arXiv subjects

Xinqi Zhang

Publications and source records attributed to Xinqi Zhang.

8 recordsLinked to original sources

arXiv2026

NormWorlds-CF: Solver-Verified Counterfactual Normative Reasoning with Metamorphic-Relation GRPO

Language models can reach the right normative verdict for the wrong reason. We introduce NormWorlds-CF, a solver-verified environment for counterfactual normative reasoning in executable rule worlds. Its deterministic solver produces final answers, proof and falsification certificates, argument statuses, support sets, and paired-world change labels, enabling supervision and evaluation without LLM judges. The benchmark contains staged SFT diagnostics and a compact paired-world task with 270 root families and 1080 canonical-to-variant pairs. The SFT diagnostics show that final-answer supervision can saturate verdict accuracy without inducing falsification competence: answer-only SFT reaches perfect answer accuracy but scores zero on joint falsification certificates, while full-mix training with targeted replay reaches strong all-task accuracy (0.99). For the structured-change task, we introduce metamorphic-relation GRPO (MR-GRPO), a class-conditioned reward for GRPO that gives partial credit for relation families and solver-visible change fields. In matched Qwen3-1.7B continuation experiments, MR-GRPO improves held-out relation accuracy and relation-family correctness, and reduces wrong-family error, compared to sparse and answer-only GRPO. In Qwen3-4B three-seed validation, sparse reward preserves coarse relation labels best, answer-only reward improves answer-change but weakens relation-family structure, and MR-GRPO leads on answer-, support-, and status-change fields as well as class-conditioned MR and change-presence. These results show that verified counterfactual structure can shape post-training beyond final answers, while exact full change-record generation, invariant subtype recognition, and out-of-distribution (OOD) transfer remain open problems.

cs.CL↗

arXiv2026

ASTAD: Asymmetric Style Transfer for Synthetic-to-Real Adaptation in Autonomous Driving

Synthetic data mitigates the data scarcity problem in autonomous driving perception. However, the synthetic-to-real gap leads to performance degradation, hindering real-world model generalization. Although current methods leverage diffusion models for photorealistic style transfer to bridge this gap, they critically ignore a practical asymmetry: while synthetic data possesses perfect pixel-level annotations, real-world style reference images generally lack corresponding labels. Consequently, existing methods relying on symmetric semantic guidance suffer from either prohibitive annotation costs or severe semantic misalignment. To address this dilemma, we formally propose a novel task: Asymmetric Style Transfer for Autonomous Driving (ASTAD), which requires semantically consistent transfer using only labeled synthetic content and unlabeled real-world references. We further introduce the ASTModel, a training-free two-stage framework designed to bridge this domain gap under asymmetric constraints. ASTModel first extracts a coarse semantic prior from the unlabeled target, followed by dynamic prior refinement and class-consistent style injection during the denoising process. Extensive experiments demonstrate that ASTModel significantly outperforms existing methods in downstream perception utility and structural fidelity, while offering a 3.2$\times$ inference speedup. This work aligns synthetic-to-real adaptation with practical constraints, holding the potential to accelerate the scalable deployment of robust autonomous driving systems. Code: https://github.com/Dingyi-Yao/ASTAD.

cs.CV↗

arXiv2026

An Electric Potential-Augmented Benchmark Dataset for Physics-Guided Image Reconstruction of Electrical Capacitance Tomography

While deep learning has significantly advanced image reconstruction of Electrical Capacitance Tomography (ECT), most data-driven methods map directly between capacitance and permittivity distribution, treating the sensor as a black box. This overlooks the electric potential field -- the fundamental physical link governing the nonlinear and ill-posed ``soft-field'' effect. To address this, we propose an electric potential-augmented ECT benchmark dataset designed to explicitly integrate latent physics behind ECT into the learning process. Generated via a COMSOL-MATLAB pipeline for an eight-electrode sensor as an example, the dataset comprises 20,000 randomized samples across four typical flow patterns. Crucially, alongside the conventional capacitance vectors and permittivity distributions depicted as images, each sample preserves eight excitation-wise full-field potential maps. Beyond data release, we provide illustrative evaluation protocols for both forward and inverse problems of ECT. Through comprehensive testing on both in-distribution (IID) and out-of-distribution (OOD) scenarios, we systematically demonstrate how the inclusion of electric potential maps enhances modeling accuracy and robustness. Fundamentally, the explicit inclusion of latent field information significantly lowers the barrier to integrating physical laws into ECT modeling, thereby establishing a standardized foundation for future physics-guided machine learning of ECT image reconstruction.

cs.CV↗

arXiv2025

Whole-Body Proprioceptive Morphing: A Modular Soft Gripper for Robust Cross-Scale Grasping

Biological systems, such as the octopus, exhibit masterful cross-scale manipulation by adaptively reconfiguring their entire form, a capability that remains elusive in robotics. Conventional soft grippers, while compliant, are mostly constrained by a fixed global morphology, and prior shape-morphing efforts have been largely confined to localized deformations, failing to replicate this biological dexterity. Inspired by this natural exemplar, we introduce the paradigm of collaborative, whole-body proprioceptive morphing, realized in a modular soft gripper architecture. Our design is a distributed network of modular self-sensing pneumatic actuators that enables the gripper to intelligently reconfigure its entire topology, achieving multiple morphing states that are controllable to form diverse polygonal shapes. By integrating rich proprioceptive feedback from embedded sensors, our system can seamlessly transition from a precise pinch to a large envelope grasp. We experimentally demonstrate that this approach expands the grasping envelope and enhances generalization across diverse object geometries (standard and irregular) and scales (up to 10$\times$), while also unlocking novel manipulation modalities such as multi-object and internal hook grasping. This work presents a low-cost, easy-to-fabricate, and scalable framework that fuses distributed actuation with integrated sensing, offering a new pathway toward achieving biological levels of dexterity in robotic manipulation.

cs.RO↗

arXiv2025

MVVM: Deploy Your AI Agents-Securely, Efficiently, Everywhere

The rise of AI agents powered by Large Language Models (LLMs) presents critical challenges: how to securely execute and migrate these agents across heterogeneous environments while protecting sensitive user data, maintaining availability during network failures, minimizing response latency for time-critical decisions, and ensuring output safety in mission-critical applications. We present MVVM, a WebAssembly-based secure container framework that enables transparent live migration of LLM agent workspaces between edge devices and cloud servers with end-to-end privacy guarantees, resilient multi-tier replication, speculative execution for latency optimization, and integrated validation for safety assurance. MVVM introduces two key innovations: (1) a two-way sandboxing framework leveraging hardware enclaves and accelerator extensions that protects both the agent from malicious hosts and the host from compromised agents; (2) an efficient cross platform migration mechanism using WebAssembly and WASI's platform-agnostic design, enabling seamless movement across ARM phones, RISC-V MCUs, x86 servers, and heterogeneous accelerators; and three astonishing use cases: (1) privacy-aware daemon that automatically determines whether to execute locally or remotely based on data sensitivity and resource availability; (2) multi-tier replication with intelligent quality degradation that maintains service availability despite network failures or resource constraints; (3) a comprehensive execution framework combining speculative execution for 10x latency reduction with parallel validation that ensures output safety without compromising responsiveness. Our evaluation demonstrates that MVVM is validated on three separate devices across 18 workloads.

cs.OS↗

arXiv2025

Community Empowerment through Location-Based AR: The Thámien Ohlone AR Tour

Community empowerment is the process of enabling communities to increase control over their narratives, resources, and futures. In HCI and design, this social challenge centers on helping marginalized groups gain agency through technology and design interventions. For Indigenous communities in particular, empowerment means not only representation but sovereignty in how their stories are told and by whom. Location-based augmented reality (AR) offers a novel opportunity to address this challenge. By overlaying digital content onto physical places, AR can spatially anchor community narratives in the real world, allowing communities to re-tell the story of a place on their own terms. Such site-specific AR experiences have already been used to reveal hidden histories, re-imagine colonial monuments, and celebrate minority cultures. The affordances of XR - particularly ARś spatial interaction and immersive storytelling - make it a promising tool for cultural continuity and community activism. In this position paper, we focus on how these XR affordances can empower communities, using the Thámien Ohlone AR Tour as a case study. We outline why traditional digital interventions fall short of true empowerment, how AR's immersive qualities uniquely support Indigenous self-determination, insights from co-designing the Ohlone AR Tour, and future directions to scale such efforts responsibly.

cs.HC↗

arXiv2024

Sociotechnical Considerations for SLAM Anchors in Location-Based AR

In this position paper, we explore the power of storytelling and its connection to place through the use of Augmented Reality (AR) technology, particularly within the context of Thámien Ohlone history on the Santa Clara University campus. To do this, we utilized SLAM and 8th Wall to create virtual, location-based experiences that geolocate tribal stories at present-day sites, showcase the living culture of the Thámien Ohlone tribe, and advocate for physical markers that could exist to recognize their story. When doing so, we made sure to select locations that added to the story each stop tells to serve as our anchors. Our research then investigates both the social and technical considerations involved in selecting anchors for AR experiences, using the Thámien Ohlone AR Tour as a case study.

cs.HC↗

arXiv2023

Hydrogen jet diffusion modeling by using physics-informed graph neural network and sparsely-distributed sensor data

Efficient modeling of jet diffusion during accidental release is critical for operation and maintenance management of hydrogen facilities. Deep learning has proven effective for concentration prediction in gas jet diffusion scenarios. Nonetheless, its reliance on extensive simulations as training data and its potential disregard for physical laws limit its applicability to unseen accidental scenarios. Recently, physics-informed neural networks (PINNs) have emerged to reconstruct spatial information by using data from sparsely-distributed sensors which are easily collected in real-world applications. However, prevailing approaches use the fully-connected neural network as the backbone without considering the spatial dependency of sensor data, which reduces the accuracy of concentration prediction. This study introduces the physics-informed graph deep learning approach (Physic_GNN) for efficient and accurate hydrogen jet diffusion prediction by using sparsely-distributed sensor data. Graph neural network (GNN) is used to model the spatial dependency of such sensor data by using graph nodes at which governing equations describing the physical law of hydrogen jet diffusion are immediately solved. The computed residuals are then applied to constrain the training process. Public experimental data of hydrogen jet is used to compare the accuracy and efficiency between our proposed approach Physic_GNN and state-of-the-art PINN. The results demonstrate our Physic_GNN exhibits higher accuracy and physical consistency of centerline concentration prediction given sparse concentration compared to PINN and more efficient compared to OpenFOAM. The proposed approach enables accurate and robust real-time spatial consequence reconstruction and underlying physical mechanisms analysis by using sparse sensor data.

cs.CE↗