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Li Ma

Publications and source records attributed to Li Ma.

At least 37 records · Page 2Linked to original sources
arXiv2026

D$^3$-Subsidy: Online and Sequential Driver Subsidy Decision-Making for Large-Scale Ride-Hailing Market

Ride-hailing platforms like DiDi Chuxing operate in highly dynamic environments where balancing driver supply and passenger demand is critical. Although driver-side subsidies serve as a primary lever to align these forces and improve key KPIs like completed rides (\texttt{Rides}) and gross merchandise value (\texttt{GMV}), optimizing them in production requires simultaneously meeting three constraints: (i) responsiveness to stochastic shocks, (ii) strict subsidy-rate caps, and (iii) low-latency execution at city scale. These requirements rule out expensive per-order optimization, calling for a forward-looking, constraint-aware city-level controller for online sequential decision making. To meet these requirements, we introduce D$^3$-Subsidy (Dynamic Driver-side Diffusion-based Subsidy), a hierarchical diffusion-based framework for deployable city-wide subsidy control. To bridge the train-inference gap, D$^3$-Subsidy employs a prefix-conditioned diffusion model that samples plausible future trajectories from immutable historical observations, ensuring the training protocol aligns with the fixed-history nature of online deployment. These generated plans are then decoded by a context-conditioned inverse module into low-dimensional city-level control signals. For scalable execution, we bridge the gap between city-level planning and fine-grained dispatch via a Lagrangian-dual-derived mapping, which embeds subsidy-rate caps directly into order-driver incentives without iterative optimization. Additionally, a multi-city pretraining strategy with parameter-efficient fine-tuning enables robust transfer across heterogeneous cities. Extensive offline evaluations demonstrate that D$^3$-Subsidy improves \texttt{Rides} and \texttt{GMV} while enhancing cap compliance, and a real-world A/B test confirms significant uplift while keeping budget-related violation metrics within operational thresholds.

cs.LG↗
arXiv2026

Lighting in Motion: Spatiotemporal HDR Lighting Estimation

We present Lighting in Motion (LiMo), a diffusion-based approach to spatiotemporal lighting estimation. LiMo targets both realistic high-frequency detail prediction and accurate illuminance estimation. To account for both, we propose generating a set of mirrored and diffuse spheres at different exposures, based on their 3D positions in the input. Making use of diffusion priors, we fine-tune powerful existing diffusion models on a large-scale customized dataset of indoor and outdoor scenes, paired with spatiotemporal light probes. For accurate spatial conditioning, we demonstrate that depth alone is insufficient and we introduce a new geometric condition to provide the relative position of the scene to the target 3D position. Finally, we combine diffuse and mirror predictions at different exposures into a single HDRI map leveraging differentiable rendering. We thoroughly evaluate our method and design choices to establish LiMo as state-of-the-art for both spatial control and prediction accuracy.

cs.CV↗
arXiv2026

BodyReLux: Temporally Consistent Full-Body Video Relighting

Being able to relight human performance is a fundamental task for post production and content creation. We present BodyReLux, a subject-specific video diffusion-based framework for relighting full-body human performances in a temporally consistent way. Our model is trained on a hybrid dataset of pixel-aligned video relighting pairs, covering a diverse combination of lighting conditions, performances and viewpoints. To acquire such dataset, we combine traditional static One-Light-at-a-Time (OLAT) capture and a novel dynamic performance capture in which two smoothly varying lighting sequences are rapidly interleaved. Because the lighting operates above the human flicker-fusion threshold, the interleaving does not appear to strobe. We train our video relighting model from a pretrained text-to-video model to fully leverage the generative priors for producing high quality videos. To achieve accurate lighting control, we introduce a new lighting conditioning method that represents each light source as a token. We further condition on sequences of lighting using masked attention to support dynamic lighting control. Together with a carefully designed data augmentation pipeline, we achieve photorealistic, robust, and temporally consistent video relighting of subject-specific human performances.

cs.CV↗
arXiv2026

A tree-based kernel for densities and its applications in clustering DNase-seq profiles

Modeling multiple sampling densities within a hierarchical framework enables borrowing of information across samples. These density random effects can act as kernels in latent variable models to represent exchangeable subgroups or clusters. A key feature of these kernels is the (functional) covariance they induce, which determines how densities are grouped in mixture models. Our motivating problem is clustering chromatin accessibility profiles from high-throughput DNase-seq experiments to detect transcription factor (TF) binding. TF binding typically produces footprint profiles with spatial patterns, creating long-range dependency across genomic locations. Existing nonparametric hierarchical models impose restrictive covariance assumptions and cannot accommodate such dependencies, often leading to biologically uninformative clusters. We propose a nonparametric density kernel flexible enough to capture diverse covariance structures and adaptive to various spatial patterns of TF footprints. The kernel specifies dyadic tree splitting probabilities via a multivariate logit-normal model with a sparse precision matrix. Bayesian inference for latent variable models using this kernel is implemented through Gibbs sampling with Polya-Gamma augmentation. Extensive simulations show that our kernel substantially improves clustering accuracy. We apply the proposed mixture model to DNase-seq data from the ENCODE project, which results in biologically meaningful clusters corresponding to binding events of two common TFs.

stat.ME↗
arXiv2026

An Embarrassingly Simple Graph Heuristic Reveals Shortcut-Solvable Benchmarks for Sequential Recommendation

Sequential recommendation has increasingly shifted toward generative recommenders that combine sequential patterns with semantic item information. Yet these methods are often evaluated on a small set of widely used benchmarks, raising a key question: do these benchmarks actually require the advanced modeling capabilities that modern generative recommenders claim to provide? We conduct a benchmark audit with an intentionally simple graph heuristic. Starting from only the last one or two interacted items, it retrieves candidates from a few-hop item-transition graph and ranks them by item-feature similarity. Despite using no sequence encoder, generative objective, or training, this heuristic matches or outperforms many modern baselines, with relative NDCG@10 improvements of 38.10% and 44.18% over the best competing baseline on Amazon Review Sports and CDs. We show that this behavior reflects shortcut solvability rather than an artifact of one heuristic. We identify three shortcut structures that can make next-item prediction easier than expected: low-branching local transitions, feature-smooth transitions, and limited dependence on long user histories. These shortcuts need not appear together; even one or two strong signals can make simple local retrieval highly competitive, while weakening them makes the benefits of more sophisticated models clearer. Across 14 datasets, model rankings vary substantially with dataset properties, yet the heuristic remains competitive on 10 of them. Our findings suggest that strong performance on standard benchmarks does not always demonstrate advanced sequential, semantic, or generative modeling ability. We call for more careful dataset selection and dataset-level diagnostic analysis when using benchmarks to support claims about new recommendation models.

cs.IR↗
arXiv2026

RetimeGS: Continuous-Time Reconstruction of 4D Gaussian Splatting

Temporal retiming, the ability to reconstruct and render dynamic scenes at arbitrary timestamps, is crucial for applications such as slow-motion playback, temporal editing, and post-production. However, most existing 4D Gaussian Splatting (4DGS) methods overfit at discrete frame indices but struggle to represent continuous-time frames, leading to ghosting artifacts when interpolating between timestamps. We identify this limitation as a form of temporal aliasing and propose RetimeGS, a simple yet effective 4DGS representation that explicitly defines the temporal behavior of the 3D Gaussian and mitigates temporal aliasing. To achieve smooth and consistent interpolation, we incorporate optical flow-guided initialization and supervision, triple-rendering supervision, and other targeted strategies. Together, these components enable ghost-free, temporally coherent rendering even under large motions. Experiments on datasets featuring fast motion, non-rigid deformation, and severe occlusions demonstrate that RetimeGS achieves superior quality and coherence over state-of-the-art methods.

cs.CV↗
arXiv2026

Mn substitution induced a ferrimagnetic to ferromagnetic transition in trigonal $\text{Cr}_5\text{Te}_8$

Tailoring the magnetic properties of chromium tellurides via heterointercalation with extrinsic transition metals remains largely unexplored. Here, we report a comprehensive investigation of trigonal Cr$_5$Te$_8$ and Cr$_4$MnTe$_8$ single crystals, in which Mn substitution elevates the magnetic ordering temperature from 226 to 249 K and enhances the saturation magnetic moment per magnetic ion ($m_{\text{S}}$) from 2.00 to 2.66 $μ_{\text{B}}$ at 5 K. Remarkably, the observed $m_{\text{S}}$ enhancement significantly exceeds the contribution of Mn ion moments alone, indicating the relief of intrinsic spin compensation within the parent lattice. First-principles calculations definitively establish that pristine $\text{Cr}_5\text{Te}_8$ exhibits ferrimagnetic ordering with a computed $m_\text{S}$ of 1.98~$μ_\text{B}$, and further reveal that preferential occupation of the van der Waals gaps by Mn ions induces a ferrimagnetic-to-ferromagnetic transition, yielding a predicted $m_\text{S}$ of 2.94~$μ_\text{B}$. These findings not only resolve the magnetic ground state of trigonal Cr$_5$Te$_8$ but also identify heterointercalation as a robust strategy for engineering the spin textures of chromium tellurides.

cond-mat.mtrl-sci↗
arXiv2026

Two-sample comparison through additive tree models for density ratios

The ratio of two densities provides a direct characterization of their differences. We consider the two-sample comparison problem by estimating this ratio given i.i.d. observations from two distributions. To this end, we propose additive tree models for density ratio estimation along with efficient algorithms using a new loss function, the balancing loss. The loss allows tree-based models to be trained using several algorithms originally designed for supervised learning, such as forward-stagewise optimization and gradient boosting. Moreover, the balancing loss resembles an exponential family kernel, and it can serve as a pseudo-likelihood with conjugate priors. This property enables generalized Bayesian inference on the density ratio using backfitting samplers designed for Bayesian additive regression trees (BART). Our Bayesian strategy provides uncertainty quantification for the inferred density ratio, which is critical for applications involving high-dimensional and data-limited distributions with potentially substantial uncertainty. We further show connections of the balancing loss to the exponential loss in binary classification and to the variational form of f-divergence, particularly the squared Hellinger distance. Numerical experiments demonstrate that our method achieves both accuracy and computational efficiency, while uniquely providing uncertainty quantification. Finally, we demonstrate its application to assessing the quality of generative models for microbiome compositional data.

stat.ME↗
arXiv2026

Multiscale Cochran-Mantel-Haenszel Scanning for Conditional Dependency

We propose a nonparametric approach to testing conditional independence and estimating conditional association, generalizing the Cochran-Mantel-Haenszel (CMH) test and odds-ratio estimator to continuous sample spaces. It leverages a multiscale scanning approach to decompose the sample space into a cascade of $2\times 2 \times T$ tables. Following the CMH test, we condition on the marginal order statistics, which are "almost ancillary" regarding conditional dependency. This strategy helps overcome a key challenge faced by other methods that discretize the sample space: we achieve consistency without requiring stratum sample sizes to grow to infinity, a constraint often difficult to satisfy in practice. Our method produces easy-to-compute test statistics with a known asymptotic null distribution under the conditional sampling model, scaling almost linearly with the sample size. Our simulation results demonstrate reliable Type I error control, even with small samples and high-dimensional conditioning, and competitive power compared to state-of-the-art tests. Finally, a case study on Uber ride-share data highlights the method's unique dual capability, inherited from the CMH, to both test and identify the nature of the inferred conditional association. By providing summary statistics that capture the strength and direction of local associations, our method offers practitioners a useful tool for learning conditional dependencies.

stat.ME↗
arXiv2026

SIM1: Physics-Aligned Simulator as Zero-Shot Data Scaler in Deformable Worlds

Robotic manipulation with deformable objects represents a data-intensive regime in embodied learning, where shape, contact, and topology co-evolve in ways that far exceed the variability of rigids. Although simulation promises relief from the cost of real-world data acquisition, prevailing sim-to-real pipelines remain rooted in rigid-body abstractions, producing mismatched geometry, fragile soft dynamics, and motion primitives poorly suited for cloth interaction. We posit that simulation fails not for being synthetic, but for being ungrounded. To address this, we introduce SIM1, a physics-aligned real-to-sim-to-real data engine that grounds simulation in the physical world. Given limited demonstrations, the system digitizes scenes into metric-consistent twins, calibrates deformable dynamics through elastic modeling, and expands behaviors via diffusion-based trajectory generation with quality filtering. This pipeline transforms sparse observations into scaled synthetic supervision with near-demonstration fidelity. Experiments show that policies trained on purely synthetic data achieve parity with real-data baselines at a 1:15 equivalence ratio, while delivering 90% zero-shot success and 50% generalization gains in real-world deployment. These results validate physics-aligned simulation as scalable supervision for deformable manipulation and a practical pathway for data-efficient policy learning.

cs.RO↗
arXiv2026

Self-Evolving Multi-Agent Framework for Efficient Decision Making in Real-Time Strategy Scenarios

Large language models (LLMs) have demonstrated exceptional potential in complex reasoning,pioneering a new paradigm for autonomous agent decision making in dynamic settings. However, in Real-Time Strategy (RTS) scenarios, LLMs suffer from a critical speed-quality trade-off. Specifically expansive state spaces and time limits render inference delays prohibitive, while stochastic planning errors undermine logical consistency. To address these challenges, we present SEMA (Self-Evolving Multi-Agent), a novel framework designed for high-performance, low-latency decision-making in RTS environments. This collaborative multi-agent framework facilitates self-evolution by adaptively calibrating model bias through in-episode assessment and cross-episode analysis. We further incorporate dynamic observation pruning based on structural entropy to model game states topologically. By distilling high dimensional data into core semantic information, this approach significantly reduces inference time. We also develop a hybrid knowledge-memory mechanism that integrates micro-trajectories, macro-experience, and hierarchical domain knowledge, thereby enhancing both strategic adaptability and decision consistency. Experiments across multiple StarCraft II maps demonstrate that SEMA achieves superior win rates while reducing average decision latency by over 50%, validating its efficiency and robustness in complex RTS scenarios.

cs.MA↗
arXiv2026

Tunable intersublattice exchange coupling drives magnetic evolution in Mn$_{3+x}$Ga$_{1-x}$C ($0 \le x \le 0.60$)

We investigate the magnetic and transport evolution in Mn$_{3+x}$Ga$_{1-x}$C ($0 \le x \le 0.60$), where Mn substitution at corner Ga sites induces lattice contraction and suppresses the antiferromagnetic order of Mn$_3$GaC. As $x$ increases, the magnetic ground state of the system undergoes a sequential transition from an antiferromagnetic state, via a canted ferrimagnetic state, to a robust ferrimagnetic state, accompanied by a surge in the magnetic ordering temperature. Saturation magnetic moments reaches a maximum of 3.63~$μ_{\mathrm{B}}$/f.u. at $x = 0.10$, whereas the topological Hall resistivity peaks at 1.47~$μΩ\cdot$cm for $x = 0.20$ before decreasing with further doping. First-principles calculations demonstrate a $\sim\!40^{\circ}$ canting of face-centered Mn moments at $x = 0.20$, signifying spin frustration, and an eventual antiparallel alignment of face-centered and corner-site Mn moments at higher $x$. These results reveal that intersublattice antiferromagnetic coupling governs the magnetic transformation and emergent transport phenomena, thus providing a microscopic foundation for designing high-ordering-temperature antiperovskites.

cond-mat.mtrl-sci↗
arXiv2026

Lattice-Expansion-Driven Stabilization of Helical Magnetic Order in Ru-Doped MnP

The practical utilization of MnP in chiral spintronic devices is fundamentally constrained by its low helical ordering temperature ($T_{\rm S}$). Here, we demonstrate that Ru substitution in Mn$_{1-x}$Ru$_x$P single crystals drives a highly anisotropic lattice expansion, where the $b$-axis elongation is one-quarter that of the $a$- and $c$-axes ($\sim$ 0.04 Å). This structural distortion profoundly stabilizes the helical ground state, elevating $T_{\rm S}$ from 51~K to 215~K and the critical field along the [010] direction at 5~K from 2.3 to 30.0~kOe, while suppressing the Curie temperature ($T_{\rm C}$) from 291~K to 215~K. Synthesizing these results with reported data on Mo- and W-doped analogues reveals that $T_{\rm S}$ and $T_{\rm C}$ are governed primarily by the $b$-axis parameter, exhibiting universal linear scaling relationships ($dT_{\rm S}/db = 1.59 \times 10^4\ \text{KÅ}^{-1}$, $dT_{\rm C}/db = 0.69 \times 10^4\ \text{KÅ}^{-1}$) far greater than those associated with the $a$- or $c$-axes. First-principles calculations reveal that the lattice expansion selectively attenuates ferromagnetic coupling while preserving antiferromagnetic interactions between nearest-neighbor Mn atoms, thereby enhancing magnetic frustration and stabilizing helimagnetism. These findings establish chemical pressure via directed $b$-axis engineering as a robust, generalizable paradigm for stabilizing helimagnetism in MnP.

cond-mat.mtrl-sci↗
arXiv2026

Recoil corrections to pentaquark molecules with an SU(3) anti-triplet heavy baryon

Recoil corrections, which appear at order $\mathcal{O}(\frac{1}{M})$, turn out to be crucial for the pentaquark molecules with heavy flavor. In the past, such corrections were typically regarded as negligible for the formation of heavy molecules. However, our research manifests the important impacts on the spectra of possible baryon-meson bound states. In certain cases, the binding energy sharply decreases after considering the recoil corrections. Using the one-boson-exchange (OBE) model, we have studied a series of double heavy and hidden heavy pentaquark states with an SU(3) anti-triplet baryon, including $Ξ_{c}\bar{D}^{(*)}$, $Λ_{c}\bar{D}^{(*)}$, $Ξ_{c}D^{(*)}$, $Λ_{c}D^{(*)}$, along with their bottom analogues. Considering both the $S$\--{}$D$ wave mixing effect and the recoil corrections, we find that recoil corrections, working against the stability of bound states in certain isospin-singlet channels, which cannot be ignored. For the $Ξ_{c}\bar{D}$ and $Ξ_{c}D$ systems with $I(J^P)=0(\frac{1}{2}^{-})$, the $Ξ_{c}\bar{D}^{*}$ and $Ξ_{c}D^{*}$ systems, as well as their bottom counterparts, with $I(J^P)=0(\frac{1}{2}^{-})$ and $0(\frac{3}{2}^{-})$, and the $Λ_{b}\bar{B}^{*}$ system with $I(J^P)=\frac{1}{2}(\frac{1}{2}^{-})$ and $\frac{1}{2}(\frac{3}{2}^{-})$, although the bound state solution can be found both with and without considering the recoil corrections, the inclusion of recoil corrections weakens the attraction of the molecules. This phenomenon arising from recoil corrections is especially prominent in the $Ξ_{c}\bar{D}^{*}$ and $Ξ_{c}D^{*}$ systems.

hep-ph↗
arXiv2026

On the number of non-cyclic subgroups of finite p-groups

Let $G$ be a finite $p$-group and $δ(G)$ denote the number of all non-cyclic subgroups of $G$. In this paper, an upper bound for $δ(G)$ is obtained. Furthermore, we prove that $δ(G)\leq δ(M_p(1, 1, 1) \times C_{p}^{n-3})$ (if $p=2$, then $δ(G)\leq δ(D_8\times C_{2}^{n-3})$), for any non-elementary abelian $p$-group $G$ of order $p^n$.

math.GR↗
arXiv2026

Giant Full-Space Anomalous Hall Effect Induced by Non-Coplanar Spin State in Mn-Rich Mn3Sn

Antiferromagnets are promising candidates for next-generation spintronic devices owing to their negligible stray fields and ultrafast spin dynamics. The noncollinear antiferromagnet $\mathrm{Mn}_{3}\mathrm{Sn}$ exhibits a large anomalous Hall effect (AHE). However, its specific noncollinear spin configuration leads to the forbiddance of the anomalous Hall conductivity from the (0001) basal plane, $σ_{(0001)}$, limiting practical applications. Here, using first-principles density functional theory, we demonstrate that Mn enrichment in $\mathrm{Mn}_{3}\mathrm{Sn}$ drives a magnetic transition from the coplanar $120^\circ$ spin configuration to a non-coplanar state with moments tilted toward the $c$-axis. This transition is primarily mediated by four-spin ring exchange interaction in the local triangular lattice, which breaks the time-reversal symmetry and generates a giant intrinsic anomalous Hall conductivity over the full three-dimensional space in $\mathrm{Mn}_{3}\mathrm{Sn}$. We predict that $σ_{(0001)}$ reaches as high as $\sim\!-468~Ω^{-1}\cdot\mathrm{cm}^{-1}$, and an enhanced $σ_{(01\bar{1}0)}$ of $\sim\!-229~Ω^{-1}\cdot\mathrm{cm}^{-1}$ is expected in light Mn self-doping of $\mathrm{Mn}_{3}\mathrm{Sn}$ ($\mathrm{Mn}_{3.125}\mathrm{Sn}_{0.875}$). Unlike previously reported mechanisms relying on external magnetic fields or strain, our approach exploits intrinsic compositional tuning to stabilize a non-coplanar magnetic ground state for realizing a strong full-space AHE in antiferromagnets, providing another viable pathway toward high-performance, low-power spintronic devices.

cond-mat.mtrl-sci↗
arXiv2026

RAG vs. GraphRAG: A Systematic Evaluation and Key Insights

Retrieval-Augmented Generation (RAG) improves large language models (LLMs) by retrieving relevant information from external sources and has been widely adopted for text-based tasks. For structured data, such as knowledge graphs, Graph Retrieval-Augmented Generation (GraphRAG) retrieves and aggregates information along graph structures. More recently, GraphRAG has been extended to general text settings by organizing unstructured text into graph representations, showing promise for reasoning and grounding. Despite these advances, existing GraphRAG systems for text data are often tailored to specific tasks, datasets, and system designs, resulting in heterogeneous evaluation protocols. Consequently, a systematic understanding of the relative strengths, limitations, and trade-offs between RAG and GraphRAG on widely used text benchmarks remains limited. In this paper, we present a comprehensive benchmark study comparing RAG and GraphRAG on established text-based tasks, including question answering and query-based summarization. We introduce a unified evaluation protocol that standardizes data preprocessing, retrieval configurations, and generation settings, enabling fair and reproducible comparisons. Our results highlight the distinct strengths of RAG and GraphRAG across different tasks and evaluation perspectives. Building on these findings, we explore selection and integration strategies that combine the strengths of both paradigms, leading to consistent performance improvements. We further analyze failure modes, efficiency trade-offs, and evaluation biases, and highlight key considerations for designing and evaluating retrieval-augmented generation systems.

cs.IR↗
arXiv2026

Reasoning by Exploration: A Unified Approach to Retrieval and Generation over Graphs

Reasoning over structured graphs remains a fundamental challenge for Large Language Models (LLMs), particularly when scaling to large graphs. Existing approaches typically follow the retrieval-augmented generation (RAG) paradigm: first retrieving subgraphs relevant to the query and then generating answers conditioned on the retrieved subgraphs. However, such two-phase pipelines often struggle to faithfully incorporate graph structure, since the generation process is ultimately constrained by the quality and completeness of the retrieved subgraph. Although many advanced retrievers have been proposed recently to mitigate this issue, they are usually tailored to the training graphs and generalize poorly to unseen graphs, which limits their practical applicability. In this work, we propose Reasoning by Exploration (RoE), a novel approach that unifies retrieval and generation by framing reasoning over graphs as a process of graph exploration. At each step, the LLM selects candidate nodes and edges to explore, gradually constructing reasoning paths and generating answers along the way. To enable effective exploration, RoE is trained in two stages: supervised fine-tuning (SFT) on gold reasoning paths, followed by reinforcement learning (RL) to enhance exploration effectiveness and generalization. Experiments on benchmark datasets demonstrate that RoE achieves substantial overall improvements over baselines, while also generalizing effectively to unseen graphs.

cs.IR↗