Wilson Wongso, Lihuan Li, Arian Prabowo, Xiachong Lin, Baiyu Chen, Hao Xue, Flora D. Salim
Generating high-fidelity synthetic Web of Things (WoT) telemetry, such as GPS trajectories, is increasingly important for transportation, urban planning, and location-aware services, particularly when privacy concerns restrict access to real-world mobility data. Existing trajectory generation methods face a trade-off between efficiency and road network fidelity: continuous-space methods generate trajectories efficiently but ignore road topology, while topology-aware methods rely on search-based autoregressive decoding that limits generation speed. We propose TrajDLM, a topology-aware trajectory generation framework based on block diffusion language models. TrajDLM represents trajectories as sequences of discrete road segments, combining a block diffusion backbone, graph-based road network representations, and topology-constrained sampling to generate realistic trajectories with topologically valid road transitions. To address the tendency of supervised models to favor near-shortest routes, we introduce TrajRFT, a rejection-sampling fine-tuning stage that improves route fidelity by training on self-generated routes selected for their similarity to real trajectories. Across three city-scale vehicle telemetry datasets, TrajDLM closely matches real-world mobility in population-level distributions, local route similarity, and route diversity, while generating trajectories up to $7.5\times$ faster than the strongest baseline. It also demonstrates zero-shot transfer to unseen transport modes. These results establish block diffusion with topology-constrained sampling and rejection-sampling fine-tuning as an efficient approach to generating realistic synthetic mobility telemetry for location-aware Web services and WoT applications.
Jiayi Li, Penghao Jiang, Baiyu Chen, Yifan Duan, Hira Saleem, Zhaonan Wang, Piotr Koniusz, Flora D. Salim
Neural operators efficiently simulate dynamical systems by repeatedly predicting the next state. However, small errors at each step can accumulate, causing long-term predictions to drift from the true trajectory. Long-term error reflects both the propagation of earlier prediction errors and new errors introduced at each step. How to reduce these errors is therefore a critical question. We propose the Spectral Generator Neural Operator (SGNO), which combines a linear estimate of the next state with a learned correction. The linear component uses consecutive states in the training data to fit a map from the current state to the next. A neural network corrects this prediction by learning changes that the linear model does not capture, including nonlinear interactions. Both components are trained jointly toward the same next-state target. Experiments across 13 periodic PDE configurations in one to three dimensions show that SGNO accurately learns state evolution on linear tasks and reduces 100-step rollout error by 13-74% over the strongest baseline under the main comparison protocol on each nonlinear task. Longer-horizon extrapolation tests show lower late-window prediction errors on several nonlinear tasks. These results show that combining fitted propagation with learned state-dependent correction can improve long-horizon autoregressive prediction on the evaluated PDE tasks. Code is available at https://github.com/cruiseresearchgroup/SGNO.
Zechen Li Google, Baiyu Chen University of New South Wales, Hao Xue The Hong Kong University of Science and Technology, Flora D. Salim
Motion sensor time-series are central to Human Activity Recognition (HAR), yet conventional approaches are constrained to fixed activity sets and typically require costly parameter retraining to adapt to new behaviors. While Large Language Models (LLMs) offer promising open-set reasoning capabilities, applying them directly to numerical time-series often leads to hallucinations and weak grounding. To address this challenge, we propose ZARA (Zero-training Activity Reasoning Agents), a knowledge- and retrieval-augmented agentic framework for motion time-series reasoning in a training-free inference setting. Rather than relying on black-box projections, ZARA distills reference data into a statistically grounded textual knowledge base that transforms implicit signal patterns into verifiable natural-language priors. Guided by retrieved evidence, ZARA iteratively selects discriminative cues and performs grounded reasoning over candidate activities. Extensive experiments on eight benchmarks show that ZARA generalizes robustly to unseen subjects and across datasets, demonstrating strong transferability across heterogeneous sensor domains. These results mark a step toward trustworthy, plug-and-play motion understanding beyond dataset-specific artifacts. Our code is available at https://github.com/zechenli03/ZARA.
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cs.CL · cs.AI
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表