arXiv subjects

Yuzhi Chen

Publications and source records attributed to Yuzhi Chen.

10 recordsLinked to original sources

arXiv2026

Superconducting gap in covalent bismuth dihydride BiH$_2$ under extreme conditions

Hydride superconductors at megabar pressures provide a promising platform for exploring room-temperature superconductivity. However, their superconducting gaps remain largely inaccessible to conventional spectroscopic due to diamond anvil cell constraints and minute sample dimensions. Here we develop a pulsed current method and apply it to covalent BiH$_2$ synthesized at 157--176 GPa. BiH$_2$ exhibits superconductivity at 58--70 K and upper critical fields of 11--17 T, substantially lower than those of many clathrate superhydrides, corresponding to a relatively long coherence length and an experimentally accessible critical current density. Short rectangular pulses minimize sustained Joule heating and enable currents up to 160 mA, allowing $J_c(T)$ to be measured in the low-temperature regime down to 2 K at 176 GPa. The normalized critical-current response remains reproducible between two measurement runs and is better described by a two-scale $s$-wave model than by single-gap $s$- or $d$-wave models, yielding effective energy scales of approximately 6.9 and 1.5 meV. Fully anisotropic Migdal--Eliashberg calculations yield a single highly anisotropic gap, suggesting that the two-gap behavior observed experimentally originates from gap anisotropy rather than two independent gaps. These results establish pulsed critical-current measurements as a practical gap-sensitive transport probe under extreme pressure and, with further increases in peak-current capability, provide a route toward investigating room-temperature hydrides such as La--Sc--H.

cond-mat.supr-con↗

arXiv2026

ABot-M0.5: Unified Mobility-and-Manipulation World Action Model

Mobile manipulation is a key capability for general-purpose robots, yet remains challenging for current embodied learning methods. VLA policies are typically reactive and lack explicit world modeling, while existing World Action Models (WAMs) are still poorly aligned with the structure of mobile manipulation: they operate on coarse video chunks, model entangled navigation-manipulation actions, and train inverse dynamics under supervision that does not match autoregressive inference. As a result, they often miss fine-grained contact dynamics, suffer from action-distribution conflicts, and accumulate errors over long-horizon rollouts. We propose ABot-M0.5, a new WAM built on the insight that mobile manipulation requires alignment at three levels: temporal granularity, action space, and train-test consistency. To align temporal granularity, we introduce intermediate latent actions that capture local visual state transitions and serve as an bridging action space between video latents and embodiment-specific controls. To align action space, we design a dual-level Mixture-of-Transformers architecture that disentangles both modality representations and heterogeneous action subspaces such as base movement and arm manipulation. To align inference conditions, we propose the dream-forcing training strategy that progressively trains inverse dynamics on model-predicted videos, improving train-test alignment and robustness during autoregressive prediction. Experiments on challenging mobile and fine-grained manipulation benchmarks demonstrate that ABot-M0.5 achieves state-of-the-art performance in both long-horizon task success and finegrained control accuracy. These results highlight the critical importance of granularity-aligned, action-disentangled, and inference-consistent world-action modeling.

cs.CV↗

arXiv2026

ABot-M0: VLA Foundation Model for Robotic Manipulation with Action Manifold Learning

Building general-purpose embodied agents across diverse hardware remains a central challenge in robotics, often framed as the ''one-brain, many-forms'' paradigm. Progress is hindered by fragmented data, inconsistent representations, and misaligned training objectives. We present ABot-M0, a framework that builds a systematic data curation pipeline while jointly optimizing model architecture and training strategies, enabling end-to-end transformation of heterogeneous raw data into unified, efficient representations. From six public datasets, we clean, standardize, and balance samples to construct UniACT-dataset, a large-scale dataset with over 6 million trajectories and 9,500 hours of data, covering diverse robot morphologies and task scenarios. Unified pre-training improves knowledge transfer and generalization across platforms and tasks, supporting general-purpose embodied intelligence. To improve action prediction efficiency and stability, we propose the Action Manifold Hypothesis: effective robot actions lie not in the full high-dimensional space but on a low-dimensional, smooth manifold governed by physical laws and task constraints. Based on this, we introduce Action Manifold Learning (AML), which uses a DiT backbone to predict clean, continuous action sequences directly. This shifts learning from denoising to projection onto feasible manifolds, improving decoding speed and policy stability. ABot-M0 supports modular perception via a dual-stream mechanism that integrates VLM semantics with geometric priors and multi-view inputs from plug-and-play 3D modules such as VGGT and Qwen-Image-Edit, enhancing spatial understanding without modifying the backbone and mitigating standard VLM limitations in 3D reasoning. Experiments show components operate independently with additive benefits. We will release all code and pipelines for reproducibility and future research.

cs.CV↗

arXiv2026

ABot-PhysWorld: Interactive World Foundation Model for Robotic Manipulation with Physics Alignment

Video-based world models offer a powerful paradigm for embodied simulation and planning, yet state-of-the-art models often generate physically implausible manipulations - such as object penetration and anti-gravity motion - due to training on generic visual data and likelihood-based objectives that ignore physical laws. We present ABot-PhysWorld, a 14B Diffusion Transformer model that generates visually realistic, physically plausible, and action-controllable videos. Built on a curated dataset of three million manipulation clips with physics-aware annotation, it uses a novel DPO-based post-training framework with decoupled discriminators to suppress unphysical behaviors while preserving visual quality. A parallel context block enables precise spatial action injection for cross-embodiment control. To better evaluate generalization, we introduce EZSbench, the first training-independent embodied zero-shot benchmark combining real and synthetic unseen robot-task-scene combinations. It employs a decoupled protocol to separately assess physical realism and action alignment. ABot-PhysWorld achieves new state-of-the-art performance on PBench and EZSbench, surpassing Veo 3.1 and Sora v2 Pro in physical plausibility and trajectory consistency. We will release EZSbench to promote standardized evaluation in embodied video generation.

cs.CV↗

arXiv2025

GContextFormer: A global context-aware hybrid multi-head attention approach with scaled additive aggregation for multimodal trajectory prediction

Multimodal trajectory prediction generates multiple plausible future trajectories to address vehicle motion uncertainty from intention ambiguity and execution variability. However, HD map-dependent models suffer from costly data acquisition, delayed updates, and vulnerability to corrupted inputs, causing prediction failures. Map-free approaches lack global context, with pairwise attention over-amplifying straight patterns while suppressing transitional patterns, resulting in motion-intention misalignment. This paper proposes GContextFormer, a plug-and-play encoder-decoder architecture with global context-aware hybrid attention and scaled additive aggregation achieving intention-aligned multimodal prediction without map reliance. The Motion-Aware Encoder builds scene-level intention prior via bounded scaled additive aggregation over mode-embedded trajectory tokens and refines per-mode representations under shared global context, mitigating inter-mode suppression and promoting intention alignment. The Hierarchical Interaction Decoder decomposes social reasoning into dual-pathway cross-attention: a standard pathway ensures uniform geometric coverage over agent-mode pairs while a neighbor-context-enhanced pathway emphasizes salient interactions, with gating module mediating their contributions to maintain coverage-focus balance. Experiments on eight highway-ramp scenarios from TOD-VT dataset show GContextFormer outperforms state-of-the-art baselines. Compared to existing transformer models, GContextFormer achieves greater robustness and concentrated improvements in high-curvature and transition zones via spatial distributions. Interpretability is achieved through motion mode distinctions and neighbor context modulation exposing reasoning attribution. The modular architecture supports extensibility toward cross-domain multimodal reasoning tasks. Source: https://fenghy-chen.github.io/sources/.

cs.AI↗

arXiv2025

Revealing superconducting gap in La$_3$Ni$_2$O$_7$-$δ$ by Andreev reflection spectroscopy under high pressure

The recent discovery of compressed superconductivity at 80~K in La$_3$Ni$_2$O$_7$-$δ$ has brought nickelates into the family of unconventional high-temperature superconductors. However, due to the challenges of directly probing the superconducting pairing mechanism under high pressure, the pairing symmetry and gap structures of nickelate superconductors remain under intense debate. In this work, we successfully determine the microscopic information on the superconducting gap structure of La$_3$Ni$_2$O$_7$-$δ$ samples subjected to pressures exceeding 20~GPa, by constructing different conductance junctions within diamond anvil cells. By analyzing the temperature-dependent differential conductance spectra within the Blonder--Tinkham--Klapwijk (BTK) model, we have determined the superconducting energy gap at high pressure. The differential conductance curves reveal a two-gap structure with $Δ_{1} = 23~\mathrm{meV}$ and $Δ_{2} = 6~\mathrm{meV}$, while the BTK fitting is consistent with an $s$-like, two-gap spectrum. The gap ratio $2Δ_{s1}(0) / k_{\mathrm{B}}T_{c}$ is found to be 7.61, belonging to a family of strongly coupled superconductors. Our findings provide valuable insights into the superconducting gap structures of the pressure-induced superconducting nickelates.

cond-mat.supr-con↗

arXiv2025

Evidence for the Meissner effect in the nickelate superconductor La3Ni2O7-delta single crystal using diamond quantum sensors

Quantum sensing with nitrogen-vacancy (NV) centers in diamond enables the characterization of magnetic properties in the extreme situation of tiny sample with defects. Recent studies have reported superconductivity in La3Ni2O7-delta under pressure, with zero-resistance near 80 K, though the Meissner effect remains debated due to low superconducting volume fractions and limited high-pressure magnetic measurement techniques. In this work, we use diamond quantum sensors and four-probe detection to observe both zero resistance and the Meissner effect in the same La3Ni2O7-delta single crystal. By mapping the Meissner effect, we visualized superconducting regions and revealed sample inhomogeneities. Our combined magnetic and electrical measurements on the same crystal provide dual evidence of superconductivity, supporting the high-temperature superconductivity of La3Ni2O7-delta. This study also offers insights into its structural and magnetic properties under high pressure.

cond-mat.supr-con↗

arXiv2019

Real-time measurement with a fiber optical surface plasmon resonance sensor for biochemical interaction analysis

In this paper we report a fiber optical sensor system based on surface plasmon resonance (SPR) with real-time response for biochemical interaction analysis. The fiber sensor is constructed from a multi-mode fiber with plastic cladding. To facilitate the measurement, a software program is developed which integrates the data acquisition and processing for real-time feedback. Polynomial fitting is implemented to smooth out the noise in the transmission ratio and a spectral resolution of 0.2 nm is achieved. Ethyl alcohol and water mixtures with different concentrations are measured to demonstrate the system's real-time capability. This work is essential for the development of a compact, real-time fiber SPR biosensor.

physics.app-ph↗

arXiv2018

Improving the Sensitivity of Fiber Surface Plasmon Resonance Sensor by Filling Liquid in a Hollow Core Photonic Crystal Fiber

Inspired by the classic theory, we suggest that the performance of a D-shaped fiber optical surface plasmon resonance (SPR) sensor can be improved by manipulating the fiber core mode. To demonstrate this, we propose a novel fiber SPR sensor based on a hollow core photonic crystal fiber with liquid mixture filled in the core. The fiber sensor design involves a side-polished fiber with gold film deposited on the polished plane and liquid filling. Numerical simulation results suggest that by tuning the refractive index of the liquid mixture, the predicted sensitivity will be over 6,430 nm/refractive index unit for an aqueous environment, which is competitive for fiber chemical sensing. This optimization method may lead to an ultrahigh sensitivityfiber optical biosensor.

physics.app-ph↗

arXiv2015

Angular characteristics of a multimode fiber surface plasmon resonance sensor under wavelength interrogation

In this paper the angular characteristics of a multimode fiber SPR sensor are theoretically investigated. By separating the contributions of beams incident at different angles, a compact model is presented to predict the shift of the resonance wavelength with respect to the angle and the environmental refractive index. The result suggests that the performance of conventional fiber SPR sensors can be substantially improved by optimizing the incident angle. Furthermore, our investigation suggests some problems in previous reports.

physics.optics↗