arXiv Machine Learning

Using Lower-Bound Representations for Trajectory Similarity Learning

arXiv:2608. 01039v1 Announce Type: cross Abstract: Trajectory similarity learning is fundamental to efficient trajectory retrieval under complex distance measures.

arXiv AI

Jun 17

MoCo-AIS: A Contrastive Learning Framework for Similarity Computation of Vessel Trajectories

arXiv:2606. 17978v1 Announce Type: new Abstract: Trajectory similarity is a fundamental task in analyzing mobility patterns, essential for applications such as route pattern extraction, mobility prediction, and anomaly detection.

By Ruixin Song, Md Mahbub Alam, Zahra Sadeghi, Amilcar Soares, Jos\'e F. Rodrigues-Jr, Gabriel Spadon

rag benchmarks

arXiv Machine Learning

5d ago

TraVEL: Trajectory-Guided Video Embedding Learning for Driving-Video Retrieval

arXiv:2608. 13495v1 Announce Type: cross Abstract: Efficiently retrieving relevant clips from large-scale driving logs is essential for data curation, model development, and safety analysis.

By Yi-Chung Chen, Philip Jacobson, Tom Lampo, Yiren Lu, Jin Yao, David I. Inouye, Jing Gao, Danhua Guo, Burhan Yaman

rag fine-tuning multimodal benchmarks safety

arXiv Machine Learning

Jul 1

Capturing Context-Aware Route Choice Semantics for Trajectory Representation Learning

arXiv:2510. 14819v3 Announce Type: replace-cross Abstract: Trajectory representation learning (TRL) aims to encode raw trajectory data into low-dimensional embeddings for downstream tasks such as travel time estimation, mobility prediction, and trajectory similarity analysis.

By Ji Cao, Yu Wang, Tongya Zheng, Jie Song, Qinghong Guo, Zujie Ren, Canghong Jin, Gang Chen, Mingli Song

llms rag benchmarks

arXiv Machine Learning

Jun 25

HiT-JEPA: A Hierarchical Self-supervised Trajectory Embedding Framework for Similarity Computation

arXiv:2507. 00028v2 Announce Type: replace Abstract: The representation of urban trajectory data plays a critical role in effectively analyzing spatial movement patterns.

By Lihuan Li, Hao Xue, Shuang Ao, Yang Song, Flora Salim

rag

arXiv AI

Jul 7

Unsupervised Behavioral Compression: Learning Low-Dimensional Policy Manifolds through State-Occupancy Matching

arXiv:2603. 27044v3 Announce Type: replace-cross Abstract: Deep Reinforcement Learning (DRL) is widely recognized as sample-inefficient, a limitation attributable in part to the high dimensionality and substantial functional redundancy inherent to the policy parameter space.

By Andrea Fraschini, Davide Tenedini, Riccardo Zamboni, Mirco Mutti, Marcello Restelli

diffusion reinforcement-learning benchmarks

arXiv Machine Learning

Jul 13

Multimodal Scenario Similarity Search for Autonomous Driving

arXiv:2607. 09428v1 Announce Type: cross Abstract: Large-scale autonomous-driving datasets contain vast numbers of recorded scenarios, creating a need for efficient retrieval methods that can identify situations similar to a given query.

By Tam\'as Matuszka, Andr\'as Tam\'asy, Bal\'azs Szol\'ar

llms rag agents robotics multimodal

arXiv AI

Jun 16

Beyond Scalar Distances: Semantic Attribute Gradients from Frozen MLLMs for Visual Embeddings

arXiv:2606. 15134v1 Announce Type: cross Abstract: Vision encoders for retrieval are typically trained with class-label supervision: each training pair reduces to a scalar that uniformly pushes the embedding apart or pulls it together, as if every visual attribute either differed or matched.

By Shubhang Bhatnagar, Dheeraj Baiju, Narendra Ahuja

llms rag efficiency multimodal benchmarks

arXiv AI

2d ago

Traj-LeWM: Path-Aware World-Model Planning via Latent Trajectory Cost

arXiv:2608. 14125v1 Announce Type: new Abstract: LeWM is a lightweight visual world model that learns latent dynamics end-to-end from pixels and ranks candidate action sequences by the distance between their predicted endpoints and the goal.

By Xiaodi Huang, Ziyi Ding, Jingtian Wan, Yuchen Liu, Yuan Zhang, Xiao-Ping Zhang, Jiayu Chen, Zhang Zhang, Tao Huang

arXiv AI

Jul 17

Latent Trajectory Discrimination for AI-Generated Text Detection

arXiv:2607. 14967v1 Announce Type: cross Abstract: Most existing approaches to AI-Generated Text Detection (AIGTD) treat documents as static objects and base their decisions on aggregate statistics or globally compressed embeddings.

By Gianluca Bonifazi, Christopher Buratti, Michele Marchetti, Federica Parlapiano, Giulia Quaglieri, Davide Traini, Domenico Ursino, Luca Virgili

rag diffusion benchmarks

arXiv Machine Learning

Jul 9

From Jumps to Signatures: a Generative Method for Temporal Point Processes

arXiv:2607. 06652v1 Announce Type: new Abstract: Rough path signatures are a universal feature map for continuous paths and, via the expected signature, characterise path distributions.

By Niels Cariou-Kotlarek, Vasileios Lampos

rag

arXiv Machine Learning

Jul 14

Distance-Preserving Embeddings in Inhomogeneous Random Graphs

arXiv:2607. 10074v1 Announce Type: new Abstract: Graph machine learning provides powerful tools for understanding complex networks and learning meaningful node representations.

By My Le, Luana Ruiz, Souvik Dhara

rag safety

arXiv AI

Aug 5

UniNav: A Unified World-Action Diffusion Model for Visual Navigation

arXiv:2608. 03244v1 Announce Type: new Abstract: Image-goal visual navigation is a fundamental capability for embodied agents.

By Changqing Zhou, Yueru Luo, Zeyu Jiang, Changhao Chen

llms agents diffusion robotics benchmarks

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