arXiv subjects

Xiao Li

Publications and source records attributed to Xiao Li.

At least 37 records · Page 2Linked to original sources

arXiv2026

Replica Thresholds for Stripeless Erasure Coding Based on Symmetric Block Designs

This paper investigates a fundamental question in stripeless erasure coding based on symmetric balanced incomplete block designs (SBIBDs): how many replicas per object are precisely required to guarantee recovery from any set of at most $p$ node failures? We refine the known sufficient recovery guarantee for the generalized SBIBD $(v,k,λ)$ construction. One replica is necessary and sufficient for $p=1$, and $q=λ(p-1)+2$ replicas ($q\le k$) guarantee recovery for $p\ge2$. Recovery takes one round for $p=2$ and at most two rounds in general. To study the tightness of this count, we define the universal replica threshold $q^\ast(A,p)$ for a fixed zero-diagonal SBIBD representative~$A$. We determine $q^\ast(A,p)$ for $p=1$ and $p=2$. For $p\ge3$, we identify pairwise separated sets and private target permutations as the structures determining whether the sufficient count is tight or can be further reduced. These conditions give exact thresholds for all but the case where $λ>1$ and $A$ contains no pairwise separated set of size~$p$. We improve the bounds for this remaining case and leave its exact threshold open. Finally, we give sufficient conditions for pairwise separated sets and show how affinity relabeling can realize private target permutations.

cs.IT↗

arXiv2026

Capable yet Parsimonious: Extracting and Characterizing Hidden Chain-of-Thought in Frontier Models

The rapid capability gains of frontier language models are widely attributed to improved reasoning abilities, yet this cannot be verified as raw CoT traces in closed-source systems are hidden. By registering a simple custom tool through a standard API feature, we induce frontier models to externalize intermediate reasoning. Because these traces may reflect post-hoc rationalization rather than genuine reasoning, we first evaluate against native CoT on open-source models and extend to closed-source frontier models including GPT-6 Astra. We find that the extracted reasoning matches native reasoning performance and substantially outperforms no-reasoning baselines, across competition mathematics, science, and code generation. We then characterize how frontier models structure their intermediate reasoning. Across token efficiency, reasoning-step types, and induced reasoning trees, we identify systematic differences in how models externalize, compress, and organize reasoning. We find that Astra exhibits token-efficient directed reasoning, selecting a correct trajectory earlier, while resolving elementary steps internally and externalizing only crucial reasoning. These findings provide a behavioral lens on frontier-model reasoning beyond benchmark scores.

cs.CL↗

arXiv2026

AT2019aalc: An obscured tidal disruption event candidate in an active galactic nucleus revealed by its first flare?

AT2019aalc is considered a repeating tidal disruption event (TDE) candidate occurring in an active galactic nucleus (AGN). In this paper, we highlight previously unnoticed but intriguing features that include a brief optical dimming prior to the rise of the main flare and a significantly lower post-flare luminosity compared to the pre-flare level. By applying a time-dependent obscured TDE model to AT2019aalc, we successfully reproduce its light curves with the tidal disruption of a $1.122_{-0.176}^{+0.246}\rm M_\odot$ main-sequence star by a $1.862_{-0.259}^{+0.255} \times 10^7{\rm M_\odot}$ supermassive black hole (SMBH). Both the pre-flare dimming and the reduced post-flare luminosity are attributed to obscuration of the intrinsic AGN emission. The total energy of the event derived from our fit is $1.24\times10^{51}$ ergs and about 0.2 ${\rm M_\odot}$ of debris mass is accreted by the SMBH. In addition, the $g-r$ color of the net flare in AT2019aalc exhibits a continuous reddening trend during the first flare, consistent with a gradual increase in $E(B-V)$ predicted by the obscured TDE model. These results suggest that AT2019aalc can be considered a second member of the recently proposed class of obscured TDE candidates hosted in AGNs. } \keywords{galaxies: active-galaxies: nuclei-quasars: supermassive black holes-quasars: individual (AT2019aalc)-transients: tidal disruption events.

astro-ph.HE↗

arXiv2026

Morley element for the linear strain gradient elasticity problem

We establish a discrete $H^1$-Korn inequality for finite element spaces satisfying a second order weak continuity condition. This implies that classical $H^2$-nonconforming elements satisfy the discrete Korn inequality. Based on this result, we develop a stabilized Morley finite element method for linear strain gradient elasticity using only piecewise quadratic polynomials. Thanks to the discrete $H^1$-Korn inequality, this method does not require any penalty parameter. We prove that the method is robust with respect to both $λ$ and $ι$. Numerical experiments confirm the theoretical results.

math.NA↗

arXiv2026

Fewer Steps, Better Actions: Rethinking Flow-Matching Inference for VLA Policies

Vision-language-action (VLA) policies based on flow matching generate action chunks through repeated evaluations of an action expert. Increasing the number of integration steps raises inference cost, but does not necessarily improve closed-loop success. We propose Coda, which reallocates part of this integration budget to a single learned endpoint correction. A frozen policy first completes a few-step noise-to-action trajectory; a lightweight Transformer then predicts a demonstration-supervised residual using the candidate action, source noise, and shared observation-prefix cache. Only the corrector is trained. On 50 RoboTwin Easy tasks, five-step Coda improves success from 71.64% to 74.68% over the matched five-step baseline, while reducing forward latency by 30.2% relative to the default ten-step policy. A two-step configuration achieves 71.88% success with a 2.12$\times$ speedup. An independent 13-task control shows a 5.69-percentage-point gain at nearly equal latency, supporting correction as an effective alternative to additional integration. The same design also improves frozen official SmolVLA, raising two-step success from 60.8% to 69.4%. These results show that endpoint correction improves the quality-latency trade-off of frozen flow-matching policies.

cs.RO↗

arXiv2026

Separating Stream Stability from Long-Term Recall in Language Models

Methods for streaming language models are often discussed alongside long-context and memory systems, although they solve different problems. An attention sink can stabilize autoregressive generation over an indefinitely long stream while the model remains unable to use content that has left its recent-token cache. We argue that this distinction should be explicit in system claims and evaluation. We introduce three horizons: the stability horizon, over which predictive behavior remains well behaved; the access horizon, over which past content can still causally affect the output; and the utility horizon, over which a task retains acceptable performance. We show constructively that the stability horizon can be infinite while the access and utility horizons are finite. We then propose ThreeH, an evaluation contract that measures all three horizons under a common state and compute budget. Applying the framework to attention-sink streaming clarifies its strength, constant-memory, stable generation, without treating anchor tokens as semantic memory. The framework exposes roles for cache policies, recurrent state, retrieval, and external memory. Experiments on 128K-token streams, delayed binding recall, and delayed decisions show that attention sinks preserve local modeling but not content beyond the active cache; recurrent and retrieval state extend the semantic horizon.

cs.CL↗

arXiv2026

A Graph Foundation Model for Large-Scale MIMO Detection

Large-scale multiple-input multiple-output (MIMO) detection is fundamental to modern wireless networks but constrained by performance-complexity trade-offs. Existing detectors, whether classical or learning-based, often fall short in either scalability or generalizability across heterogeneous scenarios. To overcome these limitations, we introduce a wireless-native graph foundation model (GFM) tailored for large-scale MIMO detection. The proposed GFM employs a physics-informed hybrid architecture, integrating the local correlation extraction of message passing neural networks with the global attention of graph Transformers, encoding the physical interference patterns from the expectation propagation algorithm. Via extensive pre-training, this synergy enables the learning of a general-purpose detection mapping scalable across antenna dimensions and channel conditions. For rapid downstream deployment, parameter-efficient fine-tuning is leveraged to adapt the GFM to specific non-ideal system regimes with minimal overhead. To enhance inference efficiency, a mixture-of-experts mechanism is embedded at downstream deployment to dynamically activate only the necessary sub-modules. Evaluations show that the proposed GFM consistently outperforms classical detectors and advanced data-driven baselines in accuracy, configuration generality, and cross-scenario transferability across various challenging zero-shot and few-shot conditions.

cs.IT↗

arXiv2026

On the Design of Qwen3.8-Next Architecture: Evaluation, Efficiency, and Training Stability

We describe the architecture and ablations of Qwen3.8-Flash-Next, a sparse mixture-of-experts model with 125B parameters, 6B activated per token, and additional 51B parameters of n-gram embedding tables held off the accelerator. On fourteen pre-training benchmarks the model leads the 397B-A17B predecessor on eight and trails it on the rest by at most 2.6 points, at 1/3 the activated parameters, 1/3 the training tokens, and roughly 1/9 the training FLOPs. Token mixing uses a layer-wise hybrid of Gated DeltaNet (GDN) and global attention, with one full-attention layer in every four; at continued-pretraining time those full-attention layers are replaced by Qwen Sparse Attention (QSA), which scores context at micro-block granularity with a compressed lightweight indexer. The residual stream is widened to four branches and read through an elementwise gate, a design we call the Gated Residual (GR). Capacity is added outside the backbone by a single n-gram embedding layer whose tables are prefetched from host memory. We evaluate every candidate change along three axes: loss together with downstream benchmarks; the cost of the change in training, prefill and decode; and its effect on the optimal hyperparameters and training stability. Loss and downstream accuracy do not always move together: enlarging the n-gram vocabulary lowers loss monotonically while downstream accuracy saturates. The architecture and the Muon optimizer together shift the optimal learning rate and batch size upwards, render batch-size warmup unnecessary, and substantially improve stability under stress tests. Loss, benchmarks, efficiency and stability form one design problem. Solved jointly, they yield a recipe that is simultaneously more efficient, more capable and more stable.

cs.CL↗

arXiv2026

Moiré-induced lattice reconstruction at buried atomic interfaces

Atomic reconstruction at twisted two-dimensional interfaces governs many of their emergent optical, electronic, and mechanical properties, including sliding ferroelectricity. Despite recent progress in understanding lattice reconstruction in suspended twisted bilayers, structural changes at van der Waals heterointerfaces between multilayer crystals remain largely unexplored. Here we use multi-slice electron ptychography to non-invasively recover the three-dimensional atomic structure at marginally twisted rhombohedral interfaces between thick transition-metal dichalcogenide crystals. With a position precision of ~3 pm and a depth resolution ~1 nm, we resolve the twist-induced lattice reconstruction field per layer, and the resulting dislocation network at the buried interface. Despite the bulk nature, we observe markedly strong in-plane interfacial reconstruction due to suppression of the out-of-plane bending by outer layers, exceeding predictions from our three-dimensional modelling. Furthermore, we extract the strain tensor evolution during the decay of the reconstruction into the bulk, providing a structural foundation for understanding multi-layer moiré systems.

cond-mat.mes-hall↗

arXiv2026

High-Harmonic Coherent Pulse Generation in a Storage Ring Using Multiple-Echo-Enabled Harmonic Generation

Fourth-generation storage-ring light sources have achieved transverse emittances approaching the diffraction limit at x-ray wavelengths, while their longitudinal coherence remains limited. Existing laser-modulation schemes can induce strong microbunching, but most storage-ring implementations produce only one useful coherent output from a given longitudinal region during each revolution, thereby underutilizing the intrinsic multi-user capability of storage rings. We propose a multiple-echo-enabled harmonic generation (multi-EEHG) scheme that applies successive excitation--echo cycles to the same longitudinal slice of a stored bunch within one revolution, enabling coherent-radiation delivery to multiple beamlines at different wavelengths. A general formulation of the n-stage EEHG bunching factor and a corresponding optimization procedure are derived. As an example, a triple-EEHG configuration is designed for the SAPS storage ring. Simulations demonstrate coherent radiation at multiple wavelengths with single-pulse photon numbers up to $10^9$, corresponding to an enhancement of approximately three orders of magnitude over synchrotron radiation for the same spectral bandwidth, while achieving few-meV bandwidth without a monochromator. The proposed scheme offers a scalable approach for multi-beamline coherent operation in next-generation storage-ring light sources.

physics.acc-ph↗

arXiv2026

NeuralParker: A Reinforcement Learning Planner for Irregular Parking Environments

Automated parking commonly assumes marked slots and short approach maneuvers. Delivery and service vehicles, however, may need to reach an operator-specified pose in an irregular bounded environment from a distant start. Existing learning-based parking planners often rely on local observations, which can restrict long-range route reasoning. To address this problem, we present NeuralParker, a reinforcement learning-based hybrid planner for arbitrary-pose parking. NeuralParker encodes full-environment obstacle and boundary geometry in a target-relative vertex representation, allowing the policy to retain route-defining context throughout the approach. It further couples a learned curvature--length arc policy with an in-loop terminal ensemble that selects from diverse cubic Hermite connections using a curvature-regularized cost. We also establish factorial and long-range route-choice benchmarks to evaluate planning success and trajectory quality. Experiments on these benchmarks show that NeuralParker achieves higher planning success and better overall trajectory quality than the evaluated baselines, while ablation studies support the benefits of the target-relative global representation and terminal ensemble. Finally, a real-vehicle evaluation confirms that the planner transfers effectively to real delivery-vehicle perception at a working parking site, planning successfully at low computational cost.

cs.RO↗

arXiv2026

Matrix norm shrinkage estimators and priors

We develop a class of minimax estimators for a normal mean matrix under the Frobenius loss, which generalizes the James--Stein and Efron--Morris estimators. It shrinks the Schatten norm towards zero and works well for low-rank matrices. We also propose a class of superharmonic priors based on the Schatten norm, which generalizes Stein's prior and the singular value shrinkage prior. The generalized Bayes estimators and Bayesian predictive densities with respect to these priors are minimax. We examine the performance of the proposed estimators and priors in simulation.

math.ST↗

arXiv2026

Reflex: Enabling Fast and Predictive Vision-Language-Action Models for Reaction-Critical Manipulation

Vision-Language-Action (VLA) models have recently achieved promising performance in robotic manipulation. However, existing benchmarks mainly evaluate generalization on static manipulation tasks and largely overlook dynamic interaction scenarios. To address this gap, we present ReflexBench, a benchmark for reaction-critical manipulation. ReflexBench contains six dynamic tasks and introduces an evaluation framework that decouples simulator stepping from robot control while supporting configurable latency under synchronous and asynchronous inference. Building upon ReflexBench, we propose ReflexVLA, an efficient VLA model designed for reaction-critical manipulation without large-scale robot-data pretraining. ReflexVLA enhances temporal reasoning through latent future prediction and multi-frame temporal fusion within the vision backbone, while reducing deployment latency through batched visual encoding and CUDA Graph replay. Experiments show that ReflexVLA consistently improves dynamic manipulation performance while maintaining competitive accuracy on standard static manipulation benchmarks, and real-world experiments further demonstrate its effectiveness under practical deployment conditions. Project website: https://reflexvla.github.io

cs.RO↗

arXiv2026

A Structure-Preserving GSAV Exponential Integrator for Smectic-A Liquid Crystals

The modified Landau--de Gennes (mLdG) theory provides a powerful continuum framework for modeling smectic-A (SmA) liquid crystals by coupling the tensorial orientational order parameter $\mathbf{Q}$ with the scalar positional order parameter $u$. In this paper, we develop and analyze a structure-preserving generalized scalar auxiliary variable exponential integrator (GSAV-EI) scheme for the fully coupled mLdG system. The key ingredient is a backward Euler-type reformulation of the exponential integrator, which reveals a coercive discrete structure suitable for energy estimates and error analysis. Moreover, it eliminates the mesh-dependent time-step restriction ($τ\lesssim h^2$) required in the existing GSAV-EI error analysis and provides a transparent connection between exponential time-differencing and implicit time-stepping methods. Building on this reconstructed structure, we prove unconditional modified-energy stability and establish optimal-order fully discrete error estimates. Numerical experiments in two and three dimensions validate the theoretical convergence rates, verify the discrete energy-dissipation law, and illustrate the self-assembly dynamics of the SmA phase.

math.NA↗

arXiv2026

Repurposing RGB-based Foundation Model for Depth Estimation on Thermal Images Using Hierarchical Supervision

Depth estimation from thermal images is highly valuable for robotic applications in adverse conditions, such as nighttime and rainy weather. Recent studies have sought to transfer knowledge from RGB-based foundation models to thermal modalities, yet the rich hierarchical representations these models encode remain underutilized. To address this limitation, we propose RGB-HS, a novel framework for thermal-image depth estimation that leverages hierarchical supervision from an RGB-based foundation model. Specifically, we first replace the baseline thermal encoder with a foundational model and introduce a parallel RGB branch that also employs a foundational model as an encoder of the same architecture, taking RGB images as input. The alignment is then performed across multiple levels between the tokens of the two encoders, allowing the thermal student branch to capture both structural precision and semantic abstraction from the RGB teacher branch. Furthermore, we introduce verification to refine the alignment process by weighting tokens from the RGB branch based on RGB image quality. Extensive experiments on the popular benchmark demonstrate that RGB-HS achieves competitive performance and more effectively exploits the representational capacity of RGB-based foundation models for depth estimation on thermal images.

cs.CV↗

arXiv2026

Two-dimensional beam compression for sub-femtosecond electron beam generation

Sub-femtosecond electron beams are powerful probes of ultrafast electronic, atomic, and nuclear dynamics, and promising drivers for ultrashort radiation generation from the extreme-ultraviolet to gamma-ray regimes. However, producing such beams at hundred-MeV energies with pC-level charge remains challenging. Here we develop a two-dimensional beam-compression scheme based on transverse--longitudinal coupling, in which dispersive beam optics convert the small transverse emittance of modern electron beams into an ultrashort bunch length. Linear analysis and particle tracking show that, after the dominant longitudinal and energy-spread contributions are cancelled, the compressed bunch length is governed primarily by transverse beam quality and collective-effect growth. We further derive and verify a scaling model that captures collective-effect-induced bunch-length degradation and provides a charge--energy operating map for sub-femtosecond compression. Start-to-end simulations of a realistic injector-to-compressor beamline produce a 200 MeV, pC-level bunch with a bunch length of 0.45 fs and a peak current of about 3.5 kA. Jitter studies over a broad range of beam energies show that the output bunch-length distribution narrows and then varies only weakly with increasing energy. These results suggest a feasible route toward compact, high-energy attosecond electron beam sources and may provide a basis for future sub-femtosecond radiation sources based on undulator emission or inverse Compton scattering.

physics.acc-ph↗

arXiv2026

OpenRIS: Democratizing reconfigurable intelligent surfaces for real-world wireless enhancements

Wireless enhancement is critical for next-generation mobile communication systems to realize seamless connectivity, yet traditional network expansion strategies are becoming economically unsustainable. Reconfigurable intelligent surfaces (RISs) provide a promising alternative by improving signal utilization. However, high hardware and deployment costs of advanced RISs limit their large-scale application. Here, we democratize this technology with OpenRIS, an open-source and low-cost platform composed of Lego-like meta-bricks. With digital-twin assistance, these meta-bricks can be flexibly assembled into arbitrary shapes to achieve customized, mass-deployable wireless enhancement without extra power. Experiments and full-wave simulations verify that the discretized OpenRIS achieves consistent performance with the continuous RIS. We further develop a dual-user wireless transmission system and a three-dimensional coverage measurement system to showcase the versatile applicability of OpenRIS in wireless enhancements. As a plug-and-play solution, OpenRIS accelerates the translation of RIS theory into practice and is poised to integrate into infrastructure, reshaping the future wireless world as steel and concrete shape modern cities.

eess.SP↗

arXiv2026

Interaction-induced sign reversal of the orbital magnetic susceptibility in Chern insulators

It has been well established that the orbital magnetization of interacting electrons can be simply evaluated by applying the single-particle formula to self-consistent HF bands. However, we show that such procedure fails qualitatively for orbital magnetic susceptibility, especially in topological systems: in a Chern-insulating phase of twisted MoTe$_2$, the interaction-induced correction reverses the sign of the susceptibility. This result follows from an algebraic framework that solves the HF problem at finite magnetic field, where noncommuting canonical momenta obstruct a direct calculation: a \emph{reverse Peierls substitution} maps every magnetic-translation-invariant operator to a unique bivariate function, converting the finite-field self-consistency into a function equation that can be expanded systematically in $B$. At first order, this yields a linear equation for the field-induced change $δX$ of the Fock potential, leading to an intrinsically interaction-induced susceptibility in addition to a single-particle-like one. The framework reproduces the Středa formula for insulators, and an auxiliary-Hilbert-space construction carries it to periodic and moiré systems. Finite-field HF calculations in a gapped Dirac model and in the twisted-MoTe$_2$ Chern insulator confirm the theory quantitatively.

cond-mat.str-el↗