arXiv subjects

Hao Yu

Publications and source records attributed to Hao Yu.

At least 19 recordsLinked to original sources

arXiv2026

Energy-flux noise induced by imperfections of the Gaussian beam in the Li-Baker HFGW detector

This paper investigates the energy-flux noise induced by imperfections of the Gaussian beam (GB) in the Li-Baker high-frequency gravitational wave (HFGW) detector. Modeling these imperfections with higher-order Laguerre-Gaussian (LG) beams, we analytically derive the resulting noise-to-signal ratio for both co- and cross-polarized $\mathrm{LG}_{nl}$ modes. It is found that higher-order co-polarized LG beams generate no tangential energy-flux noise at the optimal detection plane ($ϕ=π/2$ or $3π/2$), whereas higher-order cross-polarized LG beams introduce non-negligible noise. Specifically, for an individual higher-order cross-polarized $\mathrm{LG}_{nl}$ mode under our representative parameters, maintaining the noise-to-signal ratio below unity typically requires the power fraction to satisfy $σ_{nl}<10^{-46}$, with the $\mathrm{LG}_{01}$ mode imposing the most stringent constraint of $σ_{01}<6.0\times10^{-49}$. Monte Carlo simulations suggest that the total power fraction $σ_{\mathrm{total}}$ of multiple higher-order cross-polarized $\mathrm{LG}_{nl}$ modes needs to remain below $10^{-49}$. Consequently, we propose employing a polarization filter to retain only the co-polarized component of the GB adopted in the practical experiment, thereby suppressing the noise while preserving signal integrity. These results provide theoretical guidance for noise control in the Li-Baker HFGW detector.

gr-qc↗

arXiv2026

WeVisCut: The Whole Editing Room in One Agent From Narration to Long-Form Video

Producing long-form videos from narration requires interdependent decisions that remain coherent across shots. Existing agentic editors often use multi-stage workflows driven by general models, leaving the quality-cost trade-off of compact, task-adapted policies unclear. We address this gap with WeVisCut, a long-form script-to-video system that connects tool execution with policy learning. Its agent harness maintains shared production state so that tool feedback informs subsequent decisions and repairs. Because finished videos do not expose these decisions, our four-stage data pipeline combines editing inversion with verified replay to turn recovered editing intent into executable supervision. Applied to 3,750 finished videos, this pipeline yields over 130K training examples. We use this supervision to train a single 9B policy that coordinates production and recovers from failures within the same harness. Moreover, we introduce a 100-task benchmark and five metrics assessing editing quality and delivery reliability. Under a shared tool environment, WeVisCut substantially outperforms its base model and achieves comparable performance to Claude Opus 5 while using nearly 33x fewer generated tokens per successful output minute. It also outperforms task-matched native editors on our proposed metrics.

cs.CV↗

arXiv2026

On Hamilton cycles in quasiprimitive graphs of order $2pq$

Let $p>q>5$ be primes. We determine the socles and corresponding actions of quasiprimitive imprimitive groups of degree $2pq$, all of which are almost simple. Using this classification, we prove that a connected graph of order $2pq$ admitting a quasiprimitive automorphism group contains a Hamilton cycle, except possibly for three specified families of actions with socle $\PSL(2,p)$ or $\PSL(2,q^2)$.

math.CO↗

arXiv2026

On 2-Arc-Transitive Cayley Graphs of Nonabelian Characteristically Simple Groups

The classification of symmetric Cayley graphs has attracted considerable attention. Results have been obtained for finite abelian groups and nonabelian simple groups. However, the problem remains open for finite nonabelian characteristically simple groups, although some results on normality for small valencies are known. In this paper, we determine the possible structures of automorphism groups $X$ of connected 2-arc-transitive Cayley graphs $Γ$ of $G\cong T^k$, where $T$ is a finite nonabelian simple group and $k\geqslant2$. In the quasiprimitive case, we prove that $G\unlhd X$, $Γ$ is complete, or $X$ is of product action type with restricted parameters. For the non-quasiprimitive case, we determine the possible structures of the quasiprimitive normal quotients of $Γ$.

math.GR↗

arXiv2026

The Mathematical Theory of Double Descent as Shrinkage, not Overfitting: Matching Bounds for Path-Regularized Multilayer Neural Networks

We give a nonasymptotic generalization theory for multilayer fully connected neural networks trained with path regularization, for general Lipschitz losses and without assuming the loss bounded, the architecture sparse, or any width or depth limit. Approximation error is modelled explicitly, and along the way we settle an open problem of E et al. on approximation rates in generalized Barron spaces. We treat the underparameterized and overparameterized regimes separately, which is what allows the resulting curve to be non-monotone in the width. We then prove lower bounds on the error itself, valid at every target and at every local minimizer, and combine them with the upper bounds into a theorem asserting that the error - not merely our bound on it - descends, rises and descends again as the width grows, with a peak exceeding the final plateau by a factor diverging in the sample size. Having both sides of the bound also identifies what drives the ascent, and it is not overfitting: once the regularization exceeds the dual norm of the noise, which the prescribed schedule does at every width, a slow-rate oracle inequality controls the error with no variance term at all. The rise is instead the shrinkage demanded by the regularization that the underparameterized regime requires, and we show it cannot be removed by any width-independent regularization under which the theory's guarantees hold. This mechanism is a property of the estimator analyzed here; we do not claim it accounts for the double descent reported for unregularized training.

cs.LG↗

arXiv2026

Triangulation of orbifolds with corners

We prove that every compact orbifold with corners admits a triangulation in which the image of every codimension one face is a subcomplex. Faces need not be embedded, and the local groups may permute boundary components. The proof turns the boundary hyperplanes of linear charts into mirrors by means of the squaring map, which, unlike the folding $y\mapsto|y|$, is compatible with smooth changes of charts, and then applies the triangulation theorem for orbit spaces of proper Lie groupoids.

math.GT↗

arXiv2026

Geometric chains of orbifolds with corners and singular homology

We study a geometric homology theory (GHT for short). Its chains are generated by smooth maps from compact oriented orbifolds with corners to a smooth space, and two kinds of chains are set to zero: those admitting an orientation reversing automorphism, and degenerate ones, which, together with their boundaries, factor through smooth maps to orbifolds of lower dimension. Bisecting the source of a geometric chain along a hypersurface transverse to its corners changes the chain only by the boundary of an explicit prism and by prisms over its faces; cutting a geometric cycle along a regular level set of a smooth function on the target does not change its homology class. Geometric chains also behave well under products and pullbacks along fibre bundles, which overcomes some of the combinatorial rigidity of singular chains. We prove that geometric homology is naturally isomorphic to singular homology with rational coefficients for every smooth space admitting a good cover, such as smooth manifolds, orbifolds and locally finite polyhedra, and also for orbifolds with corners. The proof uses cutting operators along level sets of functions on the target, realized by explicit prisms, instead of triangulations. For continuous maps into arbitrary spaces we conjecture the same statement, and prove it for Euclidean neighbourhood retracts in degrees at most one, with surjectivity in degree two. Without the relation killing degenerate chains the comparison fails for orbifold chains: the weighted projective plane $\mathbb{P}(1,1,p)$, $p\ge5$ prime, mapped to a point is a cycle that does not bound. Applications on Morse theory and the BV structure on geometric chains of moduli spaces of bordered Riemann surfaces, following Costello and Harrelson--Voronov--Zúñiga, are given.

math.GT↗

arXiv2026

Learning to Pose Problems: Reasoning-Driven and Solver-Adaptive Data Synthesis

Data synthesis for training large reasoning models offers a scalable alternative to limited, human-curated datasets, enabling the creation of high-quality data. However, existing approaches face several challenges: (i) indiscriminate generation that ignores the solver's ability and yields low-value problems, or reliance on complex data pipelines to balance problem difficulty; and (ii) a lack of reasoning in problem generation, leading to shallow problem variants. In this paper, we develop a problem generator that reasons explicitly to plan problem directions before synthesis and adapts difficulty to the solver's ability. We recover problem-design CoT from human-authored multi-part questions, using later subquestions as targets to infer the design rationale. These traces initialize the generator, which is then optimized with a seed-conditioned reward based on solver answer consistency. The reward guides difficulty adjustment relative to each seed while favoring problems near the solver's competence boundary. Across 10 mathematical and general reasoning benchmarks, our method outperforms seed-only training by 2.69 and 3.39 percentage points after one and three rounds, respectively. The framework also extends to vision-language models. Our code is publicly available at https://github.com/WalkerWorldPeace/Reasoning-Synthesis.

cs.AI↗

arXiv2026

InfiniSplat v2: Analyzing and Improving Feed-Forward 3DGS from Any Views

Feed-forward Gaussian reconstruction builds 3D scenes directly from input images for novel view rendering, but learning Gaussian attributes from predicted geometry for high-quality rendering under large viewpoint changes remains challenging. We analyze limitations in Gaussian attribute learning and introduce InfiniSplat v2, which improves rendering quality through architectural and training changes. Our method decouples geometry and attribute prediction with a frozen geometry branch, aligns predicted Gaussians using ground-truth camera poses during training, and adopts large-angle target view supervision. Across six datasets, the feed-forward outputs of the same trained model achieve state-of-the-art mean LPIPS and DISTS among the evaluated methods in both pose-conditioned and pose-free settings. In the pose-conditioned setting, mean LPIPS and DISTS decrease by 25.8% and 32.9%, respectively, compared with the strongest evaluated baselines. We further find that opacity learned with only image reconstruction supervision suppresses geometrically inconsistent points. Project page: https://zju3dv.github.io/InfiniSplat2/

cs.CV↗

arXiv2026

DIME: Scaling Facial Representation Learning via Differential Masked Autoencoding

Scaling up data and model capacity has driven substantial progress in visual representation learning. However, conventional pretext tasks exhibit diminishing returns on fine-grained facial analysis. We attribute this to a scaling bottleneck: these tasks model each image's appearance and are dominated by identity, pose, and illumination, so subtle within-identity facial differences contribute little to the training signal. We introduce DIME, a differential masked autoencoder for large-scale facial representation learning. DIME builds on a simple observation: two images of the same person share identity-specific appearance, so their difference exposes facial changes. DIME reconstructs both masked images in an identity-matched pair and, through an Explicit Differential Direction Supervision (EDDS) objective, aligns the direction of their reconstructed difference with the true difference, while source isolation makes each reconstruction depend only on its own image. Shared appearance cancels in this difference, and because EDDS ignores magnitude, subtle facial changes count as much as large pose or illumination changes. DIME uses identity labels only to form pretraining pairs, requires no dense annotations, and learns dense facial representations with emergent part-level semantics. We pretrain DIME on 20 million high-resolution facial images consolidated from existing face datasets. DIME's AU detection performance continues to improve as pretraining data scales from 6M to 20M images, with its BP4D F1 increasing by 5.9 points, while MAE gains only 0.5 points on the same subsets. DIME also outperforms specialized facial methods on AU detection, AU intensity estimation, cross-domain anti-spoofing, landmark localization, head pose estimation, face parsing, and face generation.

cs.CV↗

arXiv2026

Frozen History: When Temporal Losses Stop Accumulation in Learned Real-Time Supersampling

Learned real-time supersampling accumulates jittered low-resolution samples along motion vectors. Many methods use a temporal consistency loss to reduce flicker. We show that with accurate registration, this loss can suppress accumulation: on static content, the network copies its warped history at almost no temporal cost. Camera jitter in motion vectors raises this cost and can hide the effect. We identify four registration errors that training can conceal: jitter-carrying motion vectors, one motion field for both resolution lattices, jittered depth, and an unregistered skip path. A model-free oracle shows that correct registration makes freezing two orders of magnitude cheaper on static pixels. Controlled training shows that registration reverses the temporal weight's effect on PSNR. Under exact registration, increasing the weight reduces pixel variance at the expense of accuracy and perceived quality, with a 0.7 to 0.9 dB loss on static frames at weight 0.25. This reversal recurs across three network families and two scales; the loss also reduces accuracy on unjittered STSS data. With a small temporal weight and a normalised sample accumulator, a plain registered network improves the best published QRISP PSNR by 0.34 to 0.62 dB at $\times$2, $\times$3 and $\times$4 upscaling to 1080p. At $\times$2, its static pixel standard deviation is one quarter that of DLSS 2. Without retraining, it outperforms classical temporal upsampling by at least 4.4 dB on Arm's jittered Bistro capture. Our most accurate $\times$4 model runs in 2.03 ms per 1080p frame on an H200. Code: https://github.com/CV-AC/Frozen_history

cs.CV↗

arXiv2026

TRACE: Rollout-Guided Quantization-Aware Training for FP4 Reinforcement Learning of MoE Language Models

Reinforcement learning (RL) for post-training large language models (LLMs) incurs substantial computation and memory overhead during rollout generation, which motivates low-precision rollout for efficient RL training. However, existing FP4 RL methods suffer from a key limitation: they primarily optimize quantization accuracy on the training and rollout paths independently rather than directly reducing the discrepancy between the two quantized execution paths. In this work, we propose TRACE (Train-Rollout Quantization Alignment via Compact GuidancE), an FP4 quantization framework for RL training of Mixture-of-Experts (MoE) language models that addresses the limitation of existing FP4 RL methods. TRACE incorporates rollout-guided quantization-aware training that uses rollout-side quantization outcomes to guide training-side FP4 rounding decisions, directly reducing train-rollout discrepancy. Moreover, TRACE adopts an efficient quantization-information caching scheme that selectively retains mantissa and scale information from deeper layers to reduce the storage and communication overhead introduced by rollout guidance. We evaluate TRACE on four large-scale MoE language models across reasoning, coding, and long-horizon RL tasks. Our results demonstrate that TRACE enables joint FP4 weight/activation and FP4 KV-cache rollout with RL performance comparable to BF16 rollout, while achieving up to 5.4xrollout speedup and strong final FP4 performance compared with post-hoc FP4 quantization of BF16-trained policies.

cs.LG↗

arXiv2026

How Private is Private? A Comparative Study for Face De-Identification

Face de-identification (FDeID) has emerged as a critical privacy-preserving technology, yet its evaluation remains fundamentally fragmented. Existing protocols rely on inconsistent metrics, heterogeneous datasets, and partial annotation coverage, so methods targeting different utility dimensions, such as landmark versus expression preservation, are reported on different benchmarks under different metrics, rendering cross-method comparison infeasible. We revisit FDeID evaluation from both the data and metric perspectives. On the data side, we introduce UtilFace, a curated, demographically balanced benchmark with high identity diversity, assembled from four large-scale face datasets through identity-aware cleaning, resolution enhancement, and stratified filtering. On the metric side, we propose HiFD, a Hierarchical Face De-identification metric that unifies identity suppression, multi-level utility preservation, and image quality under a single consistency-based paradigm: every component is computed from pretrained estimators' outputs on the original face and its de-identified counterpart, directly quantifying how much identity is suppressed and how much downstream-perceivable utility survives. HiFD organizes facial signals into a three-level utility hierarchy spanning macro cues (L1), micro cues (L2), and imperceptible cues (L3), and aggregates the five resulting components into a single interpretable score via weighted harmonic mean, with configurable application-specific profiles. Using this unified protocol, we conduct a comprehensive comparative study spanning adversarial, GAN-based, and diffusion-based methods, surfacing trade-offs and failure modes that remain invisible under existing protocols. We release the benchmark and evaluation toolkit to foster systematic and reproducible research in privacy-preserving human face analysis.

cs.CV↗

arXiv2026

Photonic Chips for Universal Quantum Computing: Retrospective and Prospective

Integrated photonic quantum chips have emerged as a promising platform for realizing fault-tolerant photonic quantum computers. Compared with bulk-optics systems, integrated photonics offer compact device footprints, enhanced programmability, and exceptional stability, attributes that are essential for executing large-scale and computationally demanding quantum tasks. In this review, we provide a comprehensive overview of recent advances in both discrete-variable and continuous-variable universal photonic quantum computation. We survey progress in fault-tolerant architectures and state-of-the-art integrated platforms and highlight the key challenges that must be addressed to scale photonic systems toward full fault tolerance. Finally, we outline future opportunities and research directions aimed at bridging the gap between current proof-of-principle demonstrations and fully scalable quantum microprocessors.

quant-ph↗

arXiv2026

OneSearch-VL: Unified Multimodal Deep Research Agent for Image and Video

Single-image, multi-image, and video deep research require different visual operations but share a workflow of visual grounding, external retrieval, and fact composition. A key challenge is to preserve the dependencies linking localized visual anchors, entity relations, source-supported facts, and answer-producing operations. We introduce OneSearch-VL, a unified agent centered on the Visually Grounded Evidence Graph (VGEG), which encodes these dependencies as a shared task-level reference for data construction, process supervision, and operation-level evaluation. Our VGEG-based data engine constructs and verifies multi-image and video questions and filters expert trajectories. Using these data, we assemble OneSearch-VL-SFT-110K and OneSearch-VL-RL-10K for SFT and RL, respectively. We further derive the Evidence-aware Visual-Grounded Rubric reward (EVGR) from VGEG annotations to supervise evidence traceability and visual grounding during RL. For fine-grained evaluation, we construct OneSearch-MI-Bench and OneSearch-Video-Bench, organizing questions by the research operations encoded in their VGEGs. Experiments show that OneSearch-VL-8B improves over Qwen3-VL-8B with tool access by 20.2 and 17.6 percentage points on the two new benchmarks, respectively, while also achieving substantial gains across 7 image benchmarks and VideoDR. Project repository: https://github.com/appletea233/OneSearch-VL

cs.CV↗

arXiv2026

Strategy-proof voting schemes under single-peaked preferences over intervals

When a subset of alternatives is allowed to be the collective choice, one approach is to study the social choice correspondence defined on agents' preferences over alternatives. This paper considers a different approach. We argue that agents should have preferences directly over outcomes. We consider the social choice problem on a closed real interval, and assume a single-peaked domain over intervals. Our focus is on the peak-only social choice function, referred to as the voting scheme. The leftright scheme, which generalizes the minmax scheme, is the only strategy-proof voting scheme. Adding anonymity, it reduces to the generalized median scheme. For an odd number of agents, the median scheme is the only voting scheme satisfying efficiency, anonymity, and strategy-proofness.

econ.TH↗

arXiv2026

Exact Counts of Binary Phylogenetic Networks with Four Reticulations

Phylogenetic networks provide a flexible framework for representing reticulate evolutionary processes, such as hybridization, introgression, recombination, and horizontal gene transfer. However, their combinatorial complexity makes even basic enumeration problems difficult. Building on our previous work for networks with up to three reticulations, we derive an explicit closed-form formula for the number of unrestricted rooted binary phylogenetic networks with four reticulations on $n$ labeled taxa. Our approach is based on tree-component graphs. We classify the 79 possible component graphs corresponding to networks with four reticulations into ten groups. We then enumerate the networks associated with each group by combining known counts of one-component networks, forests, and networks with fewer reticulations. Summing these contributions yields the desired formula. This result extends the exact enumeration of unrestricted binary phylogenetic networks to four reticulations and further demonstrates the effectiveness of component graphs for systematically organizing and counting increasingly complex network classes.

q-bio.PE↗

arXiv2026

Counting and Min-Cost Encoding for Tokenization in Large Language Models

Mainstream large language models rely on a tokenizer to encode text into a token sequence. Different tokenizers may yield token sequences of substantially different lengths for the same text. With a fixed model architecture, shorter token sequences correspond to lower inference time. We propose a tokenizer training approach named Counting and Filtering (CNF) and a text encoding algorithm called Min-Cost Encoding (MCE). MCE defines a cost function over a text segment, and determines the best segmentation by globally minimizing the overall segmentation cost. CNF builds a raw vocabulary by directly counting valid substrings, and then constructs the final vocabulary through a filtering step based on actual token usage when segmenting the training corpus with MCE. The CNF-MCE conbination offers several advantages over BPE, including higher token efficiency, greater scalability, and lower dependency. Across six text categories and two vocabulary-size groups, CNF-MCE consistently achieves better compression than the evaluated BPE tokenizers. With a 250K vocabulary, CNF-MCE increases compression rate by 26% and 30% on English web text over the o200k_base and qwen250k tokenizers. Experiments scaling the vocabulary to 1M entries on English web text demonstrate sustained improvements over BPE, with a token efficiency improvement of over 60% and vocabulary utilization rising from 52.9% to 96.9%. The MCE algorithm does not depend on a merge list (as in BPE) or token probability (as in UnigramLM), making it applicable to a wide range of vocabularies, including those built from BPE, UnigramLM, CNF, and others. Language models trained from scratch at the 1.8B and 8B scales achieve comparable average performance to models using the BPE tokenizers across 11 benchmarks. These results demonstrate that CNF-MCE can improve token efficiency significantly while maintaining competitive downstream performance.

cs.CL↗