arXiv subjects

Zhijie Xu

Publications and source records attributed to Zhijie Xu.

At least 19 recordsLinked to original sources

arXiv2026

SAFARI: An Industrial Benchmark for LLM-Assisted Hazard Analysis and Risk Assessment

Large language models (LLMs) are increasingly considered for safety-critical engineering, yet their reliability in regulated functional-safety workflows remains underexplored. We introduce SAFARI (Safety-Aware Functional Automotive Risk Inference), the first industrial benchmark for LLM-assisted automotive Hazard Analysis and Risk Assessment (HARA) under ISO 26262. It contains 3,000 de-identified industrial HARA cases and evaluates two coupled tasks: open-ended hazard analysis and standards-grounded risk assessment. To evaluate open-ended HARA artifacts, we propose the first reference-anchored LLM-as-a-judge protocol with high expert correlation. Experiments with nine frontier LLMs show that models often produce plausible hazard narratives but remain weak at ISO 26262 risk classification, with the best ASIL macro-F1 reaching only 0.261. Chain-of-Thought prompting provides limited benefit and often degrades categorical risk assessment. Error analysis further localizes major failures to scenario-critical context omissions during hazard generation and to controllability misjudgments during risk assessment, indicating where expert oversight should be concentrated. The dataset can be obtained from https://github.com/xixi47520-hash/HARA.

cs.CL↗

arXiv2026

Living-Harness Is an Interactive-Agent Evolver

Large language model (LLM) agents may recover from a failure within an episode or after a retry, yet the same execution failure can recur in later tasks because post-episode feedback rarely revises the persistent harness that guides future interactions. Static harnesses improve reliability through fixed tools, context, memory, and workflow structures, but remain unchanged after deployment. We propose $\textbf{Living-Harness}$, a self-evolving agent harness that converts each completed trajectory and its evaluator signals into posterior evidence for bounded harness updates. Guided by a domain-level $\textbf{Evolution-SOP}$ ($\textbf{S}$tandard $\textbf{O}$perating $\textbf{P}$rocedure), Living-Harness extracts an episode abstraction and structured update evidence, and writes two complementary forms of procedural knowledge: episodic memory that records trigger conditions, failure patterns, and recovery actions, and a state graph that records state nodes, repair edges, and transition rules. The updated harness state is retrieved to guide future interactions, while tools and base context remain frozen, allowing procedural repairs to accumulate across evolution cycles. On eight interactive environments derived from $τ^2$-Bench and MultiWOZ-2.4, Living-Harness improves average Pass@1 over the strongest interactive baseline by 10.07 and 9.91 percentage points, respectively, and supports retrieval-only reuse of the evolved harness state across model backbones. Our code will be made publicly available soon at https://github.com/anotherbricki/Living-Harness.

cs.MA↗

arXiv2026

CARE: Confidence-Aware Reasoning for Reliable Medical VQA

Reinforcement Fine-Tuning (RFT) has enabled medical Multimodal Large Language Models (MLLMs) to produce Chain-of-Thought (CoT) reasoning for visual question answering, yet these models suffer from $\textit{confidence miscalibration}$---a systematic gap between expressed certainty and actual diagnostic accuracy that undermines clinical trust. We propose $\textbf{CARE}$, a $\textbf{C}$onfidence-$\textbf{A}$ware medical $\textbf{RE}$asoning framework that jointly optimizes accuracy and calibration through a dual-stage pipeline. First, a scalable Medical-CoT synthesis provides structured cold-start data for Supervised Fine-Tuning. Second, Group Relative Policy Optimization (GRPO) with a novel $\textbf{Confidence-Aware Reward (CAR)}$ mechanism ties the model's confidence to diagnostic correctness within the reward signal. Across three Medical VQA benchmarks, $\textbf{CARE}$ achieves the highest diagnostic accuracy while obtaining the lowest Expected Calibration Error and Hallucination Rate, establishing a foundation for trustworthy clinical decision support. Our code is available at https://github.com/anotherbricki/CARE.

cs.CV↗

arXiv2026

Linking Perception, Confidence and Accuracy in MLLMs

Recent advances in Multi-modal Large Language Models (MLLMs) have predominantly focused on enhancing visual perception to improve accuracy. However, a critical question remains unexplored: Do models know when they do not know? Through a probing experiment, we reveal a severe confidence miscalibration problem in MLLMs. To address this, we propose Confidence-Driven Reinforcement Learning (CDRL), which uses original-noise image pairs and a novel confidence-based reward to enhance perceptual sensitivity and robustly calibrate the model's confidence. Beyond training benefits, calibrated confidence enables more effective test-time scaling as a free lunch. We further propose Confidence-Aware Test-Time Scaling (CA-TTS), which dynamically coordinates Self-Consistency, Self-Reflection, and Visual Self-Check modules guided by confidence signals. An Expert Model acts in multiple roles (e.g., Planner, Critic, Voter) to schedule these modules and provide external verification. Our integrated framework establishes new state-of-the-art results with consistent 8.8% gains across four benchmarks. More ablation studies demonstrate the effectiveness of each module and scaling superiority.

cs.CV↗

arXiv2026

SEVADE: Self-Evolving Multi-Agent Analysis with Decoupled Evaluation for Hallucination-Resistant Irony Detection

Sarcasm detection is a crucial yet challenging Natural Language Processing task. Existing Large Language Model methods are often limited by single-perspective analysis, static reasoning pathways, and a susceptibility to hallucination when processing complex ironic rhetoric, which impacts their accuracy and reliability. To address these challenges, we propose **SEVADE**, a novel **S**elf-**Ev**olving multi-agent **A**nalysis framework with **D**ecoupled **E**valuation for hallucination-resistant sarcasm detection. The core of our framework is a Dynamic Agentive Reasoning Engine (DARE), which utilizes a team of specialized agents grounded in linguistic theory to perform a multifaceted deconstruction of the text and generate a structured reasoning chain. Subsequently, a separate lightweight rationale adjudicator (RA) performs the final classification based solely on this reasoning chain. This decoupled architecture is designed to mitigate the risk of hallucination by separating complex reasoning from the final judgment. Extensive experiments on four benchmark datasets demonstrate that our framework achieves state-of-the-art performance, with average improvements of **6.75%** in Accuracy and **6.29%** in Macro-F1 score.

cs.CL↗

arXiv2025

Universal spectrum and scaling laws for halo mass function, structure, and dark matter mass constraints

Between the linear and nonlinear regimes, we identify a universal transition range centered on a characteristic halo mass $m_h^*\propto t$, within which gravitational dynamics self-organize the matter field toward an effective spectral index n=-1. In a bottom-up hierarchy, early collapse of low-mass halos preserves imprints of the primordial spectrum, whereas prolonged assembly of halos near $m_h^*$ erases that memory and establishes universality. We formulate a scale-to-scale cascade, the redistribution of mass and energy across scales, that yields universal scaling laws for the halo mass function and internal structure. Globally, the cascade drives a random walk of halos with mass-dependent waiting time $τ_g\propto m_h^{-λ}$; A Fokker-Planck equation gives mass function $f_M\propto m_h^{-λ}$ and $λ=2/3$ for the gravity-dominant transition range. Locally, a radially directed cascade governs particle migration with waiting time $τ_{gr}\propto r^{-γ}$, yielding density $ρ_r\propto r^{-2γ}$ and $γ=2/3$ on scales near $m_h^*$. The cascade drives the system toward a statistically steady state that continuously releases energy and maximizes entropy, characterized by scale-independent rates, preventing mass or energy buildup at intermediate scales. Scale-dependent dominance of the primordial spectrum versus gravity implies two effective exponents, producing double-$λ$ mass functions and double-$γ$ density in excellent agreement with simulations. Using Illustris and Virgo, we measure an inverse kinetic-energy cascade from small to large scales at $\varepsilon_u \approx -10^{-7}$m$^2$/s$^3$, a direct potential-energy cascade of $-1.4\varepsilon_u$, and a net dissipation of -0.4$\varepsilon_u$ via halo mergers and particle migration. The dependence of waiting time and step length on the particle mass suggests new constraints near $10^{12}$GeV.

astro-ph.CO↗

arXiv2025

A Unified Framework for Event-based Frame Interpolation with Ad-hoc Deblurring in the Wild

Effective video frame interpolation hinges on the adept handling of motion in the input scene. Prior work acknowledges asynchronous event information for this, but often overlooks whether motion induces blur in the video, limiting its scope to sharp frame interpolation. We instead propose a unified framework for event-based frame interpolation that performs deblurring ad-hoc and thus works both on sharp and blurry input videos. Our model consists in a bidirectional recurrent network that incorporates the temporal dimension of interpolation and fuses information from the input frames and the events adaptively based on their temporal proximity. To enhance the generalization from synthetic data to real event cameras, we integrate self-supervised framework with the proposed model to enhance the generalization on real-world datasets in the wild. At the dataset level, we introduce a novel real-world high-resolution dataset with events and color videos named HighREV, which provides a challenging evaluation setting for the examined task. Extensive experiments show that our network consistently outperforms previous state-of-the-art methods on frame interpolation, single image deblurring, and the joint task of both. Experiments on domain transfer reveal that self-supervised training effectively mitigates the performance degradation observed when transitioning from synthetic data to real-world data. Code and datasets are available at https://github.com/AHupuJR/REFID.

cs.CV↗

arXiv2024

On the statistical theory of self-gravitating collisionless dark matter flow: Scale and redshift variation of velocity and density distributions

This paper studies the scale and redshift variation of density and velocity distributions in self-gravitating collisionless dark matter flow by a halo-based non-projection approach. All particles are divided into halo and out-of-halo particles for redshift variation of distributions. Without projecting particle fields onto a structured grid, the scale variation is analyzed by identifying all particle pairs on different scales $r$. We demonstrate that: i) Delaunay tessellation can be used to reconstruct the density field. The density correlation, spectrum, and dispersion functions were obtained, modeled, and compared with the N-body simulation; ii) the velocity distributions are symmetric on both small and large scales and are non-symmetric with a negative skewness on intermediate scales due to the inverse energy cascade at a constant rate $\varepsilon_u$; iii) On small scales, the even order moments of pairwise velocity $Δu_L$ follow a two-thirds law $\propto{(-\varepsilon_ur)}^{2/3}$, while the odd order moments follow a linear scaling $\langle(Δu_L)^{2n+1}\rangle=(2n+1)\langle(Δu_L)^{2n}\rangle\langleΔu_L\rangle\propto{r}$; iv) The scale variation of the velocity distributions was studied for longitudinal velocities $u_L$ or $u_L^{'}$, pairwise velocity (velocity difference) $Δu_L$=$u_L^{'}$-$u_L$ and velocity sum $Σu_L$=$u^{'}_L$+$u_L$. Fully developed velocity fields are never Gaussian on any scale, despite that they can initially be Gaussian; v) On small scales, $u_L$ and $Σu_L$ can be modeled by a $X$ distribution to maximize the system entropy; vi) On large scales, $Δu_L$ and $Σu_L$ can be modeled by a logistic or a $X$ distribution; vii) the redshift variation of the velocity distributions follows the evolution of the $X$ distribution involving a shape parameter $α(z)$ decreasing with time.

astro-ph.CO↗

arXiv2024

Performance Metrics and Loss Mechanisms in Horticulture Luminescent Solar Concentrators

Horticulture Luminescent Solar Concentrators (HLSCs) represent an innovative concept developed in recent years to promote crop yields, building upon the foundation of traditional Luminescent Solar Concentrators (LSCs). HLSCs are characterized by two distinct properties: spectral conversion and light extraction. Unlike traditional LSCs, HLSCs focus on converting energy from one part of the solar spectrum (typically green) to a specific range (usually red) and aim for the converted photons to exit the device from the bottom surface rather than the edge surfaces. In this study, we start by examining the specific requirements of horticulture to clarify the motivation for using HLSCs. We re-evaluate and propose new optical metrics tailored to HLSCs. Additionally, we analyse potential loss channels for direct red emission and converted red emission. Utilizing Monte Carlo ray tracing method and experimental data, we further explore the factors influencing these loss channels. Our work provides a fundamental discussion on HLSCs and offers design guidelines for future HLSC research.

physics.optics↗

arXiv2024

On the statistical theory of self-gravitating collisionless dark matter flow: high order kinematic and dynamic relations

To better understand the collisionless dark matter flow on different scales, statistical theory involving kinematic and dynamic relations must be developed for different types of flow, e.g. incompressible, constant divergence, and irrotational flow. This paper extends our previous work on the second-order statistics (Phys. Fluids 35, 077105) to high order statistics. Kinematic and dynamic relations were developed for dark matter flow on different scales. The results were validated by N-body simulations. On large scales, we found i) third-order velocity correlations can be related to density correlation or pairwise velocity; ii) the $p$th-order velocity correlations follow $\propto a^{(p+2)/2}$ for odd $p$ and $\propto a^{p/2}$ for even $p$, where $a$ is the scale factor; iii) the overdensity $δ$ is proportional to density correlation on the same scale; iv) velocity dispersion on a given scale $r$ is proportional to the overdensity on the same scale. On small scales, i) a self-closed velocity evolution is developed by decomposing the velocity into motion in haloes and motion of haloes; ii) the evolution of vorticity and enstrophy are derived from the evolution of velocity; iii) dynamic relations are derived to relate second- and third-order correlations; iv) while the first moment of pairwise velocity follows $\langleΔu_L\rangle=-Har$ ($H$ is the Hubble parameter), the third moment follows $\langle(Δu_L)^3\rangle\propto\varepsilon_uar$ that can be directly compared with simulations and observations, where $\varepsilon_u\approx10^{-7}$m$^2$/s$^3$ is the constant rate for energy cascade; v) the $p$th order velocity correlations follow $\propto a^{(3p-5)/4}$ for odd $p$ and $\propto a^{3p/4}$ for even $p$. Finally, the combined kinematic and dynamic relations lead to exponential and one-fourth power-law velocity correlations on large and small scales, respectively.

astro-ph.CO↗

arXiv2024

Dissipative Particle Dynamics and other particle methods for multiphase fluid flow in fractured and porous media

Particle methods are less computationally efficient than grid based numerical solution of the Navier Stokes equation. However, they have important advantages including rigorous mass conservation, momentum conservation and isotropy. In addition, there is no need for explicit interface tracking/capturing and code development effort is relatively low. We describe applications of three particle methods: molecular dynamics, dissipative particle dynamics and smoothed particle hydrodynamics. The mesoscale (between the molecular and continuum scales) dissipative particle dynamics method can be used to simulate systems that are too large to simulate using molecular dynamics but small enough for thermal fluctuations to play an important role.

physics.flu-dyn↗

arXiv2024

LF-PGVIO: A Visual-Inertial-Odometry Framework for Large Field-of-View Cameras using Points and Geodesic Segments

In this paper, we propose LF-PGVIO, a Visual-Inertial-Odometry (VIO) framework for large Field-of-View (FoV) cameras with a negative plane using points and geodesic segments. The purpose of our research is to unleash the potential of point-line odometry with large-FoV omnidirectional cameras, even for cameras with negative-plane FoV. To achieve this, we propose an Omnidirectional Curve Segment Detection (OCSD) method combined with a camera model which is applicable to images with large distortions, such as panoramic annular images, fisheye images, and various panoramic images. The geodesic segment is sliced into multiple straight-line segments based on the radian and descriptors are extracted and recombined. Descriptor matching establishes the constraint relationship between 3D line segments in multiple frames. In our VIO system, line feature residual is also extended to support large-FoV cameras. Extensive evaluations on public datasets demonstrate the superior accuracy and robustness of LF-PGVIO compared to state-of-the-art methods. The source code will be made publicly available at https://github.com/flysoaryun/LF-PGVIO.

cs.CV↗

arXiv2023

Optimizing Horticulture Luminescent Solar Concentrators via Enhanced Diffuse Emission Enabled by Micro-Cone Arrays

Optimizing the photon spectrum for photosynthesis and improving crop yields presents an efficient pathway to alleviate global food shortages. Luminescent solar concentrators (LSCs), consisting of transparent host matrices doped with fluorophores, show excellent promise to achieve the desired spectral tailoring. However, conventional LSCs are predominantly engineered for photon harvesting, which results in a limited outcoupling efficiency of converted photons. Here, we introduce a scheme to implement LSCs into Horticulture (HLSC) by enhancing light extraction. The symmetry of the device is disrupted by incorporating micro-cone arrays on the bottom surface to mitigate Total Internal Reflection (TIR). Both Monte Carlo ray tracing simulations and experimental results have verified that the greatest enhancements in converted light extraction, relative to planar LSCs, are achieved using micro-cone arrays (base width 50 um, aspect ratio 1.2) with extruded (85.15% improvement) and protruded (66.55% improvement) profiles. Angularly resolved transmission measurements show that the HLSC device exhibits a broad angular radiation distribution. This characteristic indicates that the HLSC device emits diffuse light, which is conducive to optimal plant growth.

physics.optics↗

arXiv2023

Investigation of countercurrent flow profile and liquid holdup in random packed column with local CFD data

Liquid holdup and mass transfer area are critical parameters for packed column design and CO2 capture efficiency prediction. In this paper, a framework was established for modeling the liquid-gas countercurrent flow hydrodynamics in a random packed column with pall rings. Besides the column-averaged information, the radial pall ring distribution, velocity, and liquid holdup profiles are obtained to study the entrance effect and the wall influence in the packed column. With local CFD data, the validated packing specific area ap and liquid velocity uL range for liquid holdup correlation is significantly expanded with respect to existing experimental or column-averaged CFD data. The proposed liquid holdup correlation $h_L \propto u_L^{0.44}$ indicates the random packed column falls in a viscous to turbulent transition regime and it covers a Reynolds Number range of [6.7-40.2]. The derived liquid holdup correlation is in good agreement with existing correlations developed using the column-averaged experimental data.

physics.flu-dyn↗

arXiv2023

Universal scaling laws and density slope for dark matter haloes

Small scale challenges suggest some missing pieces in our understanding of dark matter. A cascade theory for dark matter is proposed to provide extra insights, similar to the cascade phenomenon in hydrodynamic turbulence. The kinetic energy is cascaded in dark matter from small to large scales involves a constant rate $\varepsilon_u$ ($\approx -4.6\times 10^{-7}m^2/s^3$). Confirmed by N-body simulations, energy cascade leads to a two-thirds law for kinetic energy $v_r^2$ on scale $r$ such that $v_r^2 \propto (\varepsilon_u r)^{2/3}$. A four-thirds law can be established for mean halo density $ρ_s$ enclosed in the scale radius $r_s$ such that $ρ_s \propto \varepsilon_u^{2/3}G^{-1}r_s^{-4/3}$, which was confirmed by galaxy rotation curves. Critical properties of dark matter might be obtained by identifying key constants on relevant scales. The largest halo scale $r_l$ can be determined by $-u_0^3/\varepsilon_u$, where $u_0$ is the velocity dispersion. The smallest scale $r_η$ is dependent on the nature of dark matter. For collisionless dark matter, $r_η \propto (-{G\hbar/\varepsilon_{u}}) ^{1/3}\approx 10^{-13}m$ is found along with the mass scale $m_X\propto (-\varepsilon_u\hbar^5G^{-4})^{1/9}\approx 10^{12}GeV$, where $\hbar$ is the Planck constant. An uncertainty principle for momentum and acceleration fluctuations is also postulated. For self-interacting dark matter, $r_η \propto \varepsilon_{u}^2 G^{-3}(σ/m)^3$, where $σ/m$ is the cross-section of interaction. On halo scale, the energy cascade leads to an asymptotic density slope $γ=-4/3$ for fully virialized haloes with a vanishing radial flow, which might explain the nearly universal halo density. Based on the continuity equation, halo density is analytically shown to be closely dependent on the radial flow and mass accretion, such that simulated haloes can have different limiting slopes.

astro-ph.GA↗

arXiv2023

Direct and in situ examination of Li+ transport kinetics in isotope labelled solid electrolyte interphase

Here, using unique in-situ liquid secondary ion mass spectroscopy on isotope-labelled solid-electrolyte-interphase (SEI), assisted by cryogenic transmission electron microscopy and constrained ab initio molecular dynamics simulation, for the first time we answer the question regarding Li+ transport mechanism across SEI, and quantitatively determine the Li+-mobility therein. We unequivocally unveil that Li+ transport in SEI follows a mechanism of successive displacement, rather than "direct-hopping". We further reveal, in accordance with spatial-dependence of SEI structure across the thickness, the apparent Li+ self-diffusivity varies from 6.7*10-19 m2/s to 1.0*10-20 m2/s, setting a quantitative gauging of ionic transport behavior of SEI layer against the underlining electrode as well as the rate limiting step of battery operation. This direct study on Li+ kinetics in SEI fills part of the decade-long knowledge gap about the most important component in advanced batteries and provides more precise guidelines to the tailoring of interphasial chemistries for future battery chemistries.

cond-mat.mtrl-sci↗

arXiv2023

On the statistical theory of self-gravitating collisionless dark matter flow

Dark matter, if exists, accounts for five times as much as the ordinary baryonic matter. Compared to hydrodynamic turbulence, the flow of dark matter might possess the widest presence in our universe. This paper presents a statistical theory for the flow of dark matter that is compared with N-body simulations. By contrast to hydrodynamics of normal fluids, dark matter flow is self-gravitating, long-range, and collisionless with a scale dependent flow behavior. The peculiar velocity field is of constant divergence nature on small scale and irrotational on large scale. The statistical measures, i.e. correlation, structure, dispersion, and spectrum functions are modeled on both small and large scales, respectively. Kinematic relations between statistical measures are fully developed for incompressible, constant divergence, and irrotational flow. Incompressible and constant divergence flow share same kinematic relations for even order correlations. The limiting correlation of velocity $ρ_L=1/2$ on the smallest scale ($r=0$) is a unique feature of collisionless flow ($ρ_L=1$ for incompressible flow). On large scale, transverse velocity correlation has an exponential form $T_2\propto e^{-r/r_2}$ with a constant comoving scale $r_2$=21.3Mpc/h that maybe related to the horizon size at matter-radiation equality. All other correlation, structure, dispersion, and spectrum functions for velocity, density, and potential fields are derived analytically from kinematic relations for irrotational flow. On small scale, longitudinal structure function follows one-fourth law of $S^l_2\propto r^{1/4}$. All other statistical measures can be obtained from kinematic relations for constant divergence flow. Vorticity is negatively correlated for scale $r$ between 1 and 7Mpc/h. Divergence is negatively correlated for $r$>30Mpc/h that leads to a negative density correlation.

astro-ph.CO↗

arXiv2023

Maximum entropy distributions of dark matter in $Λ$CDM cosmology

Small-scale challenges to $Λ$CDM cosmology require a deeper understanding of dark matter physics.This paper aims to develop maximum entropy distributions for dark matter particle velocity (denoted by $X$), speed (denoted by $Z$), and energy (denoted by $E$) that are especially relevant on small scales where system approaches full virialization. For systems involving long-range interactions, a spectrum of halos of different sizes is required to form to maximize system entropy. While velocity in halos can be Gaussian, the velocity distribution throughout entire system, involving all halos of different sizes, is non-Gaussian. With the virial theorem for mechanical equilibrium, we applied maximum entropy principle to the statistical equilibrium of entire system, such that maximum entropy distribution of velocity (the $X$ distribution) could be analytically derived. The halo mass function was not required in this formulation, but it did indeed result from the maximum entropy. The predicted $X$ distribution involves a shape parameter $α$ and a velocity scale, $v_0$. The shape parameter $α$ reflects the nature of force ($α\rightarrow0$ for long-range force or $α\rightarrow\infty$ for short-range force). Therefore, the distribution approaches Laplacian with $α\rightarrow0$ and Gaussian with $α\rightarrow\infty$. For an intermediate value of $α$, the distribution naturally exhibits a Gaussian core for $v\ll v_0$ and exponential wings for $v\gg v_0$, as confirmed by N-body simulations. From this distribution, the mean particle energy of all dark matter particles with a given speed, $v$, follows a parabolic scaling for low speeds ($\propto v^2$ for $v\ll v_0$ in halo core region, i.e., "Newtonian") and a linear scaling for high speeds ($\propto v$ for $v\gg v_0$ in halo outskirt, i.e., exhibiting "non-Newtonian" behavior in MOND due to long-range gravity).

astro-ph.CO↗