arXiv subjects

Shuai Liu

Publications and source records attributed to Shuai Liu.

At least 19 recordsLinked to original sources

arXiv2026

A Parallel Algebraic Multigrid Preconditioned Iterative Method Based on an MPI Process Grouping Strategy for Asynchronous Concurrent Execution of the Setup and Solve Phases

Algebraic multigrid (AMG) is one of the most effective algorithms for solving large sparse linear systems and consists of a setup phase followed by a solve phase. As supercomputer performance improves and the number of MPI processes increases, AMG faces serious challenges in algorithmic scalability and parallel scalability. To address these issues, we propose an asynchronous concurrent algebraic multigrid (ACAMG) model based on an MPI process grouping strategy for setup and solve. The setup and solve phases execute asynchronously and concurrently on separate MPI process groups. This organization can both preserve AMG hierarchy quality through an appropriate choice of the setup-group size and achieve time overlap between the two phases through asynchronous concurrent execution. Based on this model, we design and implement the MPI ACAMG algorithm. Theoretical analysis and numerical experiments show that the algorithm exhibits better algorithmic and parallel scalability. For example, for a three-dimensional diffusion problem with spatially localized anisotropy, ACAMG not only reduces the solve-phase iteration count compared with the conventional method, but also achieves speedups of 4.74 and 19.36 at 64 and 128 processes, respectively.

math.NA↗

arXiv2026

$\varepsilon$-$\mathcal{VU}$ spaces and approximate leaves\\ for sublinear composite functions

Let $h$ be proper, closed and sublinear, and let $F$ be of class $C^1$. The exact $\mathcal{VU}$ calculus of the composite $f=h\circ F$ is taken as known. This paper studies enlargements $\bar\partial_\varepsilon h\subset\partial_\varepsilon h$ and the resulting $\varepsilon$-spaces of $f$. Nested inclusions give one-sided Painlevé--Kuratowski limits of $\mathcal{V}_\varepsilon$ and $\mathcal{U}_\varepsilon$ at points of the lineality space $\mathcal{U}h(0)$. An additional transversality transfers those limits through $F$; without it only the exact chain rule is available. Approximate leaves $\mathcal{M}_\varepsilon(x)=F^{-1}\bigl(F(x)+\mathcal{U}_\varepsilon h(F(x))\bigr)$ are inner semicontinuous under the nesting $\mathcal{U}h(F(\bar x))\subset\mathcal{U}_\varepsilon h(F(x))$. For a polyhedral outer function the activity set freezes along the exact leaf for small $\varepsilon$, so the dimension identity holds on that leaf and not necessarily in a full neighbourhood of $(\bar x,0)$. Partial smoothness of $h$ relative to $\mathcal{U}h(0)$ extends a known result from the origin to the lineality space; it is not used by the transfer theorems.

math.OC↗

arXiv2026

Video-HopChain: Multi-Hop Questions and Confidence-Gated Exploration for Video Reasoning Models

HopChain has shown on still images that multi-hop data synthesis improves vision-language reasoning, because long chain-of-thought reasoning exposes errors that compound across steps, while most data used for reinforcement learning with verifiable rewards (RLVR) rarely demands a chain of visual evidence, so these weaknesses are likely to stay unexposed. We observe the same problem in video, where this framework has not yet been explored. We therefore build Video-HopChain, a dataset of 22,550 multi-hop video questions over 13,378 videos, together with a held-out benchmark of 1,000 questions. Each question chains three to six yes/no questions about moments in one video, and each yields one of two integers depending on its answer. The final answer is the sum of these integers, so an exact match on that sum gives the verifiable reward that RLVR needs. We first train Qwen3-VL-8B with GRPO on a standard video dataset, and a second stage on Video-HopChain then raises the mean over eight video understanding and reasoning benchmarks from 55.4 to 57.9 and improves every one of them. Training on such a dataset, however, exposes a known limitation of GRPO: its learning signal comes from the reward variance within a group, so hard questions whose rollouts are all incorrect and easy questions whose rollouts are all correct both leave the group with no gradient. To recover these groups at the same compute budget, we introduce Confidence-Gated Exploration (CGE). With 8 rollouts per question, CGE samples the first 4 as usual. If these 4 are either all correct or all incorrect, it samples the last 4 with the policy's most confident token masked inside the reasoning span, and removes the masked positions from the loss while all 8 rollouts enter the advantage. With CGE, the mean rises further to 59.3. We release the dataset, the checkpoint, and the data generation and training code.

cs.CV↗

arXiv2026

Contactless gait-derived features as candidate digital biomarkers of depression severity in adolescents

Timely assessment of depression severity in adolescents is essential for guiding clinical care, yet conventional evaluations rely on episodic clinical assessments and symptom rating scales. Contactless gait analysis offers a potentially scalable approach to capturing objective behavioral features associated with depression. Here, we introduce the Long-Distance 3D Adolescent Depression Gait Dataset (LD3D-ADG), comprising depth-sensor gait recordings from 119 adolescents at a single center, including 39 healthy controls, 41 with mild depression, and 39 with moderate-to-severe depression. We developed UMLPA-GCN, a graph convolutional network that models spatiotemporal gait dynamics across three anatomically defined skeletal partitions. In participant-independent five-fold cross-validation, UMLPA-GCN achieved 75.63% classification accuracy, a macro-F1 score of 75.52%, and a macro-area under the receiver operating characteristic curve of 0.839 (95% confidence interval, 0.779--0.899). Class-specific recall was 87.18%, 63.41%, and 76.92% for healthy controls, mild depression, and moderate-to-severe depression, respectively. At the participant level, reduced arm swing, slower pelvic movement, restricted joint range of motion, and greater arm-swing asymmetry were identified as leading potential digital biomarkers contributing to depression severity assessment, associated with higher 17-item Hamilton Depression Rating Scale scores. These findings provide proof-of-concept evidence for contactless digital depression severity stratification in adolescents. Further external and prospective validation is needed to establish their utility for longitudinal monitoring and timely clinical assessment.

eess.SP↗

arXiv2026

Carbon in J0815+4729: Casting a shadow of doubt on the interpretation of CH absorption in the most primitive stars

Aims. We revisit the carbon abundance in the primitive iron-poor star J0815+4729 ([Fe/H] = -5.49). J0815+4729 is so carbon-enhanced and oxygen-rich that, in addition to CH and C2 molecular lines, weaker neutral atomic transitions are measurable in its spectrum, providing a unique opportunity to explore the line formation of C I and O I transitions in such an extremely iron-deficient star. Methods. Based on high-resolution spectroscopy and newly constructed atomic models for carbon and oxygen, we present the first investigation to derive carbon and oxygen abundances of this hyper iron-poor star from neutral atomic transitions under non-local thermodynamic equilibrium (NLTE) conditions. Results. The carbon abundance derived from C I lines is A(C)NLTE = 8.08+-0.07, consistent with that inferred from C2 lines (A(C)LTE =7.96 +- 0.08), but around 0.6 dex higher than the value from CH lines (A(C)LTE = 7.47 +- 0.17). Since CH lines are the most commonly-used carbon abundance indicator for extremely metal-poor stars, this result casts doubt on the quantitative interpretation of CH absorption in the most primitive stars. We confirm that J0815+4729 is one of the most carbon-enhanced stars with [Fe/H] <= -4.5 known to date. We have also determined A(O)NLTE as 7.25 +- 0.11, with NLTE corrections of around -0.11 dex. The carbon-to-oxygen ratio of the star is [C/O] = 1.06 +- 0.1.

astro-ph.SR↗

arXiv2026

EgoTools: Towards Tool-Centric Reasoning in Real-World Egocentric Videos

Real-world embodied tasks, from everyday activities to professional procedures, require agents to act under physical constraints while tracking evolving object and task states. Tool use sits at the heart of such tasks, as many everyday and professional activities are tool-mediated. Understanding them requires reasoning about affordances, hand-tool-object geometry, procedural progress, and causal effects on target objects. Yet despite strong performance on perception-oriented video tasks such as captioning and general video QA, current multimodal video models remain limited in this form of tool-centric embodied reasoning. Progress in this direction has been limited by the lack of real-world egocentric data and diagnostic benchmarks. To address this gap, we introduce EgoTools, the first comprehensive suite for egocentric tool-use understanding. It consists of two complementary components: EgoTools-Data, a large-scale corpus of 100 hours of tool-centric egocentric recordings with synchronized audio, dense captions, reasoning-heavy narrations, and supplementary 3D information; and EgoTools-Bench, a diagnostic benchmark of 1,000 QA pairs across four tracks that cover tool-use understanding from perception and geometry to procedure and causal reasoning. Experimental results show that current models still struggle to ground tool use in visual evidence: Gemini-3.1-Pro achieves 66.9% overall accuracy but only 51.7% on Perception & Grounding. Beyond evaluation, we validate EgoTools-Data as a training resource. On the full 1,000-question benchmark, full supervised fine-tuning improves Qwen3-VL-8B-Instruct from 50.0% to 60.9%, under strict source-video separation. Together, these results establish EgoTools as a unified resource for both training and diagnostic evaluation of real-world egocentric tool-use understanding.

cs.CV↗

arXiv2026

Learning Reliable GUI Agents under Imperfect Priors

GUI agents built on large language and vision-language models still struggle on unseen applications and complex multi-step tasks, as completing real GUI tasks depends on app-specific, temporally volatile operational knowledge that is scarce in pretraining corpora. Retrieval-augmented execution offers a natural remedy but faces two coupled bottlenecks: knowledge at scale is hard to acquire, and self-collected priors inevitably drift from the live environment due to version updates, promotions, ads, A/B tests, and personalization. We therefore argue that GUI agents should not pursue perfect knowledge but learn to act correctly under imperfect priors, and propose our framework that couples knowledge acquisition with noise-robust utilization: a structured exploration strategy traverses interactive elements, builds a UI state-transition graph, and synthesizes (task, trajectory) pairs via a VLM without human annotation; a noise-aware training strategy, grounded in a taxonomy of real GUI drift patterns, injects five types of realistic errors into self-explored trajectories to teach the agent to assess prior reliability before acting. Experiments on physical devices and online emulator benchmarks show that our method discovers more unique screens, covers more benchmark tasks, and more effectively rejects erroneous priors while leveraging correct ones, with accuracy gains that transfer across datasets.

cs.LG↗

arXiv2026

CoVLM-Bench: A Real-World Benchmark for Cooperative Driving Question Answering and Planning

Vision-language models (VLMs) have made substantial progress in autonomous driving, but their success has primarily been studied in ego-centric scenes. Infrastructure-side observations provide views beyond the ego vehicle's field of view, yet conventional cooperative-driving systems typically transform them into geometric representations for downstream perception and planning. Directly incorporating these views into VLMs offers an opportunity to improve cooperative scene understanding and trajectory planning. However, question answering and trajectory planning have not been jointly evaluated on the same real-world vehicle-infrastructure scenes. We present CoVLM-Bench, a benchmark for cooperative driving question answering (CDQA) and cooperative planning (CP) on vehicle-infrastructure paired scenes. CoVLM-Bench provides scene-grounded CDQA annotations, three-part rationales as auxiliary supervision, and future trajectory targets derived from recorded ego motion. It contains 2,196 paired frames with 35,136 CDQA annotations, while CP predicts six waypoints over a three-second horizon. The annotations combine model-assisted drafting, record-based computation, and human verification. Built upon CoVLM-Bench, we introduce CoVLM-Drive, a unified VLM baseline that directly uses paired views for both CDQA and CP. Experiments show that CDQA adaptation improves answer accuracy and that CoVLM-Drive reaches a lower FDE than the compared V2X planners; QA initialization and rationale supervision each reduce planning error. Together, CoVLM-Bench and CoVLM-Drive support the training and comparison of VLMs for cooperative scene understanding and planning.

cs.CV↗

arXiv2026

DA-Lion: Efficient Neural Video Representation via Direction-Aware Optimization

Implicit neural video representation (NVR) encodes a video as the parameters of an overfitted neural network, where training and testing data are identical and the objective is instance-specific signal fitting. In this deterministic regime, optimization dynamics directly determine reconstruction quality under a fixed budget, yet existing methods universally adopt general-purpose optimizers designed for stochastic gradient training. Adam-family optimizers descend steadily but converge slowly, while Lion achieves faster early progress but oscillates markedly in later stages when gradient directions become unstable. We trace this instability to the insensitivity of the sign-based update to gradient--momentum alignment. To address this, we propose Direction-Aware Lion (DA-Lion), a task-driven optimizer tailored for NVR. DA-Lion introduces (i) a direction-consistency criterion that switches between sign update and momentum update based on gradient--momentum alignment, and (ii) a learning-rate-aware magnitude modulation that stabilizes effective step sizes across training phases. DA-Lion modifies only the parameter update rule, preserving model architecture, parameter count, FLOPs, and decoding throughput. Experiments on three datasets with four NeRV-family backbones show that DA-Lion improves convergence stability and boosts PSNR/MS-SSIM across all backbones. The source code will be made available at https://github.com/maoqingyu1996/DA-Lion.

eess.IV↗

arXiv2026

Quality Assessment of 3D Gaussian Splatting: Distortions, Benchmarks, and Open Challenges

3D Gaussian Splatting (3DGS) has become a practical scene representation for real-time novel-view rendering, compression, and immersive content delivery. However, its quality assessment still largely follows rendered-view proxy protocols that sample camera poses, render images or videos, and apply inherited image and video quality metrics. While convenient, this practice does not fully capture 3DGS-native degradations that originate from Gaussian primitive distributions, splatting and visibility behavior, and trajectory-dependent artifacts. This survey reviews recent 3DGS quality assessment studies from four perspectives, covering distortion characteristics, subjective benchmarks, objective metric reliability, and emerging directions for native 3DGS evaluation. Across the literature, we find that different benchmarks construct different notions of quality, and that metric effectiveness is highly protocol dependent, varying with distortion sources, stimulus formats, and view and trajectory sampling. Finally, we summarize open challenges and outline practical guidelines for building more comparable and representative 3DGS-QA evaluations.

eess.IV↗

arXiv2026

MM-Future: Multi-Mode Joint World-Action Modeling for Autonomous Driving

Autonomous driving involves coupled decision-making and scene evolution under multi-mode uncertainty. To capture this coupling and uncertainty, we introduce MM-Future, a world-action model that generates multiple paired scene-action hypotheses and models bidirectional interaction within each pair. Each hypothesis is initialized from a structured action prior and an independent future scene source, which are then co-evolved through a modality-aware diffusion Transformer. To support efficient multi-mode rollout, MM-Future compresses multi-view video into planning-oriented representations, dubbed MM-Tokens. Finally, a future-conditioned proposal scorer ranks trajectory candidates by shared history context and their paired predicted future. On NAVSIM navtest, MM-Future achieves 94.0 PDMS and 91.5 EPDMS, while attaining a 32.3 HD-Score in zero-shot closed-loop evaluation on HUGSIM. Ablations show consistent improvements over both single-mode and action-only variants, validating the benefit of multi-mode joint world-action modeling.

cs.CV↗

arXiv2026

A $\mathcal{VU}$-calculus for composite functions and the $\mathcal{U}$-Hessian of partly smooth functions

We assemble a finite-dimensional $\mathcal{VU}$-calculus for composite nonsmooth functions whose outer function is convex: the chain rule, separable and convex sums, a strictly differentiable perturbation of a lower semicontinuous (lsc) term, the model $δ_X+f_0+θ\circ F$, and a finite maximum of $C^1$ functions. The same algebra yields an $\varepsilon$-$\mathcal{VU}$ chain rule for a proper outer approximation of the subdifferential. On the set $\mathcal{R}_{h,F}$ of points at which the convex chain rule holds, the subspaces $\mathcal{V}f$ and $\mathcal{U}f$, an orthonormal frame of $\mathcal{U}f$, and the $\mathcal{U}$-gradient $\bar g_u$ are written in terms of the factors. If $f$ is $C^1$-partly smooth at $\bar{x}$, that gradient is $\bar g_u=U_f^\top\nabla_{\mathcal{M}}f(\bar{x})$. If $f$ is $C^2$-partly smooth and $0\in\ri\partial f(\bar{x})$, the convex $\mathcal{U}$-Hessian $H_U$ (when $f$ is convex) or the local matrix $H_\varepsilon$ (when $f$ is prox-regular at $\bar{x}$ for $0$) equals the Gram matrix $U_f^\top\nabla^2_{\mathcal{M}}f(\bar{x})\,U_f$; the same matrix, written along the active manifold in a continuous frame, depends continuously on the base point. If in addition $f$ is $C^2$-partly smooth at $\bar{x}$, then under prox-regularity and subdifferential continuity at $\bar{x}$ for $0$, $H_\varepsilon\succ 0$ is equivalent to tilt stability of $\bar{x}$ and to strong metric regularity of $\partial f$ at $(\bar{x},0)$, with $\lip\bigl((\partial f)^{-1}\bigr)(0\mid\bar{x})=\|H_\varepsilon^{-1}\|$. The calculus and the test are illustrated on a hinge composite and two elementary tests of $H_U\succ 0$.

math.OC↗

arXiv2026

Towards a knowledge-enhanced single-cell foundation model

Single-cell foundation models (scFMs) increasingly rely on large-scale transcriptomic pretraining, yet expanding pretraining data can yield diminishing gains while substantially increasing computational cost. Our data scaling analyses showed that incorporating biological knowledge, including cell-level text annotation and gene-level regulatory information, provided additional scaling dimension than simply increasing data size. Motivated by this observation, we present scKITE, a simple yet effective scFM that integrates cell-annotation and gene-regulatory supervision into a shared transcriptomic Transformer encoder through lightweight auxiliary decoders. These decoders are used only during pretraining and subsequently discarded, yielding a general-purpose encoder enriched with biological knowledge for downstream applications. With only 179,067 pretraining samples, i.e., less than 0.5\% of those used by previous strong scFMs, scKITE outperformed these models across diverse downstream tasks, highlighting knowledge-enhanced pretraining as a promising paradigm for biologically grounded scFMs.

cs.AI↗

arXiv2026

Ultra-Low-Loss Silicon Nitride on Sapphire for Broad-Transparency Nonlinear and Quantum Photonics

The field of photonic integrated circuits (PIC) has flourished in the past two decades, fueling numerous cutting-edge applications across sensing, networking, data interconnect, and quantum information processing. As a guiding material for PIC, Si$_3$N$_4$ has seen extensive use for its ultra-low loss, broad transparency, and diversity in implementation across both thin and thick films. Although the standard, traditional silicon dioxide (SiO$_2$) on silicon (Si) substrates that underpin the majority of Si$_3$N$_4$ photonics face drawbacks in the form of long-wavelength transparency limited by SiO$_2$, high-stress deposition for anomalous dispersion thick-film Si$_3$N$_4$, and leakage loss to the Si layer for low-confinement thin-film Si$_3$N$_4$. Featuring increased long-wavelength transparency into the mid-infrared, low-stress deposition of Si$_3$N$_4$, and a low index, this work investigates sapphire substrates as alternate hosts for Si$_3$N$_4$ photonics with greater spectral coverage and reduced fabrication complexity. This work presents a robust method of fabricating ultra-low loss photonic integrated circuits on a 500-nm-thick Si$_3$N$_4$-on-sapphire platform, exhibiting record-low losses below $0.1 \rm \;dB/cm$. Implemented using this process are high-Q microrings with intrinsic quality factors in excess of $4.5\times10^6$ and coupled-ring photonic molecules to support nonlinear gain. Leveraging the achievable low loss and high-Q, this work further reports the first demonstration of Kerr-comb and soliton generation on the Si$_3$N$_4$-on-sapphire platform. These advances in loss, quality factor, and soliton generation on this versatile, broad-transparency platform pave the way for future work in spectroscopy and quantum-enhanced sensing across previously prohibited spectral regions for Si$_3$N$_4$ photonics with reduced fabrication complexity.

physics.optics↗

arXiv2026

Quantum Wave-Particle Duality in Free-Electron--Free-Electron Entanglement

Point-particle descriptions of electron-electron interaction omit the coherent longitudinal extent of a free-electron quantum wave packet (QEW). A relativistic two-electron wave-packet theory identifies the longitudinal QEW size as a direct control parameter for entanglement generated by mutual electromagnetic coupling. For two electrons in spatially separated paths, the quadratic interaction phase gives a dimensionless entangling parameter $\Gee$ and the logarithmic negativity $\EN=\operatorname{arsinh}(\Gee)/\ln2$. Full time-dependent Schrödinger equation calculations confirm the scaling for narrow QEWs and reveal higher-order Coulomb effects at larger spatial extent. Free drift increases the interaction-point QEW width while preserving the momentum probability distribution, thereby enhancing the subsequently generated entanglement. The results establish a direct connection between free-electron wave-particle duality and bipartite entanglement.

quant-ph↗

arXiv2026

Acoustic toroidal vortices with programmable links and knots

Toroidal vortices are three-dimensional torus-shaped wave structures characterized by phase circulation around a closed vortex line. Their toroidal geometry provides a natural foundation for constructing linked and knotted wave structures. Here we experimentally synthesize scalar acoustic toroidal vortices using a programmable circular phased array. Full spatiotemporal measurements directly resolve the toroidal envelope, the closed phase-singularity ring, the associated poloidal phase winding, and the free-space evolution of the wave packet. By introducing an independently controlled phase winding along the toroidal cycle, we realize scalar acoustic hopfions and directly reconstruct their three-dimensional equiphase fibers from the measured complex pressure field. Varying the poloidal and toroidal winding numbers controls the phase-fiber geometry, linking, and connectivity, yielding a Hopf link, a multicomponent torus link, and a trefoil knot. These results provide direct experimental access to the geometry, propagation dynamics, and phase-fiber topology of scalar toroidal wave fields, establishing a reconfigurable acoustic platform for linked and knotted wave structures.

physics.optics↗

arXiv2026

NTIRE 2026 Low-light Enhancement: Twilight Cowboy Challenge

This paper presents a review of the NTIRE 2026 Low-light Enhancement: Twilight Cowboy Challenge. The objective of the competition was to merge a set of misaligned smartphone images in the raw domain, captured in low-light conditions, into a single, clean image. Introduced setup simultaneously addresses two problems of low-light photography: visual degradations such as high noise and mixed scene illuminants, and the geometric inconsistencies caused by hand movement during multi-frame capture. To advance research in low-light and nighttime computational photography, a challenging dataset was collected comprising 585 real-world scenes, spanning indoor low-light and outdoor nighttime conditions, for training and benchmarking participant solutions. The competition employed a three-stage evaluation protocol: automatic validation via the CodaBench platform in stages one and two, followed by blind assessment on a private test set for the final ranking. Ten teams surpassed the established baseline, achieving improvements of up to +6.49 dB in PSNR and +0.0101 in SSIM, thereby establishing new state-of-the-art performance for burst-based low-light image enhancement. These results demonstrate significant progress in handling real-world noise, motion, and illumination variability in the low-light setting. Comprehensive results, leaderboards, and additional information are publicly available at https://nightimaging.org.

cs.CV↗

arXiv2026

HumanCLAW: Can Vision-Language Models Act Through a Body?

Evaluating whether a vision-language model (VLM) can act through a physical body is challenging. The outcome of an action couples the VLM's decision with motor control. When a task fails, it is hard to tell whether the VLM made a bad choice or the motor controller simply failed to execute it, e.g., losing balance and falling. In this work, we introduce HumanCLAW, an evaluation framework that decouples action decision-making from low-level execution. At every step, a harnessed, off-the-shelf VLM issues an atomic skill command, and the command is translated into a sub-second chunk of continuous full-body motion with real physical consequences, including gravity and collisions. The body can therefore act freely in the physical world, while execution-side disturbances, balance and motor errors, are factored out. What remains measurable is the model's action intelligence: its moment-to-moment choice of what the body should execute next. Based on this framework, we build HumanCLAW-Bench: 1,218 long-horizon, egocentric find-navigate-interact episodes across 41 indoor scenes. We test nine state-of-the-art VLMs and find that none solves the benchmark; the best model reaches only a 16.8% success rate. Recognizing the target is not the bottleneck. What current VLMs lack is embodied self-awareness: they lose track of their own body, failing to tell where it is, whether it has reached the goal, or whether it has hit an obstacle.

cs.CV↗