Item tokenizer encodes semantic embeddings into token IDs to replace the randomly assigned item IDs used in traditional recommendation models, fundamentally addressing the problems of excessive parameters and cold starts. However, the most common tokenizer, RQ-VAE, suffers from low decoding efficiency due to the inherent dependencies among its codebooks. Meanwhile, efficient independent tokenizers such as optimized product quantization (OPQ) still struggle with dimensional correlations and distribution complexity of semantic embeddings. In this work, we propose a f\underline{low}-based item \underline{T}okenizer (Tlow) to transform raw semantic embeddings into a latent space where embeddings conform to a unified standard normal distribution, achieving dual advantages of dimensional independence and distributional simplicity. Independent tokenization performed on these latent embeddings yields semantically clear token IDs. Additionally, we introduce a novel codebook guidance to align the codebook space with the token embedding space, further aiding the learning of more semantically distinct token embeddings. Offline experiments on four public datasets demonstrate that Tlow's tokenization and codebook guidance significantly improve recommendation performance. The improvement on cross-domain and multi-modal recommendations also proves the effectiveness of item tokenization in a simplified embedding space. Online experiments for a multi-modal retrieval task on China's largest social media platform WeChat validate Tlow's powerful distribution transformation capability. The retrieval model based on token IDs improves user CTR by 10.32\% globally and by 11.64\% for new items. Our codes are available at https://github.com/wjjln/Tlow.
Nian Li, Chonggang Song, Jingtao Ding et al.· 0 citations
Generating realistic and diverse pedestrian background flows is critical for numerous downstream applications, ranging from the training and validation of autonomous driving systems to the simulation of mobile communication networks. While recent diffusion-based models achieve state-of-the-art accuracy, they suffer from prohibitive inference latency, lack of physical consistency, and an inability to generalize across heterogeneous datasets, rendering them impractical for industrial Hardware-in-the-Loop testing. To address these challenges, we propose Real-time Adaptive Physics-Informed Diffusion (RAPID), a unified framework explicitly designed to balance high-fidelity generation with strict real-time constraints. First, we introduce a Canonical Representation Module that harmonizes diverse datasets via coordinate-invariant encoding and adaptive modality imputation, enabling unified training across varying scene scales. Second, we propose a Map Context Encoder that decouples computationally expensive map perception from the iterative denoising loop using cached latent embeddings. Third, a Physics-Informed Implicit Sampler integrates Social Force Model gradients as directional priors, encouraging physical consistency (e.g., collision avoidance). Extensive experiments on five heterogeneous benchmarks demonstrate that RAPID establishes a new state-of-the-art balance between fidelity and safety. Notably, it is the only framework capable of operating consistently below the 30 ms industrial threshold, maintaining almost constant inference latency regardless of crowd density. The system exhibits precise controllability over agent behaviors and has been successfully deployed in a production-grade autonomous-driving simulation platform as a core digital twin kernel for large-scale autonomous driving validation. The code is publicly available at https://github.com/tsinghua-fib-lab/RAPID.
Zihan Yu, Huandong Wang, Jingtao Ding et al.· Proceedings of the 32nd ACM...· 0 citations
Mobile GUI Agents powered by multimodal large language models (MLLMs) show promise in human-computer intelligence. However, current research primarily focuses on reactive task execution while lacking a comprehensive understanding-prediction-execution process for user intentions, which are the core requirements of active agents. In this paper, we propose the Act2Intention framework that builds an active mobile agent by integrating understanding, predicting user intentions, and executing decisions. First, we construct the Act2Intention Bench through data collection and validated generation, comprising 72,511 intentions and over 700,000 actions across 52 apps, thereby establishing the first benchmark for evaluating proactive agents via continuous intention-action trajectories. We further develop the Act2Intention Agent, achieving proactive services through Proactive-oriented Intention Understanding, Personalized Proactive Intention Prediction, and Experience-guided Intention Execution. Experimental results show that supervised fine-tuning on Act2Intention Bench yields absolute improvements of +32.0 Acc-S, +10.25 Acc-S, and +6.9 SSR points over non-fine-tuned counterparts under the same agent framework for intention understanding, prediction, and execution, respectively. This success underscores the necessity and value of the Act2Intention Bench, which establishes a standardized platform for developing and evaluating proactive agents and consequently paves the way for research on intention-driven human-computer interaction.
Xiaokai Yan, Jingtao Ding, Yong Li et al.· 0 citations
Generating realistic and diverse pedestrian background flows is critical for numerous downstream applications, ranging from the training and validation of autonomous driving systems to the simulation of mobile communication networks. While recent diffusion-based models achieve state-of-the-art accuracy, they suffer from prohibitive inference latency, lack of physical consistency, and an inability to generalize across heterogeneous datasets, rendering them impractical for industrial Hardware-in-the-Loop testing. To address these challenges, we propose Real-time Adaptive Physics-Informed Diffusion (RAPID), a unified framework explicitly designed to balance high-fidelity generation with strict real-time constraints. First, we introduce a Canonical Representation Module that harmonizes diverse datasets via coordinate-invariant encoding and adaptive modality imputation, enabling unified training across varying scene scales. Second, we propose a Map Context Encoder that decouples computationally expensive map perception from the iterative denoising loop using cached latent embeddings. Third, a Physics-Informed Implicit Sampler integrates Social Force Model gradients as directional priors, encouraging physical consistency (e.g., collision avoidance). Extensive experiments on five heterogeneous benchmarks demonstrate that RAPID establishes a new state-of-the-art balance between fidelity and safety. Notably, it is the only framework capable of operating consistently below the 30 ms industrial threshold, maintaining almost constant inference latency regardless of crowd density. The system exhibits precise controllability over agent behaviors and has been successfully deployed in a production-grade autonomous-driving simulation platform as a core digital twin kernel for large-scale autonomous driving validation. The code is publicly available at https://github.com/tsinghua-fib-lab/RAPID.
Zihan Yu, Huandong Wang, Jingtao Ding et al.· Proceedings of the 32nd ACM...· 0 citations
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