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20242026
most citedC-TRAIL: A Commonsense World Framework for Trajectory Planning in Autonomous Driving

2 citations · 2 across the 16 of their papers we have counts for

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12 papers · 1 filter

cs.CR2026

Risk-aware Road-network Trajectory Synthesis under Local Differential Privacy

Hongzhen Xiang, Chenxu Wang, Yumeng Song +5

Road-network trajectory synthesis under local differential privacy (LDP) enables mobility data analysis without uploading raw trajectories to an untrusted server. Existing locally…

cs.AI2026

Robustness Analysis of Agentic AI to Inconsistent and Incomplete Tool Responses

Jiachen Xu, Torben Bach Pedersen, Zhongming Yao +2

Tool-using agents increasingly rely on external tools to complete multi-step tasks, but tool returns can fail in different ways and require different recovery actions. Existing rob…

cs.DB2026

VTRQ: Enabling Verifiable Trajectory Range Queries in Hybrid-Storage Blockchains

Zhongming Yao, Junchang Xin, Yumeng Song +7

Due to their increasingly large volumes, outsourcing of trajectory storage and querying to third-party service providers has become attractive. However, in such outsourced environm…

cs.RO2026

GAPL: Grounded Action-effect Policy Learning for LLM-Based Trajectory Planning

Zhihong Cui, Hengyu Liu, Zhangkai Wu +5

Trajectory planning for autonomous driving requires both high-level reasoning and precise low-level control. Large Language Models (LLMs) offer semantic-rich planning capabilities,…

cs.DC2026

DepTGL: A Parallel Framework for Memory-based TGNN Training with Adaptive Temporal Data Dependency Management

Linfang Chen, Zhen Song, Lei Liu +6

Memory-based Temporal Graph Neural Networks (M-TGNNs) maintain recursively updated node states to capture fine-grained temporal interactions. However, existing distributed framewor…

cs.AI2026

Reliable AI Needs to Externalize Implicit Knowledge: A Human-AI Collaboration Perspective

Hengyu Liu, Tianyi Li, Zhihong Cui +5

This position paper argues that reliable AI requires infrastructure for human validation of implicit knowledge. AI learns from both explicit knowledge (papers, documentation, struc…

cs.DB2026

AegisTS: A Hierarchical Agentic AI System with Reinforcement Learning for Multivariate Time Series Data Cleaning

Yuhan Shi, Yuanyuan Yao, Lu Chen +3

Multivariate time series (MTS) are frequently affected by co-occurring quality issues, such as missing values, outliers, and constraint violations, which significantly undermine do…

cs.AI2026★ 2 cited

C-TRAIL: A Commonsense World Framework for Trajectory Planning in Autonomous Driving

Zhihong Cui, Haoran Tang, Tianyi Li +4

Trajectory planning for autonomous driving increasingly leverages large language models (LLMs) for commonsense reasoning, yet LLM outputs are inherently unreliable, posing risks in…

cs.DB2026

CLEAR: A Knowledge-Centric Vessel Trajectory Analysis Platform

Hengyu Liu, Tianyi Li, Haoyu Wang +5

Vessel trajectory data from the Automatic Identification System (AIS) is used widely in maritime analytics. Yet, analysis is difficult for non-expert users due to the incompletenes…

cs.LG2026

FEAT: A Linear-Complexity Foundation Model for Extremely Large Structured Data

Zhenghang Song, Tang Qian, Lu Chen +7

Structured data is widely used in domains such as healthcare, finance, and scientific data management. Recent studies on structured data foundation models (SFMs) aim to support dat…

cs.DB2026

VISTA: Knowledge-Driven Vessel Trajectory Imputation with Repair Provenance

Hengyu Liu, Tianyi Li, Haoyu Wang +4

Repairing incomplete trajectory data is essential for downstream spatio-temporal applications. Yet, existing repair methods focus solely on reconstruction without documenting the r…

cs.LG2026

TCRL: Temporal-Coupled Adversarial Training for Robust Constrained Reinforcement Learning in Worst-Case Scenarios

Wentao Xu, Zhongming Yao, Weihao Li +4

Constrained Reinforcement Learning (CRL) aims to optimize decision-making policies under constraint conditions, making it highly applicable to safety-critical domains such as auton…