arXiv subjects

Shuai Li

Publications and source records attributed to Shuai Li.

At least 19 recordsLinked to original sources

arXiv2026

Auditing Multi-Proxy Explanations of Web User Preferences: Identification Boundaries and Abstention

Web systems infer user preferences from behavior, ratings, text, and LLM-generated profiles, yet explaining a proxy may reveal its measurement process rather than the preference of interest. We develop an audit framework distinguishing point identification, partial identification, and abstention relative to an explicit reference distribution. Under feature-dependent, arbitrarily signed proxy bias, a strict majority of uncontaminated proxy attributions lets the coordinatewise median recover latent-attribution support and direction and, with externally justified positive scales, magnitude. We prove this threshold sharp using response-valid indistinguishable worlds, characterize sharp minimax error and exact compatible-target sets under approximate cleanliness and scale uncertainty, and propagate clean-proxy attribution error. A frozen known-truth benchmark spans two response families, four proxy counts, all contamination counts, three bias strengths, and eleven methods, with a focal comparison confirmed on twenty unseen seeds. Under correct budgets, a direct attribution-tensor audit achieved 100% coverage, 95.40%-97.05% active-driver sign decisions, and no false signs. In a temporally separated Amazon study, median aggregation reduced comparator-relative conditional sign error for all four targets; six of eight prespecified target-metric contrasts had Bonferroni intervals excluding zero. A controlled real-covariate perturbation favored the median on the prespecified focal metric for all four targets. Compatible sets showed that the current proxy configuration rarely licensed strict-sign conclusions. Weak comparator calibration limits the Amazon evidence to fitted-surface alignment rather than latent-preference recovery.

cs.LG↗

arXiv2026

When Category Means Move: A Paired-Support Audit of Recommendation Logs with Random Insertions

Content-category dashboards summarize production recommendation logs, although the serving policy selects the users, items, and contexts behind each mean. Using KuaiRand random-insertion impressions, we audit these summaries on the same 12,472 users and 5,486 videos in both logs. Standard means have absolute/centered mean absolute error (MAE) 0.1935/0.0491 and rank correlation 0.7833. User demeaning plus a random grand-mean anchor reduces absolute MAE by 80.0%, but centered MAE by only 21.0%, without reliably improving rank. A complete-support ledger allocates weighted squared error mainly to within-video residual (72.41%) and video mix (23.90%), with 3.65% from random support. In a frozen later test, early category calibration reaches MAE 0.0251 versus 0.0531 for scalar calibration; their direct difference is -0.0280 [-0.0296, -0.0172]. Decision evidence is cutoff-specific: the nominal top-10 regret change for row-weighted demeaning versus naive is -0.0062 [-0.0084, -0.0003], while top-5 and top-20 intervals include zero. The six-contrast family-wise top-10 interval [-0.0136, 0.0013] includes zero. An outcome-free disjoint randomized-reference split also leaves all six nominal contrasts covering zero. A known-truth stress test shows incomplete improvement under a mean-additive positive control and failure under item mix and weak overlap. All decompositions are descriptive and each result is conditional on its overlap cohort. KuaiRand intervals quantify user-cluster variation conditional on the realized catalog and calendar; we claim neither intrinsic category effects nor a universally valid correction.

cs.IR↗

arXiv2026

Group Distributionally Robust Optimization with Flexible Sample Queries

Group distributionally robust optimization (GDRO) aims to learn models that perform well across $m$ distributions simultaneously. Existing methods either (i) query $m$ samples per round, which imposes strict implementation requirements, or (ii) query only a single sample per round, which requires many rounds to converge and therefore leads to long runtime. To avoid these limitations, we introduce a novel setting called \emph{flexible sample queries} (FSQ), which allows the number of samples that can be queried to vary across rounds. Under the FSQ setting, we cast GDRO as a two-player game: one performs non-oblivious online convex optimization, while the other tackles a non-oblivious prediction with limited advice (PLA) problem. In this game, the first player updates its decision using follow-the-regularized-leader with stochastic gradients. For the second player, we develop a new PLA method equipped with an adaptive exploration strategy that can leverage an arbitrary per-round sample size. We then establish the \emph{first} high-probability regret bound for non-oblivious PLA. By integrating these components, we develop an anytime GDRO algorithm that supports FSQ, and derive a high-probability optimization error bound of $O\left(\frac{1}{t}\sqrt{\sum_{j=1}^t \frac{m}{r_j}\log m}\right)$, where $r_j$ is the sample size at round $j$. This result demonstrates that the optimization error decreases as the per-round sample sizes increase, and implies the same near-optimal sample complexity of $O(m\log (m)/ε^2)$ for any fixed sample size $r\in[m]$.

cs.LG↗

arXiv2026

CUFormer: Modality-Aware CT-Ultrasound Point Cloud Registration for Orthopedic Navigation

Registering intraoperative Ultrasound (US) bone-surface point clouds to preoperative Computed Tomography (CT) models can support radiation-free orthopedic navigation. Differences in imaging physics produce distinct surface sampling patterns, complicating cross-modal feature matching. Bone strongly reflects ultrasound and casts an acoustic shadow, so US captures only the probe-facing bone surface and leaves most CT points unmatched. This partial coverage makes correspondences ambiguous on near-symmetric bones. We propose CUFormer, a modality-aware registration framework. We introduce a Modality-Aware Transformer (MAT) that encodes input modality through identity embeddings and updates features through modality-specific adapters within a shared geometric matcher. We model imaging visibility and use predicted reference-side visibility to weight coarse and fine correspondences. CUFormer achieves the lowest mean registration errors among evaluated methods on two CT-US datasets, UltraBones100k and UltraBonesHip, and the CT-RGB-D dataset SpineDepth. On UltraBones100k, it reaches mean rotation error 4.53$^\circ$, translation error 3.19\,mm, and Root Mean Squared Error (RMSE) 2.54\,mm, reducing RMSE by 65.8\% relative to the best-performing baseline by this metric. These results demonstrate CUFormer's effectiveness in aligning partial bone surfaces with preoperative CT across anatomical sites.

cs.CV↗

arXiv2026

MoPort: A View-Aware 2D Interface for Motion Language Models

Motion language models (MoLMs) encode 3D human motion as discrete tokens for language-based understanding. Deploying them on monocular video raises two problems. Pose detection yields 2D joint sequences, leaving a dimensional gap from the 3D motion used in training. Moreover, the 2D observation changes with the camera viewpoint, while the depth lost in projection makes the 2D pose ambiguous across viewpoints. We propose MoPort, a view-aware 2D interface that enables MoLMs to take 2D observations as input. We introduce Paired View Alignment (PVA), which gives two views of the same motion a shared 3D teacher target and directly constrains the consistency of their latents, so that 2D observations from different viewpoints are mapped to a consistent semantic representation. Camera-Modulated Encoding (CME) estimates the camera extrinsics from the 2D sequence and uses them to modulate encoding, providing the 2D latents with geometric information about the viewpoint. MoPort achieves R@1 of 0.5674 on HumanML3D and 0.3286 on KIT-ML, outperforming all evaluated baselines. On HumanML3D, its R@1 decreases only from 0.5763 on in-distribution viewpoints to 0.5585 on out-of-distribution viewpoints. We validate portability on three downstream MoLMs. Furthermore, on the real-world RGB video dataset Fit3D, MoPort achieves 80.36\% accuracy. Code will be released upon acceptance.

cs.CV↗

arXiv2026

IGDA-ICMH: an Importance Guided Feature and Network Dual Adaptation Framework towards Image Compression for Machine and Human Vision

With the surge of machine vision applications, image compression for both machine and human vision (ICMH) has become an important research area. Existing studies mainly focus on task-specific adaptation or multi-task joint optimization, yet they often overlook the unequal contributions of different features and network layers to downstream machine vision tasks. To address this issue, we first quantitatively investigate the impact of different feature and network layers on machine vision performance, providing a principled understanding of their importance. Based on this analysis, an Importance Guided feature and network Dual Adaptation (IGDA) framework is developed for ICMH. It adopts implicit neural representation as the basic architecture and designs an efficient adaptation structure with both feature and network weight adaptation. An Importance-Guided Network Adaptation (IGNA) module is developed with a low-rank adaptation strategy to selectively adjust key network layers according to their importance in different tasks. An Importance-Guided Feature Adaptation (IGFA) module is developed with a low-rank and Kronecker product-based transform to explore the non-local property of the features in order to reduce the number of adaptation parameters. Extensive experiments on representative machine vision tasks, including image classification and object detection, demonstrate that our IGDA-ICMH framework achieves significantly better performance than the existing implicit neural representation-based methods for ICMH.

eess.IV↗

arXiv2026

A Rate-Distortion Optimized Skip Mode Coding Framework for Neural Video Compression in AVS

With the development of deep learning, neural video compression (NVC) is gaining increasing interest and is currently under standardization in the AVS (Audio Video Coding Standard). Skip mode coding, a crucial component in the traditional video compression, is also adopted in AVS NVC. Although the skip mode takes a large portion in encoding the motion and residual features in the NVC, there is a lack of investigation on the rate distortion performance of the skip mode coding. In this paper, we first thoroughly investigate the rate and distortion distribution in the skip mode coding within a spatial frame and under different temporal reference qualities, and demonstrate that distortion distribution changes spatially and temporally. Therefore, a rate-distortion optimized skip mode coding (RDOSkip) framework is proposed, where the rate and distortion in the skip mode coding is formulated based on a statistical distribution predicted by the hyperprior and the Bayes' theorem. An adaptive skip mode decision method based on spatial-temporal joint RD optimization is developed for the skip mode coding among channels and frames in a group of pictures. It addresses the limitation of the rigid fixed-distortion distribution assumption in skip mode coding within the existing methods that rely on a fixed skip threshold. The temporal skip mode decision method has been adopted in the ongoing AVS NVC standard and incorporated into the reference software. The proposed RDOSkip can be directly used in the inference without any training and with negligible complexity increase. Experimental results further demonstrate that the proposed RDOSkip can improve the performance by 0.90% and 1.29% on AVS and HEVC datasets, respectively, in terms of BD-rate saving.

eess.IV↗

arXiv2026

Tan's Contact for Trapped Spin-Orbit-Coupled Fermi Gases at Finite Temperature

Tan's contact, bridging microscopic features of many-body quantum systems to two-body contact interactions, is pivotal in the description of quantum gases. However, past studies of Tan's contact of the spin-orbit coupled Fermi gases have been exclusively discussed at zero temperature. The thermal effects on that, which cannot be neglected in most experimental conditions, has not been explored so far. Here, through a path integral Monte Carlo (PIMC) study, we show that thermal effects fundamentally alter the scaling relation between spin-orbit coupling (SOC) and Tan's contact. The linear scaling of Tan's contact with respect to the strength of SOC found at zero temperature is drastically undermined by the thermal effects. And a new mechanism based on the competition between the mean occupancy and the energy-reweighted occupancy of the opposite spin fermion pairs within the interaction range has been unveiled, which radically changes the above linear scaling to a non-monotonic dependence. Our work should be useful for future experiments to explore the preternatural thermal effects on Tan's contact of the strongly interacting spin-orbit-coupled Fermi gas.

cond-mat.quant-gas↗

arXiv2026

ScenePercept: Scenario-Conditioned Perceptual Assessment of Real-World User-Generated Content Images

The rapid development of mobile imaging and social platforms has led to explosive growth in user-generated content images, making their systematic and comprehensive perceptual assessment increasingly important. Existing image perception benchmarks are typically task-oriented, curating specific scenarios for quality or aesthetic assessment and applying a uniform perceptual criterion across diverse scenarios. This limits their ability to capture the scene diversity and perceptual complexity of real-world UGC images. To address these limitations, we introduce ScenePercept-Bench, a benchmark designed to reflect the distribution of real-world UGC images, comprising 23,100 images across 21 categories and 105 fine-grained scenes. To account for the influence of scene context on human perception, we design a hierarchical, scene-aware annotation protocol, covering visual quality, aesthetic perception, and scene semantics, with 15 attributes in total. Each image is annotated with an overall score, 15 attribute-level scores, and structured explanatory reasoning. Building on this benchmark, we further introduce Scenario-Conditioned Perceptual Alignment, a training framework that tackles the heterogeneity of perceptual criteria across scenarios. SCPA jointly optimizes absolute score prediction, preference ranking, and cross-scenario calibration, preserving the model general perceptual assessment ability while encouraging it to explore and adapt perceptual criteria across different scenarios. Extensive experiments demonstrate that SCPA achieves state-of-the-art performance in UGC image perception and generalizes effectively to established IQA and IAA benchmarks.

cs.CV↗

arXiv2026

Making MLLM Latents Matter: Factor-Aware Predictive Learning for Cinematic Video Generation

Cinematic video generation requires both temporal inference from limited inputs (i.e., sparse rather than frame-by-frame guidance) and factor-aware control of character, scene, action, and camera. Multimodal large language models (MLLMs) offer useful priors for both capabilities, but their representations are not automatically actionable for video generation: a Video Diffusion Transformer (DiT) may bypass them through native conditioning pathways, and using them does not ensure either factor-aware control or generation-compatible temporal prediction. We propose Factor-Aware Predictive Learning (FAPL), which establishes effective MLLM latent use as a shared prerequisite and then targets the two capabilities with complementary learning signals. First, latent-only generator grounding trains the DiT with a frozen MLLM and connector, strengthening the DiT's use of MLLM latents for generation. Second, for factor-aware control, paired videos specify a target-factor change and non-target preservation, and factor-span replacement trains the DiT to realize this relationship in its output. Third, for temporal inference, text-only and text-plus-complete-video observations share a video target, and the trained DiT is then frozen while the MLLM and connector are optimized through it. This final stage trains limited-input representations for generation while retaining complete-observation reconstruction as a complementary training mode. Extensive experiments demonstrate our consistently better performance on three cinematic video generation tasks (i.e., sparse-to-video, factor-aware editing, and multi-shot generation) over baselines and validate FAPL's contributions to MLLM feature use, temporal control, and factor coordination.

cs.CV↗

arXiv2026

VertMark: A Unified Training-Free Robust Watermarking Framework for Vertical Domain Pre-trained Language Models

With the application of vertical domain pre-trained language models (VPLMs) in specialized fields such as medical, finance, and law, model parameters and inference capabilities have become important digital assets. Achieving traceable copyright verification for VPLMs has become an urgent challenge. Existing copyright verification methods primarily rely on embedding backdoor watermarks into models. However, most of these methods require additional training, suffer from inefficient watermark embedding, and lack scalable designs for multiple vertical domains. To address these limitations, we propose VertMark, the first unified training-free and robust watermarking framework for copyright verification across multiple vertical domain VPLMs. The framework embeds ownership-encoded watermarks by establishing a hidden semantic equivalence between low-frequency trigger tokens and high-frequency domain-relevant words via a training-free parameter replacement strategy. Experiments demonstrate that VertMark can achieve efficient watermark embedding and reliable watermark verification for both text understanding and text generation downstream tasks in the medical, financial, and legal domains, with negligible impact on model performance. Moreover, VertMark exhibits strong robustness against various attacks (e.g., pruning and quantization), highlighting its practical value and providing strong protection for the copyright security of VPLMs.

cs.CR↗

arXiv2026

RLMuscle: Muscle-Driven Character Animation via Reinforcement Learning

Body deformation caused by muscle contraction plays a vital role in the visual realism of character animation, yet in most 3D animation pipelines artists have to manually specify muscle activations, which is time-consuming and gives no guarantee of physical realism. To address this, we propose a new method in which a reinforcement learning (RL) controller discovers per-muscle activations that track reference motions without manual tuning. Existing RL muscle controllers stop at 1D muscles; we go one step further, keeping the 1D model inside the RL control loop as a fast surrogate, and using a 3D volumetric simulation to produce the final deformation. Because both stages share the very same constitutive law, the muscle deformation is produced by the same activations that generated the motion. We further add a measured electromyography (EMG) prior that steers the redundant activation solution toward real human muscle recruitment. To achieve stable, efficient, and realistic simulation, we propose a novel geometric multigrid for Extended Position-Based Dynamics (XPBD) that is built on a coarse tetrahedral cage with barycentric-coordinate prolongation and closes a V-cycle on tetrahedral meshes. Within this GPU-parallelized solver, we inject the same Hill-type force-length law as an energy-based fiber constraint that turns per-fiber activation into realistic muscle contraction and bulging. We demonstrate volumetric muscle simulation on a 194-muscle human body with a diverse set of motions (walk, kick, swim, overhead-squat, etc.), simulating the full-body model at 18 ms per frame on a single RTX 4090. Our work takes a step toward character animation that is not only visually convincing but also biomechanically grounded. The website with code and videos is at https://chunleili.github.io/project-page-rlmuscle/.

cs.GR↗

arXiv2026

Highly integrated quantum key distribution transmitter enabled by silicon photonics

Quantum key distribution (QKD) provides information-theoretic security independent of computational assumptions, yet the bulk and cost of current systems hinder large-scale deployment. Although integrated photonic technologies have enabled highly integrated QKD chips, practical QKD transmitters are still predominantly implemented as rack-mounted systems. Here, we demonstrate a highly integrated standalone QKD transmitter that integrates all essential functionalities required for practical QKD operation within a compact platform built around a silicon photonic encoding chip. The transmitter occupies only $167 \times 56 \times 21~\mathrm{mm}^3$ ($\sim 0.2~\mathrm{dm}^3$), comparable in size to a half-height, half-width PCIe card and more than 30 times smaller in volume than a conventional 1U rack-mounted system. Paired with a conventional discrete-component receiver, it achieves a secure key rate of 219.1 kbps over 51.3 km of standard single-mode fiber, with performance comparable to that of conventional discrete-component implementations. This work bridges the gap between photonic chip integration and deployable QKD hardware, marking an important step toward transitioning QKD transmitters from conventional rack-mounted equipment to compact board-level platforms.

quant-ph↗

arXiv2026

HistoLoop: Iterative Contextual Inference with a Looped Transformer for Histology-Based Spatial Gene Expression Prediction

Predicting spatial gene expression from H&E images offers a scalable complement to experimental spatial transcriptomics. However, despite progress in spatial context modeling, how to construct subsequent context from spot states updated by earlier contextual interactions remains underexplored. We introduce HistoLoop, a looped Transformer that formulates prediction as iterative contextual inference. At each refinement step, HistoLoop aggregates the current spot states into context tokens at the corresponding resolution, processes them with a shared spatial Transformer, and retrieves spot-specific contextual information through cross-attention. A memory-mediated update integrates this information into persistent spot states, which are then used to construct context at the next, finer resolution. Under the HEST-Bench HVG50 evaluation protocol, HistoLoop achieves the highest macro-averaged gene-wise Pearson correlation among the compared methods across six cancer-specific tasks spanning five organs in HEST-1K. Ablation studies support the importance of recurrent state refinement and reconstructing context from updated spot states at progressively finer resolutions. Expression-derived clustering further suggests that HistoLoop better captures spatially organized multigene patterns.

cs.CV↗

arXiv2026

Solving Large-Scale Parameter Inference in Controllable Nuclear Fusion via Coherent Ising Machines and Block Coordinate Descent

Bayesian inference enables self-consistent plasma parameter inversion in tokamak integrated data analysis by fusing multi-source diagnostics. However, under nonlinear Gaussian likelihoods, finding the maximum a posteriori (MAP) estimate typically relies on sampling methods whose high computational overhead precludes real-time plasma control. To address this bottleneck, we propose a Bayesian inversion framework using a Coherent Ising Machine (CIM)-a quantum computing architecture based on coupled degenerate optical parametric oscillators (DOPOs). With native all-to-all connectivity, the CIM directly maps the negative log-posterior potential onto a spin Hamiltonian and leverages quantum vacuum fluctuations for rapid state-space search during pitchfork bifurcation. Validated on plasma current profile inversion for the EAST tokamak, this method achieves a single-run solution time of approximately 1 ms. Although current qubit limits necessitate a block-wise iterative scheme that prevents full real-time execution today, rapidly scaling quantum hardware offers a clear pathway toward real-time tokamak control.

quant-ph↗

arXiv2026

FrameGRPO: Incentivizing Video Reasoning via Multi-Frame Rollouts and Temporal Credit

Reinforcement learning with verifiable rewards, particularly Group Relative Policy Optimization (GRPO), has improved video reasoning in multimodal large language models. However, standard video GRPO varies only linguistic trajectories while conditioning every rollout in a group on the same sampled frames, which can produce homogeneous rewards and weaken group-relative learning when the shared visual evidence is insufficient or distracting. We propose \textbf{FrameGRPO}, a simple and effective reinforcement-learning framework that requires neither auxiliary networks nor changes to the model architecture. Specifically, FrameGRPO combines two lightweight components: (1) \textbf{Multi-Frame Rollouts}, which use only uniform temporal subsampling of a single maximal-frame decode to construct aligned views that diversify visual evidence and reasoning trajectories; and (2) \textbf{Gated Temporal Efficiency Credit}, which selectively reinforces a correct lower-ratio trajectory when all rollouts in a fixed full-ratio reference subset fail, without imposing an unconditional preference for fewer frames. Extensive experiments across diverse video understanding and reasoning benchmarks demonstrate robust overall improvements over standard GRPO, highlighting temporal-view diversification as an effective mechanism for exploration and credit assignment in video MLLMs.

cs.CV↗

arXiv2026

What the Elevation Map Cannot See: Semantic-Aware Locomotion and Execution-Aware Navigation for Humanoid Robot

Navigation for humanoid robots is critical, yet large-scale evaluation on physical hardware is often impractical due to cost and safety concerns, making simulation benchmarks essential. Existing VLN benchmarks achieve physically executable navigation, but still assume (1) all hazards are observable from elevation maps; (2) realized motions closely match desired motions. In real environments, however, fallen bottles may be ambiguous in elevation maps, while phones and water spills may be difficult to differentiate; hazard avoidance by the locomotion policy can cause the robot's actual trajectory to deviate from the path intended by the VLN policy. Such command-execution mismatch can accumulate and lead the robot toward unintended locations. To expose these failure modes, we introduce a benchmark that models both elevation-subtle hazards and execution deviations, together with a closed-loop VLN + locomotion control framework that continuously realigns high-level navigation with the robot's actual state. We evaluate navigation in simulation and further validate the locomotion policy on a physical Unitree G1 humanoid robot. Results show that semantic input reduces contact with hazards poorly represented in elevation maps, while anti-deviation improves navigation success. These findings highlight the need to evaluate humanoid navigation jointly in terms of route completion and hazard avoidance.

cs.RO↗

arXiv2026

PFedMA: Who Should Join Next as Federated Multi-Agent Orchestration Evolves?

Federated Multi-Agent Systems (FedMAS) offer a promising paradigm for collaborative reasoning among LLM-based agents while keeping raw client data decentralized. However, reliable multi-round collaboration remains challenging. Agent selection must take into consideration evolving task requirements and the agent's historical reliability, while privacy rules must adapt to changing collaboration contexts and meet the client's personalized needs. To address these challenges, we propose PFedMA, a federated multi-agent framework for feedback driven multi-round collaboration under heterogeneous privacy requirements. Across rounds, PFedMA maintains historical capability and reliability states to guide agent selection, evaluates task responses before aggregation, and refines subsequent queries according to accumulated collaboration feedback. PFedMA enables bidirectional local to global and global to local private rule evolution across heterogeneous clients. Clients generalize locally updated privacy rules and selectively incorporate globally maintained rules, thereby facilitating cross-client privacy rule evolution while preserving personalized constraints. Extensive experiments across reasoning, privacy sensitive, and adversarial settings show that PFedMA improves task performance, reduces measured privacy leakage, and improves system robustness.

cs.AI↗