The paper introduces a new resilience evaluation framework for Embodied Agents Systems (EAS), defining metrics for Rebound, Stability, and Graceful Extensibility. It demonstrates that traditional outcome-centric metrics obscure dynamic behaviors, while the proposed metrics reveal process-level distinctions such as recovery costs and instability across 400 household tasks with 10 EAS. Optimizing with these metrics reduces recovery costs and improves stability and extensibility, highlighting a trade‑off among resilience characteristics that should guide deployment-specific system design.
By Yapeng Liu, Yuanzhao Zhai, Xudong Gong, Dawei Feng, Bo Ding, Lin Wang, Huaimin Wang
The paper introduces a resilience recovery method for complex traffic networks that relies on trend forecasting. It develops a SIRD‑R fault propagation model incorporating risk values and constructs a resilience model combining real‑time and overall resilience. Using LSTM networks, the method forecasts network resilience and proposes a recovery strategy, with experimental results confirming its effectiveness and scalability across diverse traffic networks.
By Sheng Hong, Tianyu Yue, Yang You, Zhengnan Lv, Xu Tang, Jing Hu, Hongwei Yin
arXiv:2606. 07044v1 Announce Type: new Abstract: Accurate and coherent passenger demand forecasting is essential for Urban Rail Transit (URT) operations.
By Dang Viet Anh Nguyen, Alma Fazlagic, Kristine Pryds Loft, Filipe Rodrigues
The paper evaluates three approaches—LLM-Z3 full replanning, IPyHOPPER hierarchical repair, and iTIMO local-revision—for revising travel itineraries after disruptions such as flight cancellations or hotel unavailability. Using two TREK-derived benchmark sets (500 single-disruption cases and 200 compound-disruption cases), the study compares effectiveness, plan stability, and computational cost. Results show LLM-Z3 with Gemini achieves the highest compound-disruption success, IPyHOPPER nearly matches single-disruption success while preserving more of the original itinerary, and iTIMO makes fewer edits but consumes more tokens, offering practical guidelines for balancing feasibility recovery, commitment preservation, and computational cost.
By Xiaofei Yuan, Yan Zhang, Shaobo Qiao, Huangleshuai He, Leyan Ni, Mingchen Ju, Lujia Yang, Sijia Xu, Yifu Tang, Zhengyi Yang
PRISM (Proactive Risk Intelligence and Safety Management) is an agentic multi-model architecture designed to shift autonomous transportation safety from reactive crash avoidance to proactive, continuous risk management. It uses inverse crash‑probability modeling to transform binary crash classifiers into dynamic safety scores, and runs three specialized models—trajectory kinematics, environmental risk, and VRU interaction—coordinated by a reinforcement‑learning reasoning layer. Across 1,296 naturalistic driving scenarios, PRISM achieved a mean safety score of 68/100, classified 77.6% of situations as advisory, and flagged 3.8% as near‑misses, with 11% requiring intervention or emergency response, highlighting trajectory risk and VRU proximity as key safety factors.
By Joyjit Roy, Samaresh Kumar Singh, Sushanta Das
arXiv:2608. 08184v1 Announce Type: new Abstract: Large multimodal agents (LMAs) are increasingly proposed for intelligent transportation systems (ITS), but existing studies often conflate multimodality, agency, empirical performance, and deployment readiness.
By Muhammad Ayub Sabir, Shaohong Zheng, Zhiyu Qu, Fatima Ashraf, Junbiao Pang