailing services and beyond through a natural and efficient conversational interface under dynamic and complex spatiotemporal urban contexts. To achieve this, we propose a spatiotemporal-aware order planning module that leverages external tools for precise spatiotemporal reasoning and progressive order planning. Additionally, we develop a cost-effective dialogue system that integrates multi-type dialog repliers with cost-aware LLM configurations to handle diverse conversation goals and trade-off response quality and latency. Furthermore, we introduce a continual fine-tuning scheme that utilizes real-world interactions and simulated dialogues to align the assistant's behavior with human prefered decision-making processes. Since its deployment in the DiDi application, DiMA has demonstrated exceptional performance, achieving 93% accuracy in order planning and 92% in response generation during real-world interactions. Offline experiments further validate DiMA's capabilities, showing improvements of up to 70.23% in order planning and 321.27% in response generation compared to three state-of-the-art agent frameworks, while reducing latency by 0.72x to 5.47x. These results establish DiMA as an effective, efficient, and intelligent mobile assistant for ride-hailing services.
The proliferation of complex non-factoid questions in modern information seeking (IS) systems exposes critical limitations in conventional Retrieval-Augmented Generation (RAG) approaches, particularly their static search strategies and the lack of systematic multi-source information integration capabilities. Facing these limitations, we present PASS (Proactive Agent-driven Search System), a novel multi-agent framework that operationalizes human-like proactive search strategies through five specialized agents: Revealer for intent analysis, Navigator for search planning, Seeker/Reader for adaptive retrieval, and Writer for response synthesis, systematically expanding the search space through iterative query refinement and multi-perspective knowledge integration. Crucially, our framework demonstrates remarkable adaptability to mid-sized LLMs, demonstrating its scalability in resource-constrained environments. To comprehensively assess the effectiveness of the proposed framework, we carry out extensive experiments on both mid-sized and proprietary large-scale LLMs, evaluating response quality for complex non-factoid questions using a newly introduced nugget-based assessment. Experimental results from offline nugget-based evaluation and online A/B Tests confirm substantial improvements in answer quality, advancing proactive information seeking methodologies and offering practical pathways for democratizing complex reasoning capabilities to resource-constrained environments.
Mobile traffic forecasting allows operators to anticipate network dynamics and performance in advance, offering substantial potential for enhancing service quality and improving user experience. It involves multiple tasks, including long-term prediction, short-term prediction, and generation tasks that do not rely on historical data. By leveraging the different types of mobile network data generated from these tasks, operators can perform a variety of network optimizations and planning activities, such as base station (BS) deployment, resource allocation, energy optimization, etc. However, existing models are often designed for specific tasks and trained with specialized data, and there is a lack of universal models for traffic forecasting across different urban environments. In this paper, we propose a Universal model for Mobile traffic forecasting (UoMo), aiming to handle diverse forecasting tasks of short/long-term predictions and distribution generation across multiple cities to support network planning and optimization. UoMo combines diffusion models and transformers, where various spatio-temporal masks are proposed to enable UoMo to learn intrinsic features of different tasks, and a contrastive learning strategy is developed to capture the correlations between mobile traffic and urban contexts, thereby improving its transfer learning capability. Extensive evaluations on 9 real-world datasets demonstrate that UoMo outperforms current models in various forecasting tasks and zero/few-shot learning. It shows an average accuracy improvement of 27.85%, 18.57%, and 15.6% in long-term prediction, short-term prediction, and generation tasks, respectively, showcasing its strong forecasting capability. We deploy UoMo on China Mobile's JiuTian platform, leveraging the predicted mobile data to optimize live networks. This optimization includes BS deployment, resulting in a 25.3% increase in served users, and BS sleep control, which reduces equipment depreciation by 40.7%. The source code is available online: https://github.com/tsinghua-fib-lab/UoMo.
A Fast and Accurate Block Compression Solution for Spatiotemporal Kernel Density Visualization
PDF ↗Spatiotemporal Kernel Density Visualization (STKDV) has been widely used across various domains in geospatial analysis, e.g., urban planning, traffic/traffic accident hotspot analysis, crime hotspot analysis, and disease spread modeling. However, STKDV is a computationally expensive tool, which has been complained by many domain experts. Although many recent solutions, including the sliding-window-based solution (SWS) and the prefix-matrix-based solution (PREFIX), have been proposed for improving the efficiency of generating an exact STKDV, these solutions still cannot be scalable to handle large-scale location datasets. To tackle this efficiency issue, we propose the pioneering block compression solution, called COMP, which can compress (or represent) a location dataset by a small amount of blocks. By combining COMP with the existing exact solutions, i.e., SWS and PREFIX, we show that COMPSWS and COMPPREFIX can generate approximate STKDV with an ε-absolute error guarantee based on properly tuning the block size. Experimental results on four large-scale location datasets (up to 6.782 million data points) also verify that COMPSWS and COMP_PREFIX can achieve speedups of 4.1x to 677.16x and 1.45x to 143.52x compared with SWS and PREFIX, respectively, without degrading the visualization results. The code of this paper can be found in https://github.com/YovelaZ/COMP.
Despite the outstanding capabilities of large language models (LLMs), knowledge-intensive reasoning still remains a challenging task due to LLMs' limitations in compositional reasoning and the hallucination problem. A prevalent solution is to employ chain-of-thought (CoT) with retrieval-augmented generation (RAG), which first formulates a reasoning plan by decomposing complex questions into simpler sub-questions, and then applies iterative RAG at each sub-question. However, prior works exhibit two crucial problems: inadequate reasoning planning and poor incorporation of heterogeneous knowledge. In this paper, we introduce AtomR, a framework for LLMs to conduct accurate heterogeneous knowledge reasoning at the atomic level. Inspired by how knowledge graph query languages model compositional reasoning through combining predefined operations, we propose three atomic knowledge operators, a unified set of operators for LLMs to retrieve and manipulate knowledge from heterogeneous sources. First, in the reasoning planning stage, AtomR decomposes a complex question into a reasoning tree where each leaf node corresponds to an atomic knowledge operator, achieving question decomposition that is highly fine-grained and orthogonal. Subsequently, in the reasoning execution stage, AtomR executes each atomic knowledge operator, which flexibly selects, retrieves, and operates atomic level knowledge from heterogeneous sources. We also introduce BlendQA, a challenging benchmark specially tailored for heterogeneous knowledge reasoning. Experiments on three single-source and two multi-source datasets show that AtomR outperforms state-of-the-art baselines by a large margin, with absolute F1 score improvements of 9.4% on 2WikiMultihop and 9.5% on BlendQA. We release our code and data https://github.com/THU-KEG/AtomR.git.
In this paper, we investigate a new problem of truss-based why-not community search. Given a k-truss community C in a graph G and a why-not vertex w ∉ C , the goal is to insert the minimum number of new edges into G to ensure that w becomes part of the k-truss community. This problem has a wide range of applications, such as friends recommendation and transportation planning. We prove that the truss-based why-not community search problem is NP-hard and propose two efficient heuristic algorithms: the expansion-based algorithm and the simulation-based algorithm. Specifically, the expansion-based algorithm incrementally inserts the edges one by one, guided by a carefully designed edge goodness function that quantifies edge quality to ensure optimal selection. In contrast, the simulation-based algorithm firstly inserts a sufficient number of edges into G to immediately include w in a k-truss community and then removes redundant edges to minimize insertions. Furthermore, we implement a set of optimizations to further enhance the efficiency of both algorithms. Extensive experiments on real-world graphs demonstrate the efficiency and effectiveness of our proposed methods and optimizations.
Next location prediction is a critical task in human mobility modeling, with broad applications in personalized recommendation, urban planning, and location-based services. Recently, researchers have used prompt-based large language models (LLMs) to improve next location prediction with pre-trained knowledge. However, they face inherent challenges in bridging the gap between textual prompts for semantic contextual understanding and human mobility data for transition pattern modeling. In this paper, we introduce SILO, a framework designed for Semantic Integration in LOcation prediction via LLMs. We first construct a hybrid semantic space that seamlessly integrates ID-based embeddings, text-derived semantics, and auxiliary contextual information, enabling comprehensive modeling of sequential mobility patterns alongside contextual nuances. We then propose user-centric prompts that specify the prediction task for LLMs while embedding user context within a special token. Further, we utilize LLMs as the prediction backbone to process both user-specific prompts and hybrid ID-context embeddings of location sequences. To enhance predictive performance, we finally introduce a dual-logits strategy, combining sequential transition logits with user profile-guided semantic preference logits. Extensive experiments on two large-scale real-world mobility datasets demonstrate that SILO significantly outperforms state-of-the-art baselines, validating its effectiveness in modeling complex mobility patterns through semantic integration using LLMs.
Empowering large language models (LLMs) with effective tool utilization capabilities is crucial for enabling AI agents to solve complex problems. However, current models face two major limitations: (1) unreliable tool planning and invocation due to low-quality instruction datasets (e.g., widespread hallucinated API calls), and (2) weak tool reflection abilities (over 90% of errors cannot be corrected) resulting from static imitation learning. To address these critical limitations, we propose Tool-MVR, a novel Tool-Augmented LLM that achieves comprehensive System 2 reasoning through two key innovations. Specifically, we first introduce Multi-Agent Meta-Verification (MAMV), a systematic pipeline that rigorously validates APIs, queries, and reasoning trajectories to construct ToolBench-V, a new high-quality instruction dataset that addresses the limitation of unreliable tool planning and invocation. Second, we propose Exploration-based Reflection Learning (EXPLORE), which enhances tool reflection capabilities by leveraging tool feedback through a dynamic "Error → Reflection → Correction" learning paradigm, resulting in our reflection dataset ToolBench-R and addressing the critical weakness in tool reflection. Finally, we obtain Tool-MVR by finetuning open-source LLMs (e.g., Qwen-7B) on both ToolBench-V and ToolBench-R. Our experiments demonstrate that Tool-MVR achieves state-of-the-art performance on StableToolBench, surpassing both ToolLLM (by 23.9%) and GPT-4 (by 15.3%) while reducing API calls by 31.4%, with strong generalization capabilities across unseen tools and scenarios. Additionally, on our proposed RefineToolBench, the first benchmark specifically designed to evaluate tool reflection capabilities. Tool-MVR achieves a 58.9% error correction rate, significantly outperforming ToolLLM's 9.1%.
Understanding and predicting urban dynamics is crucial for managing transportation systems, optimizing urban planning, and enhancing public services. While neural network-based approaches have achieved success, they often rely on task-specific architectures and large volumes of data, limiting their ability to generalize across diverse urban scenarios. Meanwhile, Large Language Models (LLMs) offer strong reasoning and generalization capabilities, yet their application to spatial-temporal urban dynamics remains underexplored. Existing LLM-based methods struggle to effectively integrate multifaceted spatial-temporal data and fail to address distributional shifts between training and testing data, limiting their predictive reliability in real-world applications. To bridge this gap, we propose UrbanMind, a novel spatial-temporal LLM framework for multifaceted urban dynamics prediction that ensures both accurate forecasting and robust generalization. At its core, UrbanMind introduces Muffin-MAE, a multifaceted fusion masked autoencoder with specialized masking strategies that capture intricate spatial-temporal dependencies and intercorrelations among multifaceted urban dynamics. Additionally, we design a semantic-aware prompting and fine-tuning strategy that encodes spatial-temporal contextual details into prompts, enhancing LLMs' ability to reason over spatial-temporal patterns. To further improve generalization, we introduce a test time adaptation mechanism with a test data reconstructor, enabling UrbanMind to dynamically adjust to unseen test data by reconstructing LLM-generated embeddings. Extensive experiments on real-world urban dynamics datasets from multiple cities demonstrate the effectiveness of UrbanMind. The results consistently show that UrbanMind outperforms state-of-the-art baselines, achieving superior accuracy and strong generalization, even in zero-shot scenarios with no prior data.
Accurate weather forecasting on Subseasonal-to-Seasonal (S2S) timescale is critical to human society such as agriculture planning and extreme weather preparation. Although data-driven models have become alternatives to computationally intensive Numerical Weather Prediction (NWP) systems, existing Transformer-based approaches suffer from biases due to planar projections distorting the spherical geometry and inadequate handling of vector-scalar variable interactions (e.g., wind velocity vs. temperature). To address these limitations, we propose a graph-based Equivariant and Invariant Message Passing (EIMP) framework that directly processes spherical grid data. It maintains SO(3) equivariant embeddings for vector data and SO(3) invariant embeddings for scalar data, which are interacted by a shared invariant message embedding. Guaranteed equivariant and invariant message aggregation functions are proposed to update embeddings under strict symmetry constraints. Extensive experiments on the Earth Reanalysis 5 (ERA5) reanalysis dataset of 41 years demonstrate the proposed model achieves significant improvement over advanced data-driven models and skillful numerical ECMWF systems. Additionally, we empirically show that EIMP demonstrates geometrically superior predictions and conduct ablation studies to validate the efficacy of its design.
Lifelong Multi-Agent Path Finding (LMAPF) focuses on planning conflict-free paths for agents, like autonomous vehicles, that are continuously assigned new tasks. The synergy of search-based and learning-based methods holds promise for striking a balance in-between effectiveness and efficiency but still faces several challenges such as inferior initial paths, weak search-learning synergy and low sample utilization rate. To address these issues, this paper proposes a new synergized LMAPF approach, named Synergistic Multi-Agent Path Optimization (SMAPO), which consists of two tightly-coupled phases: Primordial Planning and Decision Refinement. In the Primordial Planning phase, we introduce a novel load-balanced A* algorithm that integrates planned and perceived congestion costs, which enhances initial solution quality by evenly distributing spatiotemporal traffic loads, thereby mitigating potential conflicts. In the Decision Refinement phase, we propose a novel Encoder-Decoder based neural network to learn a collaborative optimization policy through multi-agent reinforcement learning. In addition, we leverage dual transformations to augment trajectory samples during online learning, enhancing both the sample utilization rate and overall learning stability. Extensive experiments reveal that our SMAPO is superior to the state-of-the-art baselines in effectiveness, efficiency, and generalization capability. Source code is available at https://github.com/ByteUser-blues/SMAPO.
Synthetic human trajectory data becoming increasingly prominent in various applications, including urban planning, traffic control, and crowd monitoring. Recent neural generative models for human trajectory data mostly follow an unconditional generative paradigm that relies on a pure data-driven imitative learning scheme, without considering the rich context of human mobility (e.g., social events or weather conditions) which may significantly impact the underlying human mobility patterns. Against this background, we propose Marionette, a Manipulatable generative model for human trajectory data with fine-grained conditions. Specifically, Marionette integrates both global and partial mobility-related contexts and extracts both sequence-level and event-level conditions. Afterward, it designs fine-grained and cascading conditioning mechanisms for modeling the temporal and spatial dynamics based on diffusion-alike Temporal Point Processes (TPPs) and discrete diffusion models, respectively, offering fine-grained controllable generative modeling of human trajectory data with both global and partial mobility-related contexts. We conduct a thorough evaluation on two real-world human trajectory datasets against a sizeable collection of baselines. Results show that our Marionette consistently outperforms the best baselines by 13.96-54.13% on statistical and distributional similarity metrics and by 9.36-40.63% in task-based data utility evaluation. Ablation studies verify our key design choices. Case studies also demonstrate the manipulability of Marionette in generating data in previously unseen scenarios.
Medical imaging is crucial for diagnosing a patient's health condition, and accurate segmentation of these images is essential for isolating regions of interest to ensure precise diagnosis and treatment planning. Existing methods primarily rely on bounding boxes or point-based prompts, while few have explored text-related prompts, despite clinicians often describing their observations and instructions in natural language. To address this gap, we first propose a RAG-based free-form text prompt generator that leverages the domain corpus to generate diverse and realistic descriptions. Then, we introduce FLanS, a novel medical image segmentation model that handles various free-form text prompts, including professional anatomy-informed queries, anatomy-agnostic position-driven queries, and anatomy-agnostic size-driven queries. Additionally, our model also incorporates a symmetry-aware canonicalization module to ensure consistent, accurate segmentations across varying scan orientations and reduce confusion between the anatomical position of an organ and its appearance in the scan. FLanS is trained on a large-scale dataset of over 100k medical images from 7 public datasets. Comprehensive experiments demonstrate the model's superior language understanding and segmentation precision, along with a deep comprehension of the relationship between them, outperforming SOTA baselines on both in-domain and out-of-domain datasets.
Recently, full-graph Graph Neural Networks (GNNs) have gained prominence by addressing complex problems such as weather forecasting and material discovery. Existing full-graph training methods do not fully manage intermediate data generated during training and rely on rigid inter-GPU communication, limiting both training speed and scale. We propose FlexGNN, which fully manages intermediate data and adaptively performs inter-GPU communication by generating and optimizing best-effort training execution plans. Extensive experiments demonstrate that FlexGNN significantly outperforms existing full-graph GNN methods in both training speed and scale. Specifically, it is up to 5.4X faster than HongTu and up to 95.5X faster than NeutronStar.
Large-scale human mobility data contains rich spatial and temporal information for urban sensing, crowd flow modeling, and urban planning. However, it is usually difficult to access wide-coverage, long-term, and consistent-time human mobility data. Most of the publicly available datasets are actually only records of discontinuous trajectories of a very small portion of urban citizens in asynchronous time due to the limited usage of apps for location data collection or the limited number of volunteers. To address this problem and empower open urban research, this paper constructs a high-quality human mobility dataset by generating large-scale citizen trajectories based on massive cellular signaling data. Particularly, we first propose a heatmap diffusion module to generate a probability heatmap that produces plausible trajectories at both the individual and city scales. Then, we propose a masked trajectory AutoEncoder, which can generate individual trajectory embeddings from partially given or empty trajectories. Third, a flexible framework is provided to incorporate the heatmap diffusion module with the masked trajectory embeddings, demonstrating significant flexibility in handling both fully masked trajectories for city-wide analysis and partially masked trajectories for specific locations. We have conducted extensive experiments to validate the utility of the regenerated trajectories at both individual and region levels for various applications. Numerous case studies further illustrate that our model learns not only the distribution of the trajectories but also the semantics of different urban areas. In summary, this paper provides a Heatmap Diffusion framework based on a Masked Trajectory AutoEncoder to regenerate flexible trajectories for open urban research. Correspondingly, we will try to open a large-scale human mobility data service for open urban research. Further information can be found at https://github.com/Rising0321/FinalOpenUR.
It has become increasingly clear that recommender systems that overly focus on short-term engagement prevents users from exploring diverse interests, ultimately hurting long-term user experience. To tackle this challenge, numerous diversification algorithms have been proposed as the final stage of recommender systems. These algorithms typically rely on measures of item similarity, aiming to maximize the dissimilarity across items in the final set of recommendations. However, in this work, we demonstrate the benefits of going beyond item-level similarities by utilizing higher-level user understanding-specifically, user intents that persist across multiple interactions or recommendation sessions-in diversification. Our approach is motivated by the observation that user behaviors on online platforms are largely driven by their underlying intents. Therefore, final recommendations should ensure that a diverse set of user intents is accurately represented. While user intent has primarily been studied in the context of search, it is less clear how to incorporate real-time dynamic intent predictions in recommender systems. To address this gap, we develop a probabilistic intent-based whole-page diversification framework for the final stage of a recommender system. Starting with a prior belief of user intents, the proposed framework sequentially selects items for each position based on these beliefs and subsequently updates posterior beliefs about the intents. This approach ensures that different user intents are represented on a page, towards optimizing long-term user experience. We experiment with the intent diversification framework on YouTube, the world's largest video recommendation platform, serving billions of users daily. Live experiments on a diverse set of intents show that the proposed framework increases Daily Active Users (DAU) and overall user enjoyment, validating its effectiveness in facilitating long-term planning. Specifically, it enables users to consistently discover and engage with diverse content that aligns with their underlying intents over time, leading to an improved long-term user experience.