arXiv subjects

Xu Chen

Publications and source records attributed to Xu Chen.

At least 73 records · Page 4Linked to original sources

arXiv2026

Emergent interweaved CDW unoccupied states in hole-doping LaTe2 with element substitution

Multiple CDW-ordered layered rare-earth tellurides have increasingly emerged as a research hotspot, owing to their unconventional CDW formation, high transition temperature, and confirmed existence of axial Higgs modes. Recently, interweaved CDW in LaTe2 and its element-substituted phase LaTe2-xSbx have been investigated through TEM and ARPES measurements, revealing their distinct origins. Nevertheless, several complex diffraction features observed in TEM patterns remain unelucidated. In this work, we carried out scanning tunneling microscopy (STM) on LaTe1.6Sb0.4 crystals at 9 K. Three interweaved CDW wave vectors, q1=8/11a*, q2=5/11a* and q3=3/11a* were observed, which are induced by hole doping in unoccupied states. The q1 and q3 are theoretically verified to be nesting vectors connecting px- and py- bands. Furthermore, the satellite spots relative to the main Bragg spots p corresponding to a 11-a-superlattice have also been detected. Our findings provide critical insights for further exploring the origin of the interweaved CDW in hole/electron doping materials.

cond-mat.str-el↗

arXiv2026

ECHO: Ego-Centric modeling of Human-Object interactions

Modeling human-object interactions (HOI) from an egocentric perspective is a critical yet challenging task, particularly when relying on sparse signals from wearable devices like smart glasses and watches. We present ECHO, the first unified framework to jointly recover human pose, object motion, and contact dynamics solely from head and wrist tracking. To tackle the underconstrained nature of this problem, we introduce a novel tri-variate diffusion process with independent noise schedules that models the mutual dependencies between the human, object, and interaction modalities. This formulation allows ECHO to operate with flexible input configurations, making it robust to intermittent tracking and capable of leveraging partial observations. Crucially, it enables training on a combination of large-scale human motion datasets and smaller HOI collections, learning strong priors while capturing interaction nuances. Furthermore, we employ a smooth inpainting inference mechanism that enables the generation of temporally consistent interactions for arbitrarily long sequences. Extensive evaluations demonstrate that ECHO achieves state-of-the-art performance, significantly outperforming existing methods lacking such flexibility. The project page is available at https://ptrvilya.github.io/echo/.

cs.CV↗

arXiv2026

Clustering-Embedded Model Predictive Path Integral Control: Avoiding Averaging-Induced Failure and Enabling Efficient Cluster Selection for Dynamic Obstacles

With the widespread availability of parallel computing hardware, sampling-based motion planning methods such as Model Predictive Path Integral (MPPI) control have become increasingly powerful for complex nonlinear systems in non-smooth task spaces. However, the sampling and forward-simulation pipeline in MPPI suffers from averaging-induced failure in cluttered environments, where the importance-weighted update averages incompatible rollouts and leads to hesitation or even collision when an obstacle lies directly ahead. This paper proposes Clustering-Embedded MPPI (CE-MPPI), a framework that architecturally resolves the averaging-induced failures inherent in standard MPPI within non-convex environments. Rather than simply mitigating interference, CE-MPPI redefines the control law by integrating a high-fidelity pruning and clustering stage. By leveraging density-based spatial clustering of applications with noise (DBSCAN) alongside a novel geometric direction feature that is extracted from collision-derived reference points, the system isolates feasible trajectory modes from the noise of infeasible rollouts. This is paired with an intelligent selection logic that optimizes for minimum cost in static scenes while actively steering opposite to obstacle flux in dynamic environments. Experiments in 2-D JAX-accelerated simulations show that CE-MPPI alleviates obstacle-front hesitation and avoids persistent coupling with moving obstacles in dynamic scenes. In particular, real-world tests on a 6-DoF UR5e manipulator with CUDA-parallel rollouts in Isaac Gym achieve a 48\% reduction in time-to-goal and a 12\% shorter end-effector path.

cs.RO↗

arXiv2026

Model-Guided Local Bayesian Optimization for Tuning of Interpretable Controllers in Injection Molding

Advanced control methods have proven effective for controlling cavity pressure, a key determinant of part-quality attributes, in the plastics injection molding process. However, the abstract nature of the resulting control laws makes them difficult to interpret in a production environment, thereby limiting adoption in industrial applications. Additionally, controller optimization poses a severe challenge due to the diversity of mold geometries and materials. We propose a method to automatically optimize interpretable controllers during manufacturing while being cycle-efficient and risk-aware. The approach uses a Physics-Inspired Neural Mixture-of-Local-Experts model of the injection molding dynamics and augments its simulated closed-loop costs with a residual Gaussian Process, enabling Local Bayesian Optimization of controller parameters. We benchmark the algorithm against Vanilla Bayesian Optimization (BO) in simulation, using three controllers with parameter counts ranging from 1 to 30. Using the local method, we identify controller parameters that yield costs comparable to or lower than those of global BO over 20 optimization iterations, while mitigating high-cost excursions during tuning.

eess.SY↗

arXiv2026

Highly anisotropic nonrelativistic charge-to-spin conversion in altermagnets

The charge-to-spin conversion provides an efficient way to manipulate the magnetization by electrical means. In this work, we report on a study on the anisotropic nonrelativistic charge-to-spin conversion response to the current direction in altermagnets. We prove that the spin conductivity dictating the charge-to-spin conversion is equivalent to the spin-polarized conductivity difference in the nonrelativistic limit. Based on the general spin-group analysis, we derive analytical expressions for the anisotropic conversion ratio and identify its maximum value. We then exemplify those phenomena in representative altermagnets based on the density functional theory calculations. The highly anisotropic charge-to-spin conversion efficiency, varying from zero to several tens of percent, was demonstrated. Our work shines more light on the exploration of the nonrelativistic generation of spin currents in altermagnets.

cond-mat.other↗

arXiv2026

Electric-Field Switchable Magnetic Spin Hall Effect

It is established that the polarity of a time-reversal-odd ($\mathcal{T}$-odd) physical quantity can be reversed under the $\mathcal{T}$ operation. Here, we use the spin-group analysis to directly demonstrate that the $\mathcal{T}$-odd magnetic spin Hall effect in ferroelectric altermagnets can be switchable by electric fields beyond the $\mathcal{T}$ operation. This arises from the ferroelectric switching of the nonrelativistic spin splitting, which swaps the roles of spin up and down channels in the reciprocal space. As a result, the $\mathcal{T}$-odd spin conductivity that are proportional to the spin-polarized conductivity difference reverses its polarity upon polarization switching. We identify spin-group operations to switch both the polarization and the magnetic spin Hall effect simultaneously for non-centrosymmetric spin point groups. Then, we exemplify those phenomena in the ferroelectric altermagnet VOI$_2$ monolayer based on density functional theory calculations and an effective Hamiltonian analysis. Our findings not only provide novel strategies to switch the magnetic spin Hall effect using the dissipation-free electric field but also open a promising avenue for electrically programmable spintronic devices.

cond-mat.other↗

arXiv2026

Towards Fast Domain Adaptation and Fine-Grained User Simulation for Evaluating Conversational Recommender Systems

Conversational Recommender Systems (CRSs) enhance user experience through multi-turn interactions, yet evaluating their performance remains challenging. While Large Language Model (LLM) based user simulators are effective, they suffer from three key limitations: (1) Lack of Domain Adaptability: Reliance on fixed prompts and predefined action spaces hinders transfer to novel domains; (2) Limited User Modeling: Inability to accurately replicate subtle linguistic styles and dynamic preferences; (3) Insufficient Evaluation Validity: Existing simulators fail to adequately assess fundamental capabilities and system robustness. To overcome these, we propose AdaptSim, an Adaptive domain and automatic prompt tuning User Simulator. AdaptSim offers an efficient framework for evaluating CRSs by enabling realistic behavior modeling and diverse style generation. It leverages automatic prompt generation and an open action mechanism to reduce manual effort and improve cross-domain flexibility. For response generation, we employ controlled text generation with a "think-then-respond" strategy for fine-grained control over language style. For CRS evaluation, AdaptSim incorporates a novel Breadth-First Search (BFS)-based, turn-level pairwise comparison framework for comprehensive assessment. Extensive experiments across three domains and four LLMs demonstrate that AdaptSim generates realistic dialogues, enabling a highly effective and reliable evaluation of CRS capabilities and robustness.

cs.IR↗

arXiv2026

Anomalous charge density wave in a two-dimensional superatomic superconductor

The spatial modulation of electron density into a wave-like pattern, known as charge density wave (CDW), represents a fundamental quantum state that often coexists with superconductivity, quantum Hall states, axion insulating phases and etc. Conventional CDWs are mediated by longitudinal acoustic phonons, exhibit picometer-scale lattice distortions ($10^{-12}$--$10^{-11}$ m), and typically vanish approaching the atomic limit. Here, we report a series of anomalous CDW behaviors in the 2D superatomic superconductor Au$_6$Te$_{12}$Se$_8$. Remarkably, its CDW is governed by transverse phonons, accompanied by an extraordinarily high real-space displacement of $\sim 4$ Ångström. Furthermore, we observe an exotic dimensional response persisting up to micrometer-scale thickness, a regime where other materials are already considered as bulk. Through liquid helium-temperature transmission electron microscopy, ultrafast pump-probe spectroscopy and transport measurements, we demonstrate a dramatic enhancement of the CDW transition temperature ($T_{\text{CDW}}$) from $<2$ K in the bulk to 110 K in approaching the ``superatomic limit''. Our findings not only reveal novel facets of both CDW and superatomic materials, but the competition between this anomalous CDW and superconductivity opens avenues for exploring unconventional electron-phonon interactions.

cond-mat.supr-con↗

arXiv2026

High-Fidelity 4D Hand-Object Capture via Multi-View Spatiotemporal Tracking and Physics-Aware Gaussians

The growing demand for high-fidelity 4D hand-object interaction (HOI) data in embodied AI and spatial computing is currently bottlenecked by the reliance on pre-scanned object templates and physical markers. While recent methods have demonstrated promising results in reconstructing 4D hand-object interaction from videos, they are highly sensitive to initial estimates of hand and object poses. Yet, estimating these poses from images is challenging, in particular under severe occlusion which is inherent in hand-object interaction scenarios. We propose a novel system for the robust and accurate reconstruction of hands and objects from synchronized and calibrated multi-view videos without requiring any templates or markers. Our system consists of two main components with key innovations: (1) a multi-view feed-forward transformer model that aggregates cross-view geometry and temporal cues to provide a reliable, metric-consistent initialization for both poses and dense object geometry, and (2) a hand-object physics-aware Gaussian-based optimization framework to refine the initial estimates, integrating tetrahedral constraints, collision refinement, and appearance decomposition to produce physically plausible and visually accurate reconstruction. Validated on public benchmarks and an extensive internal dataset, our pipeline achieves highly robust, artifact-free reconstruction, providing an efficient foundation for automated 4D asset generation. Our project page are available at https://zyshen021.github.io/HOSTPG/.

cs.CV↗

arXiv2026

Admittance-Based Surface Alignment for Human-in-the-Loop Robotic Visual Inspection

Precision visual inspection underpins quality assurance across aerospace, semiconductor, and medical manufacturing, where undetected surface anomalies on high-value parts translate directly into scrap, rework, and field failures. Robotic visual inspection requires precise alignment between the end-effector and local surface geometry in the presence of perception noise and surface irregularities. In industrial settings, a human operator is often kept in the loop via teleoperation or shared autonomy, introducing real-time adjustments that render purely offline motion planning inadequate. This motivates control architectures capable of reactive, compliant behavior under combined human and perceptual uncertainty. This paper presents a novel real-time, closed-loop robotic orientation control pipeline for precision visual inspection, with an admittance-based framework that unifies operator input and perception-driven surface alignment. We design the end-effector as a virtual sphere moving through a viscous medium, such that the resulting physically interpretable mass--damper system generates synchronized, compliant motion from orientation error and operator commands. We validate the framework on a 6-DOF manipulator demonstrating stable normal-tracking and a final mean orientation error of 0.4°.

cs.RO↗

arXiv2026

OneRank: Unified Transformer-Native Ranking Architecture for Multi-Task Recommendation

Multi-task learning (MTL) is essential in recommender systems to enable complementary learning among diverse user feedback. While modern industrial practices have shifted from DNNs to Transformer-centric architectures to strengthen sequence modeling and scaling capacity, they still decouple feature encoding from multi-task prediction, treating the Transformer as a task-agnostic encoder. This design fundamentally limits the performance and scalability by (1) creating an information bottleneck under heterogeneous task objectives, (2) inducing gradient interference that leads to the seesaw phenomenon, and (3) forcing a dataflow transition in which attention-based, context-adaptive representation learning is converted to static feed-forward task prediction with incompatible information read-write dynamics. We propose OneRank, a Transformer-native multi-task ranking framework that eliminates encoder-predictor separation and introduces task-private channels for forward representation learning and backward optimization, enabling task-specialized learning while reducing inter-task interference. In the forward pass, OneRank learns task-specific representations bottom-up through task-conditioned information selection, candidate-aware contextualization, and controlled cross-task interaction. In the backward pass, cross-task gradient detachment isolates task-private parameter updates from shared knowledge extraction modules, preventing negative transfer. We further replace static task-specific MLP scorers with dynamic matching-based scoring for context-aware personalized ranking. By internalizing multi-task reasoning within the Transformer stack, OneRank establishes a unified and scalable architectural paradigm. Offline and online experiments on large-scale industrial datasets show that OneRank significantly outperforms state-of-the-art baselines while maintaining computational efficiency.

cs.IR↗

arXiv2026

Benchmarking LLMs for Community Governance Simulation with Life-history Narratives

Effective community governance hinges on understanding what specific residents think and need. Recent work has used large language models (LLMs) to simulate human respondents, offering a scalable, reproducible way to study human attitudes and behaviors at low cost. However, these studies typically prompt the model with just a few demographic variables (age, gender, income), simulating only general role types. This is insufficient for community governance, where decisions depend on the views of specific residents. We bridge this gap with an integrated research framework covering dataset, benchmark, algorithm, and system. The dataset comprises approximately 1.2 million characters of first-person narrative collected through two-hour semi-structured interviews with each of 92 residents in an urban community, organized around nine community-governance domains. The benchmark probes 18 mainstream LLMs across four prompting strategies and shows that adding rich life-history profiles meaningfully raises fidelity above the no-profile baseline, but this gain comes with more input tokens per call from the longer prompts they require. The algorithm, curriculum-LoRA, is a parameter-efficient personalization framework that, by closing this fidelity-cost gap, matches the strongest baseline's fidelity at roughly 10x lower per-call cost and Pareto-dominates every configuration tested. The system integrates curriculum-LoRA into a closed-loop policy-evaluation pipeline. Together, these results bring individual-level LLM-based resident simulation within reach of resource-constrained local administrations, enabling community-governance decisions to be systematically pre-evaluated in silico before real-world deployment.

cs.CY↗

arXiv2026

Rosetta Memory: Adaptive Memory for Cross-LLM Agents

Memory is the key component for transforming a stateless LLM into a persistent, evolving agent through experience accumulation, long-horizon planning, and continual self-improvement. Existing memory systems typically take the LLM as the center and design memory operations tailored to a specific backbone. In practice, however, users frequently switch between LLMs, for example using Claude for coding and GPT for writing across tasks, or routing different steps to different backbones within a single task for cost-effective trade-offs. As a result, memory written by one model often needs to be consumed by another. Making upstream memory effectively adapt to and activate downstream LLMs remains a critical yet underexplored problem. To bridge this gap, we shift the perspective from LLM-centric memory design to \emph{memory-centric LLM adaptation}. Specifically, we approach the above upstream-downstream memory adaptation problem from both the write and read sides, and design two profile-conditioned operators that are jointly trained to optimize how memory is stored and presented for better task completion. To ensure the learned operators generalize across a broad set of LLMs, we propose a minimum-gain sampling curriculum that prioritizes the least-served LLMs during training. To better measure the operators' actual contribution rather than the LLM's own capability, we design a performance-gap reward that compares against a naive memory baseline. Experiments on HotpotQA, 2WikiMultihopQA, and MuSiQue demonstrate that our model consistently outperforms baselines and remains robust under unseen-model replacement.

cs.LG↗

arXiv2026

DELTAMEM: Incremental Experience Memory for LLM Agents via Residual Trees

Large Language Model (LLM)-based agents increasingly rely on memory to learn from experiences over continual interactions. However, storing experiences as independent, flat units leads to substantial redundancy and retrieval conflicts, as similar episodes repeat overlapping content and subtle scene variations cause retrieved memories to offer contradictory guidance. To address this, we introduce residual experience, positing that newly acquired experience is often an incremental variation of existing knowledge. We propose DeltaMem, a framework that organizes experience memory into two independent residual trees, one storing goal-conditioned task experience as reusable skills and another for scene-level environment knowledge. Each tree uses a root node for generalized base experiences and incremental delta nodes for subsequent variations, allowing related experiences to share a common foundation without duplication. For retrieval, a failure-penalized similarity scan locates the best match, reconstructing the full experience via root-to-match chain composition. An autonomous consolidation mechanism distills high-frequency paths into new root nodes, enabling the trees to self-organize from general heuristics to specialized variants. Experiments across diverse interactive environments show that DeltaMem consistently outperforms existing baselines. To facilitate future research, we release the code at https://github.com/import-myself/DeltaMem.

cs.AI↗

arXiv2026

SMAC: Spatial-Modal Joint Modeling and Adaptive Representation Collapse for Multimodal Object Tracking

Multimodal multi-object tracking (MOT) under complex illumination remains challenging due to insufficient joint modeling of spatial and modal features and the limited adaptability of fixed fusion strategies. To address these issues, this paper proposes a spatial-modal convolution fusion and distillation-prompt-based multimodal MOT framework. A spatial-modal fusion backbone is first constructed, where a Basic module performs spatial feature extraction and modal interaction via decoupled 3D convolution, while a Mixed module models nonlinear cross-modal correlations through amplitude-phase decomposition. In addition, a representation collapse network is designed for adaptive multimodal fusion. A Distillation Prompt Guidance (DPG) module generates dynamic modal weights under teacher supervision, and a Global Modal Difference Aggregation (GMDA) module preserves discriminative information during multimodal representation collapse. Extensive experiments on the UniRTL dataset demonstrate the effectiveness of the proposed method. The proposed tracker achieves 63.31 HOTA and 79.21 MOTA on the RNT modality, outperforming several state-of-the-art methods while maintaining favorable inference efficiency. The source code and pretrained models are publicly available at https://github.com/QitaiSun/SMAC.

eess.IV↗

arXiv2026

SCL: Towards Domain Generalization via Single-Temporal Multimodal Contrastive Learning for Remote Sensing Change Detection

In recent years, change detection and anomaly detection models based on CNN and transformer have achieved remarkable success across various datasets based on paired data. However, most such methods exhibit limited crossdataset generalization due to domain-specific designs and typically rely on large amounts of paired labeled data. In this paper, based on visual-language pre-training model, we introduce a Single-temporal multimodal Contrastive Learning (SCL) foundation models for change detection without training on the target dataset. To further improve the model's ability to learn context of textual and visual information, we propose a Dynamic Text-vision Context Optimization (DTCO) module for prompt learning. Meanwhile, to address the data dependency issue of existing methods, we introduce a controllable generation and Single-temporal trAINing strategy (SAIN). This allows us to train the model using a large number of existing single-temporal images without the need for paired label. Extensive experiments on various realworld change detection datasets demonstrate the superior performance and generalization of SCL, outperforming state-of-the-art methods under the evaluated settings. Code is available at https://github.com/Kane-Du/scl-cd.git.

cs.CV↗

arXiv2026

TwinQuant: Learnable Subspace Decomposition for 4-Bit LLM Quantization

4-bit quantization reduces the memory footprint and latency of large language model inference, but its aggressive precision reduction can severely degrade accuracy. Prior methods address this by decomposing each weight matrix into two components (e.g., via singular value decomposition) and quantizing them separately, assigning the bulk of values to a low-precision residual component while handling outliers with a high-precision low-rank component. However, such decompositions are designed to minimize the real-valued energy of the residual, rather than the post-quantization error of the residual and low-rank components. We propose TwinQuant, a 4-bit quantization framework that learns quantization-friendly decomposed subspaces and jointly reshapes both the low-rank and residual components. TwinQuant learns component-specific transformations via a joint optimization over the Stiefel and general linear manifolds, flattening their distributions and reducing dynamic-range imbalance. To enable efficient end-to-end execution, we further design a fused dual-component kernel that pipelines the two-stage low-rank computation on-chip and merges both components with a single epilogue, avoiding intermediate global-memory traffic. Across LLaMA3 and Qwen3 models, TwinQuant preserves near-FP16 accuracy and delivers up to $1.8\times$ end-to-end speedup over an FP16 baseline.

cs.DC↗

arXiv2026

Breaking-induced energy dissipation of surface gravity waves at varying scales and co-flowing wind stresses

Breaking-induced energy dissipation is studied for individual unsteady breaking waves using laboratory measurements of unidirectional surface gravity wave groups across a range of wave scales and wind stresses. A refined framework to estimate breaking-induced dissipation $ΔE_{br}$ is proposed that accounts for background dissipation from non-breaking processes. Using this framework, we show that variations in wave scale primarily influence breaking energetics, such as fractional dissipation $ΔE_{br}/E_0$ and dissipation rate $ε_b$, by modifying the breaking onset threshold. Also, co-flowing wind systematically reduces both $ΔE_{br}/E_0$ and $ε_b$ relative to unforced conditions, as wind-forced waves break earlier with reduced crest forward-leaning. Exploiting the crest-front steepness at incipient breaking $\mathcal{S}_{\text{front}}(t_b)$ to characterise breaking onset and local crest geometry, we formulate a scaling for $ε_b$ based on this local measure. This then yields $ΔE_{br}/E_0 \propto β^{*}\,\mathcal{S}_b\,(τ_b/T_b)$, where $β^{*}$ is crest forward leaning, $\mathcal{S}_b$ local steepness, and $τ_b/T_b$ non-dimensional breaking duration. This scaling highlights the important roles of crest asymmetry and breaking duration in setting the breaking energy dissipation. Finally, we consider the breaking strength parameter $b$ by assessing existing steepness-based scaling laws, and relate $b$ to $\mathcal{S}_{\text{front}}(t_b)$, yielding an approximately linear dependence once the breaking-onset threshold is considered.

physics.flu-dyn↗