arXiv subjects

Man Li

Publications and source records attributed to Man Li.

At least 19 recordsLinked to original sources

arXiv2026

Plan First, Judge Later, Run Better: A DMAIC-Inspired Agentic System for Industrial Anomaly Detection

Large language model (LLM) agents have shown promise in automating complex data-analysis workflows, but their reliable deployment remains challenging in high-stakes industrial scenarios. Industrial anomaly detection (IAD) is essential for manufacturing quality, safety, and efficiency, yet existing LLM-based IAD agents mainly focus on execution while under-exploiting strategy formulation. Consequently, they struggle to handle heterogeneous modalities in a unified and cost-effective manner. Inspired by the DMAIC quality-management framework, we propose DMAIC-IAD (DMAIC-inspired Agentic Industrial Anomaly Detection), a "Plan First, Judge Later" multi-agent system that aligns LLM agents with structured industrial problem-solving. DMAIC-IAD distills heterogeneous references into standardized operating procedures (SOPs) before strategy generation, and introduces a pre-trained execution-free judge model to rank candidate strategies without costly runtime trials. Extensive experiments across four modalities show that DMAIC-IAD improves average detection performance over applicable agentic baselines by 37.76%.

cs.AI↗

arXiv2026

AgentDoG 1.5: A Lightweight and Scalable Alignment Framework for AI Agent Safety and Security

Modern open-world agents such as OpenClaw exhibit powerful cross-environment execution capabilities yet introduce broad new safety risk sources. Meanwhile, advanced frontier AI models drastically lower attack barriers, rendering current agent alignment frameworks inadequate for real-world deployment. To tackle these emerging threats, we propose a lightweight and scalable agent safety alignment framework. Specifically, we update the agent safety taxonomy to accommodate emergent risks from Codex and OpenClaw execution scenarios. We further build a taxonomy-guided data engine with influence-function purification to train lightweight AgentDoG 1.5 variants (0.8B, 2B, 4B, and 8B parameters) using only around 1k samples, achieving comparable performance with leading closed-source models (e.g., GPT-5.4). Based on AgentDoG 1.5, we construct a highly efficient agentic safety SFT and RL training environment, which reduces deployment overhead in Docker-level environments by two orders of magnitude. Finally, we deploy AgentDoG 1.5 as a training-free online guardrail for real-time safety moderation. Extensive experimental results indicate that AgentDoG 1.5 achieves state-of-the-art performance in diverse and complex interactive agentic scenarios. All models and datasets are openly released.

cs.AI↗

arXiv2026

LGMT: Logic-Grounded Metamorphic Testing for Evaluating the Reasoning Reliability of LLMs

Large Language Models (LLMs) achieve strong performance on logical reasoning benchmarks, yet their reliability remains uncertain. Existing evaluations rely on static benchmarks, which fail to assess robustness under logically equivalent transformations and often overestimate reasoning capability. We propose LGMT (Logic-Grounded Metamorphic Testing), an oracle-free framework that leverages first-order logic (FOL) to evaluate LLM reasoning. By deriving metamorphic relations from formal logical equivalences, LGMT constructs semantically invariant test cases and detects reasoning defects through cross-case consistency checking. Experiments on six state-of-the-art LLMs show that LGMT exposes substantial hidden defects missed by traditional reference-based evaluations. We further find that models are particularly sensitive to symbol-level and conclusion-level variations, and that advanced prompting such as Few-shot CoT only partially mitigates these issues. These results suggest that LLM evaluation should move beyond isolated correctness toward robustness under logical invariance. LGMT provides a principled and scalable approach for diagnosing reasoning failures.

cs.AI↗

arXiv2025

Strongly Entangled Kondo and Kagome Lattices and the Emergent Magnetic Ground State in Heavy-Fermion Kagome Metal YbV$_6$Sn$_6$

Applying angle-resolved photoemission spectroscopy and density functional theory calculations, we present compelling spectroscopic evidence demonstrating the intertwining and mutual interaction between the Kondo and kagome sublattices in heavy-fermion intermetallic compound YbV$_6$Sn$_6$. We reveal the Yb 4$f$-derived states near the Fermi level, along with the presence of bulk kagome bands and topological surface states. We unveil strong interactions between the 4$f$ and itinerant electrons, where the kagome bands hosting the Dirac fermions and van Hove singularities predominate. Such findings are well described using a $c$-$f$ hybridization model. On the other hand, our systematic characterization of magnetic properties demonstrates an unusually enhanced antiferromagnetic ordering, where the kagome-derived van Hove singularities near $E_F$ play a vital role in determining the unconventional nature of the Ruderman-Kittel-Kasuya-Yosida interaction and Kondo coupling. These unique kagome-state-mediated exchange interactions have never been reported before and could lead to a novel phase diagram and various quantum critical behaviors in YbV$_6$Sn$_6$ and its siblings. Our results not only expand the family of exotic quantum phases entangled with kagome structure to the strongly correlated regime, but also establish YbV$_6$Sn$_6$ as an unprecedented platform to explore unconventional many-body physics beyond the standard Kondo picture.

cond-mat.str-el↗

arXiv2025

Photodynamic, UV-curable and fibre-forming polyvinyl alcohol derivative with broad processability and staining-free antibacterial capability

Antimicrobial photodynamic therapy (APDT) is a promising antibiotic-free strategy for broad-spectrum infection control in chronic wounds, minimising bacterial resistance risks. However, rapid photosensitiser diffusion, tissue staining, side toxicity, and short-lived antimicrobial effects present significant clinical limitations for integrating APDT into wound dressings. To address these challenges, we present the design of a bespoke polyvinyl alcohol (PVA) derivative conjugated with both phenothiazine and methacrylate functionalities, enabling staining-free antibacterial photodynamic effects, cellular tolerability and processability into various wound dressing formats, including films, textile fibres and nanoscale coatings. Tosylation of PVA is leveraged for the covalent coupling of toluidine blue, as confirmed by UV-Vis spectroscopy and the minimal release of TB in vitro. UV-induced network formation is exploited to accomplish cast films and nanoscale integrated wound dressing coatings. UV curing is also successfully coupled with an in-house wet spinning process to realise individual, water-insoluble fibres as the building blocks of fibrous wound dressings. A fluorometric assay supports the generation of reactive oxygen species when the UV-cured samples are exposed to work, but not UV, light, yielding a mean log10 reduction of up to 2.13 in S. aureus, and the complete eradication of P. aeruginosa. Direct and extract cytotoxicity tests with UV-cured films and fibres demonstrate the viability of L929 fibroblasts following 60-min light irradiation and 72-hour cell culture. The bespoke molecular architecture, broad processability and cellular tolerability of this PVA derivative are highly attractive aiming to integrate durable staining-free photodynamic capability in a wide range of healthcare technologies, from chronic wound dressings up to minimally invasive localised therapy.

q-bio.TO↗

arXiv2025

Electronic band structures of topological kagome materials

The kagome lattice has garnered significant attention due to its ability to host quantum spin Fermi liquid states. Recently, the combination of unique lattice geometry, electron-electron correlations, and adjustable magnetism in solid kagome materials has led to the discovery of numerous fascinating quantum properties. These include unconventional superconductivity, charge and spin density waves (CDW/SDW), pair density waves (PDW), and Chern insulator phases. These emergent states are closely associated with the distinctive characteristics of the kagome lattice's electronic structure, such as van Hove singularities, Dirac fermions, and flat bands, which can exhibit exotic quasi-particle excitations under different symmetries and magnetic conditions. Recently, various quantum kagome materials have been developed, typically consisting of kagome layers stacked along the $z$-axis with atoms either filling the geometric centers of the kagome lattice or embedded between the layers. In this topical review, we begin by introducing the fundamental properties of several kagome materials. To gain an in-depth understanding of the relationship between topology and correlation, we then discuss the complex phenomena observed in these systems. These include the simplest kagome metal $T_3X$, kagome intercalation metal $TX$, and the ternary compounds $AT_6X_6$ and $RT_3X_5$ ($A$ = Li, Mg, Ca, or rare earth; $T$ = V, Cr, Mn, Fe, Co, Ni; $X$ = Sn, Ge; $R$ = K, Rb, Cs). Finally, we provide a perspective on future experimental work in this field.

cond-mat.str-el↗

arXiv2024

Enabling Tensor Decomposition for Time-Series Classification via A Simple Pseudo-Laplacian Contrast

Tensor decomposition has emerged as a prominent technique to learn low-dimensional representation under the supervision of reconstruction error, primarily benefiting data inference tasks like completion and imputation, but not classification task. We argue that the non-uniqueness and rotation invariance of tensor decomposition allow us to identify the directions with largest class-variability and simple graph Laplacian can effectively achieve this objective. Therefore we propose a novel Pseudo Laplacian Contrast (PLC) tensor decomposition framework, which integrates the data augmentation and cross-view Laplacian to enable the extraction of class-aware representations while effectively capturing the intrinsic low-rank structure within reconstruction constraint. An unsupervised alternative optimization algorithm is further developed to iteratively estimate the pseudo graph and minimize the loss using Alternating Least Square (ALS). Extensive experimental results on various datasets demonstrate the effectiveness of our approach.

cs.LG↗

arXiv2024

Comparative Analysis Vision of Worldwide AI Courses

This research investigates the curriculum structures of undergraduate Artificial Intelligence (AI) education across universities worldwide. By examining the curricula of leading universities, the research seeks to contribute to a deeper understanding of AI education on a global scale, facilitating the alignment of educational practices with the evolving needs of the AI landscape. This research delves into the diverse course structures of leading universities, exploring contemporary trends and priorities to reveal the nuanced approaches in AI education. It also investigates the core AI topics and learning contents frequently taught, comparing them with the CS2023 curriculum guidance to identify convergence and divergence. Additionally, it examines how universities across different countries approach AI education, analyzing educational objectives, priorities, potential careers, and methodologies to understand the global landscape and implications of AI pedagogy.

cs.CY↗

arXiv2024

MultiFun-DAG: Multivariate Functional Directed Acyclic Graph

Directed Acyclic Graphical (DAG) models efficiently formulate causal relationships in complex systems. Traditional DAGs assume nodes to be scalar variables, characterizing complex systems under a facile and oversimplified form. This paper considers that nodes can be multivariate functional data and thus proposes a multivariate functional DAG (MultiFun-DAG). It constructs a hidden bilinear multivariate function-to-function regression to describe the causal relationships between different nodes. Then an Expectation-Maximum algorithm is used to learn the graph structure as a score-based algorithm with acyclic constraints. Theoretical properties are diligently derived. Prudent numerical studies and a case study from urban traffic congestion analysis are conducted to show MultiFun-DAG's effectiveness.

stat.ME↗

arXiv2023

Orbital-selective effect of spin reorientation on the Dirac fermions in a non-charge-ordered kagome ferromagnet Fe$_3$Ge

Kagome magnets provide a fascinating platform for the realization of correlated topological quantum phases under various magnetic ground states. However, the effect of the magnetic spin configurations on the characteristic electronic structure of the kagome lattice layer remains elusive. Here, utilizing angle-resolved photoemission spectroscopy and density functional theory calculations, we report the spectroscopic evidence for the spin-reorientation effect of a kagome ferromagnet Fe$_3$Ge, which is composed solely of kagome planes. As the Fe moments cant from the $c$ axis into the $ab$ plane upon cooling, the two kinds of kagome-derived Dirac fermions respond quite differently. The one with less-dispersive bands ($k_z$ $\sim$ 0) containing the $3d_{z^2}$ orbitals evolves from gapped into nearly gapless, while the other with linear dispersions ($k_z$ $\sim$ $\pi$) embracing the $3d_{xz}$/$3d_{yz}$ components remains intact, suggesting that the effect of spin reorientation on the Dirac fermions has an orbital selectivity. Moreover, we demonstrate that there is no signature of charge order formation in Fe$_3$Ge, contrasting with its sibling compound FeGe, a newly established charge-density-wave kagome magnet.

cond-mat.str-el↗

arXiv2023

SRL-ORCA: A Socially Aware Multi-Agent Mapless Navigation Algorithm In Complex Dynamic Scenes

For real-world navigation, it is important to endow robots with the capabilities to navigate safely and efficiently in a complex environment with both dynamic and non-convex static obstacles. However, achieving path-finding in non-convex complex environments without maps as well as enabling multiple robots to follow social rules for obstacle avoidance remains challenging problems. In this letter, we propose a socially aware robot mapless navigation algorithm, namely Safe Reinforcement Learning-Optimal Reciprocal Collision Avoidance (SRL-ORCA). This is a multi-agent safe reinforcement learning algorithm by using ORCA as an external knowledge to provide a safety guarantee. This algorithm further introduces traffic norms of human society to improve social comfort and achieve cooperative avoidance by following human social customs. The result of experiments shows that SRL-ORCA learns strategies to obey specific traffic rules. Compared to DRL, SRL-ORCA shows a significant improvement in navigation success rate in different complex scenarios mixed with the application of the same training network. SRL-ORCA is able to cope with non-convex obstacle environments without falling into local minimal regions and has a 14.1\% improvement in path quality (i.e., the average time to target) compared to ORCA. Videos are available at https://youtu.be/huhXfCDkGws.

cs.RO↗

arXiv2023

MM-DAG: Multi-task DAG Learning for Multi-modal Data -- with Application for Traffic Congestion Analysis

This paper proposes to learn Multi-task, Multi-modal Direct Acyclic Graphs (MM-DAGs), which are commonly observed in complex systems, e.g., traffic, manufacturing, and weather systems, whose variables are multi-modal with scalars, vectors, and functions. This paper takes the traffic congestion analysis as a concrete case, where a traffic intersection is usually regarded as a DAG. In a road network of multiple intersections, different intersections can only have some overlapping and distinct variables observed. For example, a signalized intersection has traffic light-related variables, whereas unsignalized ones do not. This encourages the multi-task design: with each DAG as a task, the MM-DAG tries to learn the multiple DAGs jointly so that their consensus and consistency are maximized. To this end, we innovatively propose a multi-modal regression for linear causal relationship description of different variables. Then we develop a novel Causality Difference (CD) measure and its differentiable approximator. Compared with existing SOTA measures, CD can penalize the causal structural difference among DAGs with distinct nodes and can better consider the uncertainty of causal orders. We rigidly prove our design's topological interpretation and consistency properties. We conduct thorough simulations and one case study to show the effectiveness of our MM-DAG. The code is available under https://github.com/Lantian72/MM-DAG

stat.ML↗

arXiv2023

A Multi-robot Coverage Path Planning Algorithm Based on Improved DARP Algorithm

The research on multi-robot coverage path planning (CPP) has been attracting more and more attention. In order to achieve efficient coverage, this paper proposes an improved DARP coverage algorithm. The improved DARP algorithm based on A* algorithm is used to assign tasks to robots and then combined with STC algorithm based on Up-First algorithm to achieve full coverage of the task area. Compared with the initial DARP algorithm, this algorithm has higher efficiency and higher coverage rate.

cs.RO↗

arXiv2023

Revisiting Friedmann-like cosmology with torsion: newest constraints from high-redshift observations

As one of the possible extensions of Einstein's General Theory of Relativity, it has been recently suggested that the presence of spacetime torsion could solve problems of the very early and the late-time universe undergoing accelerating phases. In this paper, we use the latest observations of high-redshift data, coming from multiple measurements of quasars and baryon acoustic oscillations, to phenomenologically constrain such cosmological model in the framework of Einstein-Cartan (EC) endowed with spacetime torsion. Such newly compiled quasar datasets in the cosmological analysis is crucial to this aim, since it will extend the Hubble diagram to high-redshift range in which predictions from different cosmologies can be distinguished. Our results show that out of all the candidate models, the torsion plus cosmological constant model is strongly favoured by the current high-redshift data, where torsion itself would be expected to yield the current cosmic acceleration. Specially, in the framework of Friedmann-like cosmology with torsion, the determined Hubble constant is in very good agreement with that derived from the Planck 2018 CMB results. On the other hand, our results are compatible with zero spatial curvature and there is no significant deviation from flat spatial hypersurfaces. Finally, we check the robustness of high-redshift observations by placing constraints on the torsion parameter $\alpha$, which is strongly consistent with other recent works focusing on torsion effect on the primordial helium-4 abundance.

astro-ph.CO↗

arXiv2022

Wiener Graph Deconvolutional Network Improves Graph Self-Supervised Learning

Graph self-supervised learning (SSL) has been vastly employed to learn representations from unlabeled graphs. Existing methods can be roughly divided into predictive learning and contrastive learning, where the latter one attracts more research attention with better empirical performance. We argue that, however, predictive models weaponed with powerful decoder could achieve comparable or even better representation power than contrastive models. In this work, we propose a Wiener Graph Deconvolutional Network (WGDN), an augmentation-adaptive decoder empowered by graph wiener filter to perform information reconstruction. Theoretical analysis proves the superior reconstruction ability of graph wiener filter. Extensive experimental results on various datasets demonstrate the effectiveness of our approach.

cs.LG↗

arXiv2022

Electronic structure and open-orbit Fermi surface topology in isostructural semimetals NbAs$_2$ and W$_2$As$_3$ with extremely large magnetoresistance

In transition-metal dipnictides $TmPn_2$ ($Tm$ = Ta, Nb; $Pn$ = P, As, Sb), the origin of extremely large magnetoresistance (XMR) is yet to be studied by the direct visualization of the experimental band structures. Here, using angle-resolved photoemission spectroscopy, we map out the three-dimensional electronic structure of NbAs$_2$. The open-orbit topology contributes to a non-negligible part of the Fermi surfaces (FSs), like that of the isostructural compound MoAs$_2$, where the open FS is proposed to likely explain the origin of XMR. We further demonstrate the observation of open characters in the overall FSs of W$_2$As$_3$, which is also a XMR semimetal with the same space group of $C$12/$m$1 as $TmPn_2$ family and MoAs$_2$. Our results suggest that the open-orbit FS topology may be a shared feature between XMR materials with the space group of $C$12/$m$1, and thus could possibly play a role in determining the corresponding XMR effect together with the electron-hole compensation.

cond-mat.mtrl-sci↗

arXiv2021

Charge-density-wave-induced bands renormalization and energy gaps in a kagome superconductor RbV3Sb5

Recently discovered Z2 topological kagome metals AV3Sb5 (A = K, Rb, and Cs) exhibit charge density wave (CDW) phases and novel superconducting paring states, providing a versatile platform for studying the interplay between electron correlation and quantum orders. Here we directly visualize CDW-induced bands renormalization and energy gaps in RbV3Sb5 using angle-resolved photoemission spectroscopy, pointing to the key role of tuning van Hove singularities to the Fermi energy in mechanisms of ordering phases. Near the CDW transition temperature, the bands around the Brillouin zone (BZ) boundary are shifted to high-binding energy, forming an "M"-shape band with singularities near the Fermi energy. The Fermi surfaces are partially gapped and the electronic states on the residual ones should be possibly dedicated to the superconductivity. Our findings are significant in understanding CDW formation and its associated superconductivity.

cond-mat.supr-con↗

arXiv2021

Model-Free Design of Stochastic LQR Controller from Reinforcement Learning and Primal-Dual Optimization Perspective

To further understand the underlying mechanism of various reinforcement learning (RL) algorithms and also to better use the optimization theory to make further progress in RL, many researchers begin to revisit the linear-quadratic regulator (LQR) problem, whose setting is simple and yet captures the characteristics of RL. Inspired by this, this work is concerned with the model-free design of stochastic LQR controller for linear systems subject to Gaussian noises, from the perspective of both RL and primal-dual optimization. From the RL perspective, we first develop a new model-free off-policy policy iteration (MF-OPPI) algorithm, in which the sampled data is repeatedly used for updating the policy to alleviate the data-hungry problem to some extent. We then provide a rigorous analysis for algorithm convergence by showing that the involved iterations are equivalent to the iterations in the classical policy iteration (PI) algorithm. From the perspective of optimization, we first reformulate the stochastic LQR problem at hand as a constrained non-convex optimization problem, which is shown to have strong duality. Then, to solve this non-convex optimization problem, we propose a model-based primal-dual (MB-PD) algorithm based on the properties of the resulting Karush-Kuhn-Tucker (KKT) conditions. We also give a model-free implementation for the MB-PD algorithm by solving a transformed dual feasibility condition. More importantly, we show that the dual and primal update steps in the MB-PD algorithm can be interpreted as the policy evaluation and policy improvement steps in the PI algorithm, respectively. Finally, we provide one simulation example to show the performance of the proposed algorithms.

eess.SY↗