PhysX-CoT: Structured Physical Reasoning from a Single Image to Simulation-Ready 3D Assets

Simulation-ready 3D assets are central to robotics and embodied AI. Generating them from a single image is usually framed as a vision-language model that emits a serialized asset for a decoder to turn into geometry and physical fields, leaving the image-to-3D reasoning implicit. We argue the limiting factor is this output-centric view: part placement and local shape are entangled in one global-coordinate token stream, and the intermediate physical states are never exposed for supervision, conditioning, or verification. PhysX-CoT instead casts single-image asset generation as an explicit structured physical reasoning process, an ordered and machine-parseable trajectory of part-level states covering decomposition, 2D and 3D grounding, relations, coarse geometry, and surface cues that we separately supervise, use to condition geometry, and treat as reward targets. Geometry is factorized so that 3D boxes carry placement and local codes carry shape, and CoT-aligned GRPO optimizes parse validity, grounding, geometry, placement, and physical consistency. Under a unified protocol that retrains all learned baselines on the same backbone, data, and frozen decoder, PhysX-CoT outperforms the closest full-task baseline across geometry, scale, and physical-attribute metrics. Oracle, token-matched, and state-order controls show the explicit states are functional rather than cosmetic, and in Unreal Engine~5 the generated assets parse, collide, and articulate at high validity.

Lingjing: A Simulation Testbed for Multi-Agent Embodied Tasks in Open-Ended Cities

Urban embodied intelligence requires coordination among heterogeneous agents (e.g., UAVs, ground robots, and autonomous vehicles) in dynamic cities. Simulators therefore provide a scalable foundation for developing and evaluating such coordination. Existing platforms nevertheless isolate different embodiments and decouple them from task design and evaluation. We present \textbf{Lingjing}, a simulation platform for heterogeneous multi-agent embodied intelligence in open-ended urban environments. Lingjing reconstructs and renders evolving cities from geographic data, synchronizes multiple physics engines, and exposes shared physical and structured urban state to agents. Its Gym-like interface supports user-defined ReAct agents and single- or multi-agent natural-language missions with configurable star or broadcast communication and resource constraints. Each episode becomes an attribution-ready replay that links agent trajectories and communication to relation-graph changes, resource consumption, and engine-based evaluations for systematic diagnosis. We evaluate twelve vision-language models on nine urban tasks under a shared engine-in-the-loop protocol. Controlled studies further examine communication, scalability, robustness, and failure provenance. Results expose persistent bottlenecks in grounding and long-horizon execution. They also show task-dependent coordination trade-offs and diminishing returns from added capacity, while heavier workloads further reduce success. Lingjing provides a unified testbed that enables reproducible end-to-end evaluation and systematic failure diagnosis in urban multi-agent embodied intelligence.

SARNet: A Spike-Aware consecutive validation Framework for Accurate Remaining Useful Life Prediction

Accurate prediction of remaining useful life (RUL) is essential to enhance system reliability and reduce maintenance risk. Yet many strong contemporary models are fragile around fault onset and opaque to engineers: short, high-energy spikes are smoothed away or misread, fixed thresholds blunt sensitivity, and physics-based explanations are scarce. To remedy this, we introduce SARNet (Spike-Aware Consecutive Validation Framework), which builds on a Modern Temporal Convolutional Network (ModernTCN) and adds spike-aware detection to provide physics-informed interpretability. ModernTCN forecasts degradation-sensitive indicators; an adaptive consecutive threshold validates true spikes while suppressing noise. Failure-prone segments then receive targeted feature engineering (spectral slopes, statistical derivatives, energy ratios), and the final RUL is produced by a stacked RF--LGBM regressor. Across benchmark-ported datasets under an event-triggered protocol, SARNet consistently lowers error compared to recent baselines (RMSE 0.0365, MAE 0.0204) while remaining lightweight, robust, and easy to deploy.