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Qingfeng Li

Publications and source records attributed to Qingfeng Li.

At least 19 recordsLinked to original sources
arXiv2026

Dynamical Coulomb effects on charged-pion HBT-radius splitting in central Au+Au collisions at few-GeV energies

The pair transverse momentum ($k_T$) and collision-energy ($\sqrt{s_{NN}}$) dependent difference between $π^{+}π^{+}$ and $π^{-}π^{-}$ femtoscopic correlations was observed by the HADES and STAR Collaborations. We investigate how much of the observed splitting can be accounted for by Coulomb dynamics in 0-10\% central Au+Au collisions at $\sqrt{s_{NN}}=2.42$-5.2 GeV. Using the UrQMD model and the CRAB programme, three transport scenarios that successively include baryon-baryon, baryon-meson, and meson-meson Coulomb interactions are considered. In a separate calculation based on the baryon-baryon reference, we reconstruct the time-dependent effective charge $Z_{\rm{eff}}(t)$ and radius $R_{\rm{eff}}(t)$ of the residual charged source and propagate each pion from its individual last strong-interaction point through the evolving field. The calculations reproduce the main $k_{T}$ and $\sqrt{s_{NN}}$ dependences of the radii. Baryon--meson Coulomb interactions generate most of the additional charge splitting in the microscopic calculation. The residual-source treatment also enhances the longitudinal and sideward radius ratios relative to the reference, whereas the outward ratio changes little, and the enhancement generally decreases with increasing $\sqrt{s_{NN}}$. Within the present framework, this contribution does not fully account for the data, motivating further investigation of the charged-source geometry and its interplay with strong-interaction dynamics.

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arXiv2026

CMAR-search: Commonsense and Memory Augmented Reasoning for Object Search in Dynamic Interactive Environments

Dynamic interactive object search in large-scale human environments presents substantial challenges for existing methods. Current scene representations like 3D Scene Graphs (3DSG) only provide coarse-grained spatial segmentation and cannot identify functional areas such as storage or leisure areas. Without functional area understanding, existing methods are constrained to exhaustive sequential exploration at large scales, resulting in inefficient search behaviors -- particularly in open-layout environments with numerous interactive objects such as drawers and cabinets. Moreover, these methods lack adaptability to environmental dynamics such as object relocations. To address these limitations, this paper proposes CMAR-search, a novel framework built upon Commonsense and Memory Augmented Reasoning (CMAR). Our approach leverages commonsense about area functionalities and aggregates environmental memory to construct a Functional 3D Scene Graph (F3DSG), which organizes the environment into functional areas with their associated containers. Through this structured representation, CMAR enables hierarchical action planning at both macro-area and micro-container levels, empowering the system to efficiently identify and inspect semantically relevant areas for effective object search. Notably, CMAR continuously integrates real-time perception, accumulated memory, and commonsense to dynamically relocalize objects in changing environments. Extensive experiments in simulation and real-world settings demonstrate that CMAR-search significantly surpasses state-of-the-art baselines in both success rate and search efficiency for object search in dynamic interactive environments.

cs.RO↗
arXiv2026

OASIS: Online Anchor-centric Scene Inference System for Open-Vocabulary Retrieval and Navigation in Dynamic Environments

Reliable robotic mapping in dynamic real-world environments is essential for embodied intelligence and long-term autonomy. However, existing methods often involve a trade-off: lightweight online mapping is efficient but captures limited semantic context, while dense mapping provides richer semantics at higher computational cost and is more vulnerable to distractors. They also struggle to relocalize objects after scene changes. To address these challenges, we propose the Online Anchor-centric Scene Inference System (OASIS), a unified system for online mapping, complex open-vocabulary retrieval, and navigation in dynamic environments. Motivated by the observation that indoor scenes are organized around stable semantic anchors, OASIS represents the environment using a sparse set of anchor nodes, each maintaining a unified multimodal representation that integrates semantic descriptions, visual features, and geometric states for reasoning, verification, and navigation. Its compact anchor-centric memory supports on-demand evidence extraction and hierarchical retrieval, reducing redundant storage while improving retrieval reliability. To handle scene dynamics, OASIS further reuses transient observations collected during navigation to relocalize moved objects. Experiments in both simulation and real-world environments, including standard retrieval benchmarks and navigation tasks under both static and dynamic settings, demonstrate that our system can perform complex open-vocabulary retrieval and navigation, while also enabling relocation of displaced objects after scene changes.

cs.RO↗
arXiv2026

Remembering Through Change: Provenance-Preserving Hypergraph Memory for Multimodal Agents

Long-term memory for multimodal agents remains challenging, as it must preserve fine-grained visual evidence while tracking entity evolution across sessions. Existing abstraction-based memories compress images into text, often discarding visually discriminative details, whereas pixel-preserving memories retain raw observations but struggle to reliably associate diverse observations with persistent entity identities and track their evolving states. To address these limitations, we propose MHG-Mem, a multimodal hypergraph memory that jointly organizes entity identity, state evolution, and provenance to visual evidence. Specifically, MHG-Mem represents distinct entity mentions and their observed facts as individual nodes, grouping identity-consistent mentions and facts through occurrence hyperedges. It then synthesizes aspect-specific evolution states from these grouped observations and links them to supporting entities, facts, and source evidence through evolutionary and evidential hyperedges. To navigate this structured memory, we introduce hypergraph-based agentic search, which adaptively selects an entry layer, traverses typed hyperedges across memory levels, and inspects source observations when fine-grained verification is required. These designs support both detailed visual verification and reasoning over cross-session state changes. Extensive experiments across three benchmarks demonstrate the effectiveness of MHG-Mem, improving performance by 8.20-9.98% over the strongest baseline.

cs.CL↗
arXiv2026

Advancing Nuclear Physics with Machine Learning and Artificial Intelligence

Machine learning (ML) and artificial intelligence (AI) are becoming powerful tools in scientific research across various disciplines. In this review, we summarize recent progress in AI-assisted studies of nuclear structure and reaction observables, heavy-ion collisions and dense nuclear matter properties, many-body wave functions, experimental facilities and data analysis. This review focus on new progress since the last review in 2023, and machine learning in nuclear physics is evolving from purely data inferences to physics informed learning. Future directions on the integration of physical knowledge with modern learning architectures, large foundation models and other emerging methods are also reviewed. These developments suggest that AI is enabling and advancing new approaches towards most challenging nuclear physics problems.

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arXiv2026

Effects of in-medium $NN$ inelastic cross sections and the high-momentum tail of nucleon momentum distributions on pion production in heavy-ion collisions

Pion production in intermediate-energy heavy-ion collisions (HICs) provides a sensitive probe of the nuclear equation of state and of the isospin dependence of reaction dynamics. In particular, pion production near threshold is strongly affected by the nucleon-nucleon ($NN$) inelastic cross sections and by the high-momentum components of the nucleon momentum distribution. To explore the influence of these two ingredients on pion production and charged-pion ratios, the in-medium $NN$ inelastic cross sections calculated within the relativistic Boltzmann-Uehling-Uhlenbeck transport theory and the short-range-correlation-induced high-momentum tail (HMT) are introduced into the Ultra-relativistic Quantum Molecular Dynamics (UrQMD) model. By simulating Au+Au collisions at intermediate energies, we find that the in-medium modification of the $NN$ inelastic cross sections suppresses the pion multiplicity by reducing the probability of $NΔ$ production in dense matter. The HMT, on the other hand, enhances the high-momentum components of nucleons and modifies the available energy in individual $NN$ collisions, thereby affecting $NN\rightarrow NΔ$ reactions and the subsequent pion production. With the simultaneous inclusion of these two effects, the pion yields measured by HADES and the $π^-/π^+$ ratio measured by FOPI can be reasonably reproduced. These results highlight the need to incorporate both in-medium reaction cross sections and short-range-correlation-induced high-momentum components consistently in transport-model studies of pion production in heavy-ion collisions.

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arXiv2026

Temperature dependence of the nucleon-nucleon inelastic cross section in an isospin-asymmetric nuclear medium

The nucleon-nucleon ($NN$) inelastic cross section plays an important role in constraining the nuclear equation of state at high baryon density and in describing the formation and evolution of compact astrophysical objects. In this study, the temperature $T$ dependence of the $Δ^{++}$ and $Δ^{-}$ production cross sections in the isospin-symmetric and -asymmetric nuclear medium is investigated within the self-consistent and relativistic Boltzmann-Uehling-Uhlenbeck (RBUU) framework. Two relativistic mean-field parameterizations are employed: the density-dependent parameterization (called DD-ME$δ$) and the nonlinear-dependent parameterization (called OMEG). Both parameterizations yield similar $T$-dependent baryon effective masses and mass splittings, although the OMEG set exhibits a stronger density dependence, particularly at higher densities ($> 1.5ρ_{0}$). Consequently, at lower densities, the energy, density, temperature, and isospin dependence of both $Δ^{++}$ and $Δ^{-}$ production cross sections are comparable for both sets, whereas at higher densities, the OMEG set predicts a stronger temperature and density sensitivity. Moreover, the $T$ dependence of the $NN$ inelastic cross section is enhanced with increasing density, but is suppressed in isospin-asymmetric nuclear matter compared to that in isospin-symmetric nuclear matter. The isospin dependence of the cross section remains nearly $T$-independent at small asymmetries, yet becomes more intricate in highly asymmetric systems. These findings provide valuable testing inputs for improving the thermal treatment of $Δ$ related dynamical processes in transport models and offer insights into the behavior of $Δ$ in astrophysical environments, such as core-collapse supernovae and binary neutron star mergers.

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arXiv2026

A novel filtering method for generating desired density profiles of colliding nuclei

Accurate modeling of the density profile is essential for studying heavy-ion collisions (HICs) with a transport model. Within the framework of the quantum molecular dynamics (QMD)-type model, a novel method for generating desired nuclear density distributions based on Fourier series expansion is proposed. This new initialization method is further incorporated into the ultrarelativistic quantum molecular dynamics model, and the bubble-like density distribution of $^{96}$Ru is constructed. Then, by simulating $^{96}$Ru+$^{96}$Ru collisions at $E_{\rm lab}=1500$ MeV/nucleon with different equations of state (EoS) and initialization methods, the effects of the initial density distribution on the final state observables and the constrained information of EoS are analyzed. It is found that $^{96}$Ru nuclei with a bubble density profile lead to an increased maximum compression during the collision, which in turn enhances the collective flow. Moreover, a relatively stiff EoS with $K_0>280$ MeV is favored for the conventional Woods-Saxon type density profile, whereas an EoS with $K_0$=200-280 MeV is supported when a bubble-like density profile is employed. These results demonstrate that the initial nuclear density distribution plays a non-negligible role in dynamical observables and EoS constraints. The proposed method thus provides a powerful tool for constructing exotic profiles and investigating nuclear structure effects in HICs.

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arXiv2026

Machine learning the impact parameter in heavy-ion collisions at $\sqrt{s_{\rm NN}}$ = 4 and 11 GeV: a cross-check study with UrQMD, AMPT, and JAM

By generating heavy-ion collision data with the ultrarelativistic quantum molecular dynamics (UrQMD) model, a multiphase transport (AMPT) model, and the JAM model, the impact parameter ($b$) in Au+Au collisions at $\sqrt{s_{\rm NN}}$ = 4 and 11 GeV is reconstructed using supervised learning and unsupervised learning in machine learning (ML). In supervised learning, the performance of ML algorithm is cross-checked by using data obtained from these three transport models. It is found that the typical mean absolute error (MAE) which measures the average magnitude of the absolute difference between the true and predicted $b$ is between 0.2-0.4 fm, even when training ML algorithm with data generated from one model but testing with data from others. While the conventional method (i.e., a polynomial fit to multiplicity as a function of $b$) only works for data generated from the same model. In the classification task, the present ML-based method also shows significantly superior results compared to the traditional approach. In unsupervised learning, the K-means clustering algorithm is used to partition collision events directly from experimental-style observables, showing that the algorithm autonomously identifies six clusters corresponding to different centrality classes without relying on predefined model-based binning. Our study demonstrates the strong robustness of using an ML algorithm trained on transport-model data for impact-parameter determination, and indicates that this method has the potential to be generalized to handle real experimental data.

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arXiv2026

DL-SLAM: Enabling High-Fidelity Gaussian Splatting SLAM in Dynamic Environments based on Dual-Level Probability

Recent advances in 3D Gaussian Splatting (3DGS) have enabled significant progress in dense dynamic Simultaneous Localization And Mapping (SLAM). Prevailing methods typically discard predefined dynamic objects, ignoring that transiently static objects offer valuable geometric constraints for pose estimation. A recent work attempts to leverage this potential by employing per-pixel uncertainty maps to quantify the magnitude of motion. While this approach enables transiently static objects to enhance pose estimation, it erroneously integrates these objects into the static map, resulting in persistent artifacts. Moreover, its reliance on purely geometric information leads to ambiguous object boundaries in the uncertainty maps. To overcome these limitations, we present DL-SLAM, a monocular Gaussian Splatting SLAM system built upon a novel dual-level probabilistic framework. Our method computes dynamic probability maps by combining semantic and geometric information. These pixel-level probabilities are lifted to 3D and aggregated to derive an object-level dynamic probability for each instance. Object-level probability enables the categorical pruning of dynamic Gaussians, resulting in an artifact-free static map. The static map, in turn, provides a geometrically consistent guidance to refine the pixel-wise probabilities, enhancing their reliability. Experimental results demonstrate that DL-SLAM outperforms existing approaches, improving tracking accuracy by up to 13\% while generating high-fidelity semantic maps.

cs.RO↗
arXiv2026

Semantic-Aware Prefix Learning for Token-Efficient Image Generation

Visual tokenizers play a central role in latent image generation by bridging high-dimensional images and tractable generative modeling. However, most existing tokenizers are still trained with reconstruction-dominated objectives, which often yield latent representations that are only weakly grounded in high-level semantics. Recent approaches improve semantic alignment, but typically treat semantic signals as auxiliary regularization rather than making them functionally necessary for representation learning. We propose SMAP, a SeMantic-Aware Prefix tokenizer that injects class-level semantic conditions into a query-based 1D tokenization framework. To make semantics indispensable during training, SMAP introduces a tail token dropping strategy, which forces semantic conditions and early latent prefixes to bear increasing responsibility under progressively reduced token budgets. To verify that the resulting latent space is useful for generation rather than reconstruction alone, we further introduce CARD, a hybrid Causal AutoRegressive--Diffusion generator. Extensive experiments on ImageNet show that SMAP consistently improves reconstruction quality across discrete and continuous tokenization settings, and that its semantically grounded latent space yields strong downstream generation performance under compact token budgets.

cs.CV↗
arXiv2026

Constraining the Phase-Transition EoS using the Energy Dependence of Directed Flow

We propose a hybrid equation of state (VDF+MIT EoS) to describe the hadron-quark phase transition in dense nuclear matter. By coupling this EoS with the AMPT-HC transport model and comparing to recent experimental data on proton and $Λ$ directed flow $v_1$, we constrain the transition to likely occur near $5ρ_0$--$6ρ_0$, ruling out transitions below $3ρ_0$. Furthermore, we introduce the energy derivative of the mid-rapidity $v_1$ slope, $d(dv_1/dy)/d(\sqrt{s_{NN}})$, as a weakly model-dependent observable. Its zero crossing provides a direct signature of the phase transition critical point, offering a new tool for mapping the QCD phase diagram in future experiments.

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arXiv2026

3D-Aware Implicit Motion Control for View-Adaptive Human Video Generation

Existing methods for human motion control in video generation typically rely on either 2D poses or explicit 3D parametric models (e.g., SMPL) as control signals. However, 2D poses rigidly bind motion to the driving viewpoint, precluding novel-view synthesis. Explicit 3D models, though structurally informative, suffer from inherent inaccuracies (e.g., depth ambiguity and inaccurate dynamics) which, when used as a strong constraint, override the powerful intrinsic 3D awareness of large-scale video generators. In this work, we revisit motion control from a 3D-aware perspective, advocating for an implicit, view-agnostic motion representation that naturally aligns with the generator's spatial priors rather than depending on externally reconstructed constraints. We introduce 3DiMo, which jointly trains a motion encoder with a pretrained video generator to distill driving frames into compact, view-agnostic motion tokens, injected semantically via cross-attention. To foster 3D awareness, we train with view-rich supervision (i.e., single-view, multi-view, and moving-camera videos), forcing motion consistency across diverse viewpoints. Additionally, we use auxiliary geometric supervision that leverages SMPL only for early initialization and is annealed to zero, enabling the model to transition from external 3D guidance to learning genuine 3D spatial motion understanding from the data and the generator's priors. Experiments confirm that 3DiMo faithfully reproduces driving motions with flexible, text-driven camera control, significantly surpassing existing methods in both motion fidelity and visual quality.

cs.CV↗
arXiv2026

Effects of the centrality determination method for the equation of state and nucleonic observables from Au+Au collisions at $\sqrt{s_{NN}}$ = 2.4 GeV

Centrality determination remains one of the major sources of systematic uncertainty in intermediate-energy heavy-ion collision analyses, especially for probing the nuclear equation of state (EoS) at supra-saturation densities. To quantitatively assess the uncertainties associated with different centrality determination methods and to investigate their effects on final-state EoS-sensitive observables. Within the ultra-relativistic quantum molecular dynamics (UrQMD) model, Au+Au collisions at $\sqrt{s_{NN}}$=2.4 GeV are performed within a soft and a hard EoS. Event centrality is determined using the multiplicity of all charged particles ($M_\mathrm{ch}$) and two impact parameter-based centrality filters, one based on a geometrical interpretation and the other based on the Glauber Monte Carlo (MC) model, denoted as $b_{f}$ and $b_{r}$, respectively. It is shown that there exist significant differences between the real impact parameter distributions of event samples selected by $M_\mathrm{ch}$, $b_{f}$, and $b_{r}$, particularly between $M_\mathrm{ch}$ and $b_{r}$. When the $b_{f}$ is employed, uncertainties associated with centrality selection have a weaker influence on observables than the effects induced by the EoS. In contrast, when the $b_{r}$ is used, the influence of centrality-related uncertainties becomes more pronounced than that of the EoS. These results demonstrate that a rigorous and consistent mapping between $M_\mathrm{ch}$ and impact parameter is essential to impose quantitative constraints on the high-density nuclear EoS. Furthermore, our study indicates that the geometrical interpretation of centrality remains valid and consistent with dynamical multiplicity selection, whereas the Glauber MC-based centrality determination becomes unreliable at the investigated energy.

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arXiv2025

ChronoSelect: Robust Learning with Noisy Labels via Dynamics Temporal Memory

Training deep neural networks on real-world datasets is often hampered by the presence of noisy labels, which can be memorized by over-parameterized models, leading to significant degradation in generalization performance. While existing methods for learning with noisy labels (LNL) have made considerable progress, they fundamentally suffer from static snapshot evaluations and fail to leverage the rich temporal dynamics of learning evolution. In this paper, we propose ChronoSelect (chrono denoting its temporal nature), a novel framework featuring an innovative four-stage memory architecture that compresses prediction history into compact temporal distributions. Our unique sliding update mechanism with controlled decay maintains only four dynamic memory units per sample, progressively emphasizing recent patterns while retaining essential historical knowledge. This enables precise three-way sample partitioning into clean, boundary, and noisy subsets through temporal trajectory analysis and dual-branch consistency. Theoretical guarantees prove the mechanism's convergence and stability under noisy conditions. Extensive experiments demonstrate ChronoSelect's state-of-the-art performance across synthetic and real-world benchmarks.

cs.LG↗
arXiv2025

Probing the Three-dimension Emission Source and Neutron Skin via $π$-$π$ Correlations in Heavy-Ion Collisions

The Richardson-Lucy algorithm is applied to reconstruct the three-dimensional source function of identical pions from their two-particle correlation functions. The algorithm's performance is first evaluated through simulations with Gaussian-type initial source functions. Its imaging quality and robustness are further demonstrated with experimental data from Au+Au collisions at 1.23 A GeV, collected by the HADES Collaboration. Additionally, using UrQMD simulations of Pb+Pb collisions at 1.5 A GeV, we show that the deblurred source functions exhibit sensitivity to the initial neutron skin thickness of the colliding nuclei. This highlights the potential of the Richardson-Lucy algorithm as a tool for probing the neutron density distribution in heavy nuclei.

nucl-ex↗
arXiv2025

OpenDerisk: An Industrial Framework for AI-Driven SRE, with Design, Implementation, and Case Studies

The escalating complexity of modern software imposes an unsustainable operational burden on Site Reliability Engineering (SRE) teams, demanding AI-driven automation that can emulate expert diagnostic reasoning. Existing solutions, from traditional AI methods to general-purpose multi-agent systems, fall short: they either lack deep causal reasoning or are not tailored for the specialized, investigative workflows unique to SRE. To address this gap, we present OpenDerisk, a specialized, open-source multi-agent framework architected for SRE. OpenDerisk integrates a diagnostic-native collaboration model, a pluggable reasoning engine, a knowledge engine, and a standardized protocol (MCP) to enable specialist agents to collectively solve complex, multi-domain problems. Our comprehensive evaluation demonstrates that OpenDerisk significantly outperforms state-of-the-art baselines in both accuracy and efficiency. This effectiveness is validated by its large-scale production deployment at Ant Group, where it serves over 3,000 daily users across diverse scenarios, confirming its industrial-grade scalability and practical impact. OpenDerisk is open source and available at https://github.com/derisk-ai/OpenDerisk/

cs.SE↗
arXiv2025

Unlocking the initial neutron density distribution from the two-pion HBT correlation function in heavy-ion collisions

Revealing the neutron density distribution in the nucleus is one of the crucial tasks of nuclear physics. Within the framework of the ultrarelativistic quantum molecular dynamic model followed by a correlation afterburner program, we investigate the effects of the initial neutron density distribution on the charged-pion yield ratio $π^{-}/π^{+}$, the two-pion momentum correlation function, and the emission source dimension. It is found that the $π^{-}/π^{+}$ ratio is sensitive to the initial neutron density distribution and the impact parameter, especially for collisions at large impact parameter. However, the charge splitting in the correlation functions between positively $π^{+}π^{+}$ and negatively $π^{-}π^{-}$, as well as the source radii and volumes extracted exhibit a stronger dependence on the initial neutron density distribution, but a weaker dependence on the impact parameter. The present study highlights that $π^{+}π^{+}$ and $π^{-}π^{-}$ correlation functions in heavy-ion collisions could be used to probe the initial neutron density distribution of nuclei.

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