arXiv subjects

Yao Yu

Publications and source records attributed to Yao Yu.

At least 19 recordsLinked to original sources

arXiv2026

What a Model Refuses, a State Fears: How Authoritarian Information Control Reproduces in Language-Model Guardrails

As large language models become the front door to political information, what they refuse to discuss becomes a new instrument of information control. We argue that a model's guardrail encodes not a universal notion of harm but the political threat model of the state that governs its developer, and we derive the expected structure of that control from the comparative study of how authoritarian regimes censor. Across ten models and three languages, Chinese guardrails carry its signatures: they answer to the developer's own regime, refusing identical collective-action prompts far more when a prompt names China than a foreign state; within politics they target the capacity to coordinate rather than dissent, declining even to help organize pro-government mobilization; and their strictness is porous, collapsing under adversarial paraphrase, so that the models most resistant to attack are Western frontier systems, not the strictest refusers. Machine censorship thus reproduces the friction-based logic of prior-era information control while, lacking a censor's case-by-case judgment, proving blunter than the bureaucracy it resembles---so that audits which measure refusal directly overstate how controlled a model actually is.

cs.CY↗

arXiv2026

A Class of Exact Single-Field Inflationary Solutions beyond Slow Roll

We construct exact solutions for single-field inflaton dynamics without invoking the slow-roll approximation. A suitable change of variables reduces the background equation to an Abel equation of the first kind. Although a generic Abel equation is not analytically solvable, we identify a class of inflaton potentials for which the transformed equation admits exact solutions. The resulting framework contains constant-roll inflation as a special case and also accommodates solutions with a constant second Hubble-flow parameter. We analyze the linear local attractor behavior and superhorizon evolution of these rolling backgrounds. Using the public joint CMB likelihood contours in the $(n_s,r)$ plane, we identify compatible parameter regions and show that one rolling branch can also yield $50\leq N_*<60$. Direct numerical evolution of the scalar and tensor modes at representative points validates the local-index predictions to better than $7\times10^{-4}$ in $n_s$ and $2\times10^{-6}$ in $r$. The exact family extends beyond slow roll, although the observationally selected regions displayed here lie close to the slow-roll regime.

astro-ph.CO↗

arXiv2026

DSAR: Dual-Stream Autoregressive Modeling of Temporal Cloth Dynamics for Photorealistic Animatable Avatars

Creating photorealistic and temporally coherent animatable human avatars from RGB videos remains challenging. Current methods struggle to capture realistic cloth dynamics, producing over-smoothed appearance or severe artifacts on out-of-distribution poses. This limitation stems from a fundamental oversight: existing approaches neglect the temporal causality inherent in cloth physics, where current states emerge from previous states through temporal evolution rather than instantaneous skeletal configurations alone. Without explicit modeling of this causal structure, networks learn pose-appearance correlations instead of motion evolution, leading to poor generalization. We introduce a dual-stream autoregressive framework that explicitly models both observable geometric information and implicit internal state. The geometric stream propagates surface displacement from the previous frame, while the state stream fuses current features with historical states retrieved from a memory bank. Motion-adaptive aggregation handles spatially-varying dynamics, and adaptive regularization balances smoothness with flexibility. Experiments on challenging datasets demonstrate significant improvements in rendering quality, temporal consistency, and generalization to motion patterns beyond training distributions, validating that dual-stream temporal modeling enables realistic cloth dynamics.

cs.CV↗

arXiv2026

Angular and invariant-mass observables in the four-body Higgs decay $h\to\ell\bar{\nu}_\ell\bar{\ell}^\prime\nu_{\ell^\prime}$

We study the angular distribution of the Higgs boson decay $h\to\ell\bar{\nu}_\ell\bar{\ell}^\prime\nu_{\ell^\prime}$ with $\ell\neq\ell^\prime$. Due to the presence of two undetected neutrinos, a complete angular analysis is not experimentally feasible. To overcome this, we reorganize the kinematics from the conventional lepton-neutrino pairs into a charged-lepton pair and a neutrino pair, i.e.~$\ell\bar{\ell}^\prime$ and $\bar{\nu}_\ell\nu_{\ell^\prime}$. This allows us to express the differential decay rate in terms of experimentally accessible variables, including the invariant mass squared of the neutrino pair. Using the effective field theory framework, we derive this rate and integrate over the neutrino-associated angles. This parametrization provides a clean and measurable angular distribution, offering a new probe of the $hWW$ coupling and possible beyond-the-Standard-Model contributions.

hep-ph↗

arXiv2026

CAREAgent: Clinical Agent with Structured Reasoning and Tool-Integrated for Order Generation

Clinical order generation serves as a critical bridge between clinical decision-making and real-world practice, translating medical decisions into concrete and executable orders. Existing agents mainly focus on coarse-grained decisions and overlook the fine-grained, executable information required for clinical orders. To address this gap, we propose CAREAgent, an agent for clinical order generation. To support its training, we introduce a two-stage agentic reasoning data construction method. First, we design an agent framework that constructs verifiable reasoning trajectories aligned with realistic clinical tool usage. Second, we filter reasoning trajectories by format compliance, order validity, and clinical plausibility. Building on the constructed data, the model is first trained via supervised fine-tuning to acquire fundamental reasoning formats and medical knowledge, and is subsequently optimized through reinforcement learning with multi-dimensional reward functions to enhance complex clinical reasoning capabilities. Experiments on multiple benchmarks demonstrate the effectiveness of CAREAgent. On ClinicalBench (unseen during training), CAREAgent improves the F1 score by 5.05%, 2.09%, and 0.86% over the single-agent, multi-agent, and agentic reasoning methods, respectively.

cs.AI↗

arXiv2026

Network-Level Travel Time Prediction Considering The Effects of Weather and Seasonality

Accurately predicting travel time information can be helpful for travelers. This study proposes a framework for predicting network-level travel time index (TTI) using machine learning models. A case study was performed on more than 50,000 TTI data collected from the Washington DC area over 6 years. The proposed approach is also able to identify the effects of weather and seasonality. The performances of the machine learning models were assessed and compared with each other. It was shown that the ridge regression model outperformed the other models in both short-term and long-term predictions.

stat.AP↗

arXiv2025

Conditions for Equivalence of Random Interlacements and Random Walk Reflected off of Infinity

On a transient weighted graph, there are two models of random walk which continue after reaching infinity: random interlacements, and random walk reflected off of infinity, recently introduced in arXiv:2506.18827 [math.PR]. We prove these two models are equivalent if and only if all harmonic functions of the underlying graph with finite Dirichlet energy are constant functions, or equivalently, the free and wired spanning forests coincide. In particular, examples where the models are equivalent include $\mathbb{Z}^d$, cartesian products, and many Cayley graphs, while examples that fail the condition include all transient trees.

math.PR↗

arXiv2025

Testing Non-Standard Neutrinos in Purely Leptonic Lepton Decays

We propose a method to probe sterile neutrinos using polarization observables in the purely leptonic decays $\ell^{\prime-} \to \ell^{-} \bar{\nu}_{\ell} \nu_{\ell^{\prime}}$. By analyzing angular distributions and asymmetries derived from polarized decay rates, we identify distinctive signatures of sterile neutrino mixing. In particular, we demonstrate that sterile neutrinos can induce singularities in certain asymmetry parameters as functions of the invariant mass squared of the neutrino pair. These singularities occur for sterile neutrino masses $m_{4\nu}$ satisfying $m_{4\nu}^2 < m_{\ell^{\prime}}^2 / 2$, providing a clear target for experimental investigation. Our results motivate the incorporation of polarized beam sources at future colliders to enhance sensitivity to sterile neutrinos and other new physics.

hep-ph↗

arXiv2025

Power-Law Inflation Survives Observational Constraints

Power-law inflation has stood as a classical model in inflationary cosmology since the early 1980s, prized for its exact analytical solutions and ability to naturally resolve the Big Bang theory's horizon and flatness problems through exponential expansion. However, its simplest form appears incompatible with modern precision observations, motivating increasingly complex alternatives. In this work, we demonstrate how previous predictions with power-law inflation considered only a particular solution of the field equations, and derive the complete set of general analytical solutions that satisfy current theoretical and observational constraints. This finding revitalizes power-law inflation as a viable framework, offering new possibilities for cosmological model-building while preserving its original mathematical elegance.

astro-ph.CO↗

arXiv2025

CRAFT: Time Series Forecasting with Cross-Future Behavior Awareness

The past decades witness the significant advancements in time series forecasting (TSF) across various real-world domains, including e-commerce and disease spread prediction. However, TSF is usually constrained by the uncertainty dilemma of predicting future data with limited past observations. To settle this question, we explore the use of Cross-Future Behavior (CFB) in TSF, which occurs before the current time but takes effect in the future. We leverage CFB features and propose the CRoss-Future Behavior Awareness based Time Series Forecasting method (CRAFT). The core idea of CRAFT is to utilize the trend of cross-future behavior to mine the trend of time series data to be predicted. Specifically, to settle the sparse and partial flaws of cross-future behavior, CRAFT employs the Koopman Predictor Module to extract the key trend and the Internal Trend Mining Module to supplement the unknown area of the cross-future behavior matrix. Then, we introduce the External Trend Guide Module with a hierarchical structure to acquire more representative trends from higher levels. Finally, we apply the demand-constrained loss to calibrate the distribution deviation of prediction results. We conduct experiments on real-world dataset. Experiments on both offline large-scale dataset and online A/B test demonstrate the effectiveness of CRAFT. Our dataset and code is available at https://github.com/CRAFTinTSF/CRAFT.

cs.LG↗

arXiv2025

Discontinuous hybrid neural networks for the one-dimensional partial differential equations

A feedforward neural network, including hidden layers, motivated by nonlinear functions (such as Tanh, ReLU, and Sigmoid functions), exhibits uniform approximation properties in Sobolev space, and discontinuous neural networks can reduce computational complexity. In this work, we present a discontinuous hybrid neural network method for solving the partial differential equations, construct a new hybrid loss functional that incorporates the variational of the approximation equation, interface jump stencil and boundary constraints. The RMSprop algorithm and discontinuous Galerkin method are employed to update the nonlinear parameters and linear parameters in neural networks, respectively. This approach guarantees the convergence of the loss functional and provides an approximate solution with high accuracy.

math.NA↗

arXiv2025

Randomized Routing to Remote Queues

We study load balancing for a queueing system where parallel stations are distant from customers. In the presence of traveling delays, the join-the-shortest-queue (JSQ) policy induces queue length oscillations and prolongs the mean waiting time. A variant of the JSQ policy, dubbed the randomized join-the-shortest-queue (RJSQ) policy, is devised to mitigate the oscillation phenomenon. By the RJSQ policy, customers are sent to each station with a probability approximately proportional to its service capacity; only a small fraction of customers are purposely routed to the shortest queue. The additional probability of routing a customer to the shortest queue, referred to as the balancing fraction, dictates the policy's performance. When the balancing fraction is within a certain range, load imbalance between the stations is negligible in heavy traffic, so that complete resource pooling is achieved. We specify the optimal order of magnitude for the balancing fraction, by which heuristic formulas are proposed to fine-tune the RJSQ policy. A joint problem of capacity planning and load balancing is considered for geographically separated stations. With well planned service capacities, the RJSQ policy sends all but a small fraction of customers to the nearest stations, rendering the system asymptotically equivalent to an aggregated single-server system with all customers having minimum traveling delays. If each customer's service requirement does not depend on the station, the RJSQ policy is asymptotically optimal for reducing workload.

math.PR↗

arXiv2024

A phenomenological estimate of rescattering effects in $B_s\to K^{0}\bar{K}^{0}$

The measurements in $b\to s$ penguin-dominated decays are widely recognized as a powerful test for searching for New Physics by studying the deviation from theoretical estimations within the Standard Model. We examine the final-state rescattering effects on the decay $B_s\to K^{*0}\bar{K}^{*0}$ and provide estimations of the branching ratio and longitudinal polarization of $B_s\to K^{*0}\bar{K}^{*0}$, which is consistent with experimental observations. Our conclusion is that both short- and long-distance interactions contribute significantly in this decay. The small longitudinal polarization in $B\to VV$ modes may not be a signal for New Physics.

hep-ph↗

arXiv2024

Van der Waals Magnetic Electrode Transfer for Two-Dimensional Spintronic Devices

Two-dimensional (2D) materials are promising candidates for spintronic applications. Maintaining their atomically smooth interfaces during integration of ferromagnetic (FM) electrodes is crucial since conventional metal deposition tends to induce defects at the interfaces. Meanwhile, the difficulties in picking up FM metals with strong adhesion and in achieving conductance match between FM electrodes and spin transport channels make it challenging to fabricate high-quality 2D spintronic devices using metal transfer techniques. Here, we report a solvent-free magnetic electrode transfer technique that employs a graphene layer to assist in the transfer of FM metals. It also serves as part of the FM electrode after transfer for optimizing spin injection, which enables the realization of spin valves with excellent performance based on various 2D materials. In addition to two-terminal devices, we demonstrate that the technique is applicable for four-terminal spin valves with nonlocal geometry. Our results provide a promising future of realizing 2D spintronic applications using the developed magnetic electrode transfer technique.

cond-mat.mes-hall↗

arXiv2024

Novel Model-Independent Approach to Explore New Physics in Five-body Semileptonic Decays

Substantial contribution of the tensor current in semileptonic decays is regarded as a clear signal for new physics. In this work, we propose a model-independent approach to unambiguously test contribution of the tensor current in semileptonic five-body decays $\bar{D}_{(s)}/\bar{B}_{(s)}\to V\ell\bar{\nu}_{\ell}\,(\ell=e,\mu,\tau)$ with $V\to \pi^0\pi^+\pi^-$, where $V$ denotes vector particles. We derive three parameters associated with the angular asymmetry, which are always equal to one in the Standard Model regardless the data of form factor but will deviate if contribution of the tensor current doesn't vanish. The outcomes have potential applications in precisely testing the Standard Model and searching for new physics. Relevant measurements can be performed using data collected by BESIII, Belle~II, and LHCb.

hep-ph↗

arXiv2024

Five-body $D\to V$ Semileptonic Decays

Our main objective is to derive the decay rate for the semileptonic decays $D\to V\ell^+\nu_{\ell}\,(\ell=e,\mu)$, where $V$ represents a vector particle. In these decays, the vector particle $V$ decays into three pseudo-scalar particles. To accomplish this, we evaluate the phase-space factor for the five-body decay with a set of eight independent variables which uniquely define a point in the phase space. We further conduct a detailed investigation of the $D\to \omega\ell^+\nu_{\ell}$, where $\omega$ subsequently decays into $\pi^+\pi^-\pi^0$, within the Standard Model and in a general effective field theory description of the weak interactions at low energies. The outcomes of this study have potential applications in the measurement of $D\to \omega$ form factors. These measurements can be performed using data obtained from BESIII.

hep-ph↗

arXiv2024

A Constrained Deep Reinforcement Learning Optimization for Reliable Network Slicing in a Blockchain-Secured Low-Latency Wireless Network

Network slicing (NS) is a promising technology that supports diverse requirements for next-generation low-latency wireless communication networks. However, the tampering attack is a rising issue of jeopardizing NS service-provisioning. To resist tampering attacks in NS networks, we propose a novel optimization framework for reliable NS resource allocation in a blockchain-secured low-latency wireless network, where trusted base stations (BSs) with high reputations are selected for blockchain management and NS service-provisioning. For such a blockchain-secured network, we consider that the latency is measured by the summation of blockchain management and NS service-provisioning, whilst the NS reliability is evaluated by the BS denial-of-service (DoS) probability. To satisfy the requirements of both the latency and reliability, we formulate a constrained computing resource allocation optimization problem to minimize the total processing latency subject to the BS DoS probability. To efficiently solve the optimization, we design a constrained deep reinforcement learning (DRL) algorithm, which satisfies both latency and DoS probability requirements by introducing an additional critic neural network. The proposed constrained DRL further solves the issue of high input dimension by incorporating feature engineering technology. Simulation results validate the effectiveness of our approach in achieving reliable and low-latency NS service-provisioning in the considered blockchain-secured wireless network.

eess.SP↗

arXiv2023

How many preprints have actually been printed and why: a case study of computer science preprints on arXiv

Preprints play an increasingly critical role in academic communities. There are many reasons driving researchers to post their manuscripts to preprint servers before formal submission to journals or conferences, but the use of preprints has also sparked considerable controversy, especially surrounding the claim of priority. In this paper, a case study of computer science preprints submitted to arXiv from 2008 to 2017 is conducted to quantify how many preprints have eventually been printed in peer-reviewed venues. Among those published manuscripts, some are published under different titles and without an update to their preprints on arXiv. In the case of these manuscripts, the traditional fuzzy matching method is incapable of mapping the preprint to the final published version. In view of this issue, we introduce a semantics-based mapping method with the employment of Bidirectional Encoder Representations from Transformers (BERT). With this new mapping method and a plurality of data sources, we find that 66% of all sampled preprints are published under unchanged titles and 11% are published under different titles and with other modifications. A further analysis was then performed to investigate why these preprints but not others were accepted for publication. Our comparison reveals that in the field of computer science, published preprints feature adequate revisions, multiple authorship, detailed abstract and introduction, extensive and authoritative references and available source code.

cs.DL↗