arXiv subjects

Fuqiang Wang

Publications and source records attributed to Fuqiang Wang.

At least 19 recordsLinked to original sources

arXiv2026

MAGAR: Multimodal Agents for GUI-Based 3D Mechanical Assembly and Minimal Rework

A GUI command can be correct at the interface level yet still produce an incorrect assembly. An agent must identify the relevant components, respect their structural relationships, and determine which parts should move during recovery. We introduce MAGAR-Bench, a benchmark of 4,000 multimodal samples from 26 assembly families that separates Assembly Decision, Atomic Action, and Minimal Rework. For rework, the objective counts distinct moved parts within the validated recovery candidate set for each state, rather than the number of actions. We also introduce MAGAR, a selective correction framework built on a frozen policy obtained by supervised fine-tuning. Its key design matches correction granularity to output dependencies. Structured candidates revise assembly fields and rework scope, while complete action candidates keep native GUI primitives coupled with their parameters. A learned selector retains the original prediction unless a candidate passes a task-specific intervention threshold. On recorded states from held-out assembly families, MAGAR improves Overall, the task-balanced aggregate, from 37.50 to 41.39, with gains on all three tasks. Across 209 interventions, 70 correct an error and 22 introduce one. These results support task-specific correction while highlighting the remaining tradeoff between fixing errors and preserving reliable predictions.

cs.AI↗

arXiv2026

FORGE: Fault-Oriented Rework Code Generation for Executable Interlocking Assembly

Large language models have demonstrated strong capabilities in structured reasoning and code generation, yet identifying an assembly fault does not guarantee the ability to generate executable rework programs. In interlocking assemblies, a locally plausible repair program may violate blocking dependencies, omit required recovery operations, or terminate in an incorrect configuration. We identify this challenge as fault-conditioned executable rework generation, where models must jointly reason about structural failures and generate programs that restore constrained assembly states. To benchmark this capability, we introduce FORGE-Bench, containing 4,000 tasks across 26 interlocking assemblies with five assemblies held out for evaluating generalization to unseen structures. We further propose FORGE (Fault-Oriented Rework Code Generation for Executable Interlocking Assembly), a structured state-to-program framework that decomposes rework generation into Rework Structuring and Action Grounding. Rework Structuring captures fault-induced recovery dependencies, while Action Grounding maps these dependencies into state-conditioned executable actions. Experiments show that locally reasonable repair code is insufficient for reliable assembly recovery, and that connecting structural recovery reasoning with executable action realization is essential for verified target restoration.

cs.AI↗

arXiv2026

FindLink: Learning to Assess Scientific Connections from Source-Grounded Premises

Scientific discovery from the literature requires assessing connections among findings obtained under different study conditions. Source attribution makes individual findings traceable, but does not determine what they jointly support. We introduce GI-TraceBench, a benchmark of 10,282 questions in gastrointestinal oncology that unifies provenance question answering, knowledge discovery, and hypothesis evaluation through shared Paper, Claim, and Evidence Span records. We further propose FindLink, a two-stage framework for provenance-grounded scientific reasoning. Premise Grounding constructs source-bound premises with explicit study context and support status. Structured Reasoning organizes these premises into support plans that represent both supported relations and unresolved conditions, then generates evidence-conditioned answers using deterministic compiler feedback. The compiler verifies structural and source-binding consistency, but does not assess scientific entailment. On the 1,545-question fixed-evidence test set, FindLink improves the balanced family macro score from 67.95 to 87.25 over direct supervised fine-tuning of the same backbone. A retrained variant without Structured Reasoning achieves slightly higher premise-identifier F1, but substantially lower relation and evidence-sufficiency F1. These results indicate that scientific reasoning requires not only identifying grounded premises, but also assessing what connections those premises support.

cs.CL↗

arXiv2026

VIGOR: Verifiable Industrial Geometry Optimization and Repair in Scene Programs

Large language models (LLMs) increasingly generate structured layouts and executable scene programs from natural-language instructions. However, executability does not imply engineering feasibility: an industrial scene program may execute successfully while its final state still violates coupled boundary, collision, clearance, aisle, or structural constraints. We identify this mismatch as the executability-engineering-feasibility gap, where locally plausible placements can remain globally infeasible because corrections to one device may introduce violations elsewhere. To make this gap measurable in a controlled setting, we construct an execution-grounded industrial scene-program dataset with 1,749 constructible scenarios, complete executable programs, and 94 final-state geometric checks per scenario. We further propose VIGOR, which combines supervised scene-program generation with execution-grounded verification and staged geometric refinement. Supervised fine-tuning establishes complete program generation, local projection resolves independently correctable violations, and joint conflict-graph repair coordinates residual multi-object conflicts while preserving structural relations. Experiments show that VIGOR achieves 96.86% Complete Feasibility and an Overall score of 91.20, outperforming the strongest evaluated baseline by 10.42 points in Overall. These results show that, under controlled industrial layout conditions, reliable scene-program generation requires not only executable programs, but also explicit verification and coordinated resolution of coupled engineering constraints.

cs.AI↗

arXiv2026

LimiX-2: A Contextual Mechanism Network Towards General Structured-Data Intelligence

We introduce LimiX-2, a new model in the LimiX family, developed through model and data scaling guided by our previously established scaling laws. LimiX-2 adopts the Contextual Mechanism Networks (CMNs) paradigm and is pretrained with Context-Conditional Masked Modeling (CCMM). CMNs shifts the organizing principle of in-context learning from target-centric prediction to mechanism-oriented joint modeling. Rather than centering the network on the $p(y \mid x, D_{\mathrm{context}})$ objective of conventional tabular PFNs, it is designed around learning $p(x, y \mid D_{\mathrm{context}})$, a context-dependent representation of the joint structure underlying data generation. Pretraining uses synthetic datasets generated by structural causal models (SCMs) spanning diverse graph structures, functional mechanisms, and observation processes. Evaluations on TabArena, TALENT, and BCCO show that LimiX-2 outperforms current dataset-specific models and tabular foundation models. Beyond predictive performance, the CMN paradigm also promotes causal awareness in LimiX-2: its feature attention encodes direct causal relationships, enabling accurate causal skeleton recovery.

cs.AI↗

arXiv2026

PaperFlow: Profiling, Recommending, and Adapting Across Daily Paper Streams

Scientific paper recommendation is typically evaluated as static ranking over a fixed candidate set, yet real scientific reading unfolds as a daily, longitudinal process in which interests shift and feedback accumulates. We introduce PaperFlow, a framework that organizes it into three coupled stages: Profiling, which constructs and maintains a structured, inspectable scholarly profile from heterogeneous cold-start evidence; Recommending, which ranks each date-specific paper stream through multi-signal aggregation under a fixed display budget; and Adapting, which updates user state from semantically distinct feedback signals and models interest drift across days. We further define a longitudinal user-day benchmark that fixes users, dates, candidate pools, visible inputs, and hidden simulated relevance labels under a shared temporal information boundary. The benchmark contains 24 simulated research users, 50 daily paper streams, 1,200 user-day episodes, 20,727 unique papers, and 497,448 episode-paper records. We additionally specify a blind human-evaluation protocol to validate alignment between automatic metrics and expert judgments. Experiments against five scientific recommendation baselines show that PaperFlow achieves the strongest oracle-based ranking, the highest behavioral alignment with simulated reading selections, and the best blind human-evaluation score.

cs.IR↗

arXiv2026

A higher-harmonic observable for the chiral magnetic effect in heavy-ion collisions

The chiral magnetic effect (CME) is a phenomenon in which electric charge is separated by a strong magnetic field from local domains of chirality imbalance in quantum chromodynamics. The CME-sensitive azimuthal correlator difference $Δγ$ between opposite- and same-sign charged hadron pairs is designed to detect charge separation along the magnetic field, on average perpendicular to the reaction plane. However, the search for the CME is hindered by large background contributions to $Δγ$ from particle correlations coupled with elliptic flow. In this work, we explore higher-harmonic components in differential $Δγ(ϕ_{\rm pair})$ as a function of the pair azimuthal angle. Such components could arise from event-by-event fluctuations of the magnetic fields throughout the collision zone, in both direction and magnitude. We show by using heavy-ion physics models that the hexadecapole component of $Δγ(ϕ_{\rm pair})$ is sensitive to the CME and insensitive to physics backgrounds. This could offer a unique observable for the CME that is robust against background contributions.

nucl-th↗

arXiv2026

Experimental review on the chiral magnetic effect in relativistic heavy ion collisions

The chiral magnetic effect (CME) refers to a predicted phenomena in quantum chromodynamics that manifests as a charge separation along an external magnetic field, driven by an imbalance of quark chirality. Searches for the CME has been carried out by azimuthal particle correlations in relativistic heavy ion collisions where such a chirality imbalance is anticipated and a strong magnetic field is created in the initial stage. No conclusive experimental evidence on the CME has been established so far because of large background contributions to azimuthal correlation observables. We review the status of the experimental search for the CME, covering the observables used, the techniques to mitigate backgrounds, and the strengths and limitations of various experimental approaches, and outline a future prospect of the CME search in high-energy nuclear collisions.

nucl-ex↗

arXiv2025

Investigating event-shape methods in the search for the chiral magnetic effect in relativistic heavy ion collisions

The Chiral Magnetic Effect (CME) is a phenomenon in which electric charge is separated by a strong magnetic field from local domains of chirality imbalance and parity violation in quantum chromodynamics (QCD). The CME-sensitive observable, charge-dependent three-point azimuthal correlator $Δγ$, is contaminated by a major physics background proportional to the particle's elliptic flow anisotropy $v_2$. Event-shape engineering (ESE) binning events in dynamical fluctuations of $v_2$ and event-shape selection (ESS) binning events in statistical fluctuations of $v_2$ are two methods to search for the CME by projecting $Δγ$ to the measured anisotropy $v_2=0$ intercept. We conduct a systematic study of these two methods using physics models as well as toy model simulations. It is observed that the ESE method fulfills the general premise of measuring the CME but is statistically hungry. It is found that the intercept from the ESS method depends on the details of the event content, such as the mixtures of background-contributing sources, because of statistical fluctuations of intertwining variables used in the method, and is thus not practically useful or clean to measure the CME.

physics.data-an↗

arXiv2025

Investigating the Event-Shape Methods in Search for the Chiral Magnetic Effect in Relativistic heavy-ion Collisions

The azimuthal correlator $Δγ$ searching for the chiral magnetic effect (CME) is contaminated by a major background proportional to the elliptic flow $v_2$. Event-shape engineering (ESE) and event-shape selection (ESS) binning events in {\em dynamical} and {\em statistical} fluctuations of $v_2$, respectively, are two methods searching for the CME. We conduct a systematic study using physics and toy model simulations. It is found that ESE fulfills the general premise of measuring the CME but is statistically hungry, whereas ESS is not practical to measure the CME because of the intertwining variables used in the method.

nucl-ex↗

arXiv2025

Observation of Universal Expansion Anisotropy from Cold Atoms to Hot Quark-Gluon Plasma

Azimuthal anisotropy has been ubiquitously observed in high-energy proton-proton, proton-nucleus, and nucleus-nucleus (heavy-ion) collisions, shaking the early belief that those anisotropies require an intense phase of multiple interactions between the created particles. This work reports a study of anisotropic expansion of cold $^{6}$Li Fermi gases, initially trapped in an anisotropic potential, as a function of the interaction strength that can be readily tuned by an external magnetic field. It is found that the expansion anisotropy builds up quickly at small interaction strength, without the need of a large amount of interactions. An unexpected and quantitative universal scaling of the expansion anisotropy is observed for the first time between cold atom and heavy-ion systems as a function of the number of collisions per particle or opacity ($n_{\rm coll}$), despite their vast differences in scale and physics. The expansion isotropy in both the cold atom gases and heavy-ion collisions increases smoothly and shows no sign of saturation in the observed opacity range, with an approximate power-law dependence of $\sqrt{n_{\rm coll}}$, characteristic of random walks. This universality potentially unifies a variety of vastly different physical systems, from weakly interacting dilute gases to the strongly interacting quark-gluon plasma of the early universe.

cond-mat.quant-gas↗

arXiv2025

Exploring Data-Driven Corrections for $ϕ$-Meson Global Spin Alignment Measurements

Non-central heavy ion collisions generate large orbital angular momentum (OAM), providing opportunities to study spin phenomena such as the global spin alignment of vector mesons. Such studies are expected to reveal properties of the quark-gluon plasma produced in these collisions. Global spin alignment of vector mesons, such as the $ϕ$-meson, can be measured by the $00^{\rm th}$ coefficient of the spin density matrix, $ρ_{00}$, via the polar angle of the decay kaon momentum in the parent rest frame with respect to the OAM direction of the collision. A deviation of $ρ_{00}$ from the isotropic value of $1/3$ indicates a finite spin alignment. The reported signal of $ρ_{00}-1/3$ is on the order of $\sim 1\%$ and therefore corrections for finite detector performance and acceptance, which are expected to be on the order of a few tenths of a percent, are important. Additional complications in the detector corrections may arise from the $ϕ$-meson azimuthal anisotropy which could become intertwined with the detector efficiency. Typically, detector corrections for global spin alignment of vector mesons are performed with Monte-Carlo (MC) methods using detector simulation packages such as GEANT, however it is unclear if such methods can be trusted at the needed level of precision. In this paper, we investigate an alternative, data-driven approach in correcting for detector effects. This approach utilizes detector effects on combinatorial kaon pairs from $ϕ$-meson decays that fall within the $ϕ$-meson mass window, which can be obtained through statistical identification of decay kaons in real data analysis. We examine the degree of success of such a data-driven approach using toy-model MC simulations as well as its shortcomings.

nucl-ex↗

arXiv2025

Experimental Search for the Chiral Magnetic Effect in Relativistic Heavy-Ion Collisions: A Perspective

The chiral magnetic effect (CME) refers to generation of the electric current along a magnetic field in a chirally imbalanced system of quarks. The latter is predicted by quantum chromodynamics to arise from quark interaction with non-trivial topological fluctuations of the vacuum gluonic field. The CME has been actively searched for in relativistic heavy-ion collisions, where such gluonic field fluctuations and a strong magnetic field are believed to be present. The CME-sensitive observables are unfortunately subject to a possibly large non-CME background, and firm conclusions on a CME observation have not yet been reached. In this perspective, we review the experimental status and progress in the CME search, from the initial measurements more than a decade ago, to the dedicated program of isobar collisions in 2018 and the release of the isobar blind analysis result in 2022, to intriguing hints of a possible CME signal in Au+Au collisions, and discuss future prospects of a potential CME discovery in the anticipated high-statistic Au+Au collision data at the Relativistic Heavy-Ion Collider by 2025. We hope such a perspective will help sharpening our focus on the fundamental physics of the CME and steer its experimental search.

nucl-ex↗

arXiv2025

Energy-Embedded Neural Solvers for One-Dimensional Quantum Systems

Physics-informed neural networks (PINN) have been widely used in computational physics to solve partial differential equations (PDEs). In this study, we propose an energy-embedding-based physics-informed neural network method for solving the one-dimensional time-independent Schrödinger equation to obtain ground- and excited-state wave functions, as well as energy eigenvalues by incorporating an embedding layer to generate process-driven data. The method demonstrates high accuracy for several well-known potentials, such as the infinite potential well, harmonic oscillator potential, Woods-Saxon potential, and double-well potential. Further validation shows that the method also performs well in solving the radial Coulomb potential equation, showcasing its adaptability and extensibility. The proposed approach can be extended to solve other partial differential equations beyond the Schrödinger equation and holds promise for applications in high-dimensional quantum systems.

physics.comp-ph↗

arXiv2025

A "breathing'' octupole $^{208}$Pb nucleus: resolving the elliptical-to-triangular azimuthal anisotropy puzzle in ultracentral relativistic heavy ion collisions

Relativistic heavy ion collisions provide a unique opportunity to probe the nuclear structure by taking an instantaneous snapshot of the colliding nuclei and converting it into momentum anisotropies of final emitted hadrons. A long-standing puzzle of too large a ratio of the elliptical-to-triangular ($v_{2}$-to-$v_{3}$) anisotropies in ultracentral $^{208}$Pb+$^{208}$Pb collisions at the Large Hadron Collider(LHC) cannot be solved simply by hydrodynamic simulations with initial conditions containing the spherical or certain deformed shape of $^{208}$Pb. In this Letter, using the iEBE-VISHNU relativistic viscous hydrodynamic hybrid model simulations with the Trento initial condition, we show that a dynamic octupole deformation--a shape-breathing of $^{208}$Pb --could potentially solve the $v_{2}$-to-$v_{3}$ puzzle and simultaneously describe the $v_3\{4\}$ data measured in experiment. Our results highlight the unique capability of capturing transient collective properties of nuclei on yoctosecond ($10^{-24}$~s) timescales, unfeasible with low-energy nuclear reactions.

nucl-th↗

arXiv2025

Influence of the chiral magnetic effect on particle-pair elliptic anisotropy

Chiral Magnetic Effect (CME) is a phenomenon in which electric charge is separated by a strong magnetic field from local domains of chirality imbalance in quantum chromodynamics. The CME-sensitive, azimuthal correlator difference $Δγ$ between opposite-sign (OS) and same-sign (SS) charged hadron pairs is contaminated by a major physics background proportional to the particle elliptic anisotropy ($v_2$). The CME signal, on the other hand, contributes to the difference in the pair elliptic anisotropies between OS and SS pairs ($Δv_{2,\rm pair}$). We investigate $Δv_{2,\rm pair}$ and found its sensitivity to CME to be similar to that of the $Δγ$ observable.

hep-ph↗

arXiv2025

Azimuthal Correlation Anisotropies in p + p Collisions Simulated by Pythia

Stimulated by the keen interest of possible collective behavior in high-energy proton-proton and proton-nucleus collisions, we study two-particle angular correlations in pseudorapidity and azimuthal differences in simulated p + p interactions by the Pythia 8 event generator. Multi-parton interactions and color connection are included in these simulations which have been perceived to produce collectivity in final-state particles. Meanwhile, contributions from genuine few-body nonflow correlations, not of collective flow behavior, are known to be severe in those small-system collisions. We present our Pythia correlation studies in a pedagogical way and report azimuthal harmonic anisotropies analyzed by several methods. We observe anisotropies in those Pythia simulated events qualitatively and semi-quantitatively similar to experimental data. Our findings highlight the delicate nature of azimuthal anisotropies in small-system collisions, and provide a benchmark helping improve data analysis and interpret experimental measurements in small-system collisions.

nucl-th↗

arXiv2025

Impact of Tracking Resolutions on $ϕ$-Meson Spin Alignment Measurement

Measurements of global spin alignment of vector mesons in relativistic heavy-ion collisions can provide unique insights into spin-orbit interactions and vector meson dynamics in the Quark-Gluon Plasma (QGP) produced in those collisions. The global spin alignment is measured by the $00^{\rm th}$ coefficient of the spin density matrix, $ρ_{00}$, via the polar angle ($θ^{*}$) of the decay-daughter momentum in the parent rest frame with respect to the direction of the orbital angular momentum of the collision. Such measurements are affected by the angular and momentum resolutions of the reconstructed tracks in the experiment. Such effects are nontrivial because of kinematic complications caused by the boost to the parent rest frame, and could be important given that the global spin alignment signal is weak. In this paper, we investigate the effects of experimental tracking resolutions on measurements of the $ϕ$(1020) meson $ρ_{00}$. We study these effects for two methods of $ρ_{00}$ measurements, the conventional method analyzing the $ϕ$-meson yield versus $\cos^2 θ^*$ and the invariant mass ($m_{\rm inv}$) method utilizing $\langle\cos^2θ^*\rangle$ versus $m_{\rm inv}$. Using typical resolution values from experiments, we find that the effect of track resolution on $ρ_{00}$ is small, well within typical measurement uncertainties.

physics.data-an↗