arXiv subjects

Hang Yu

Publications and source records attributed to Hang Yu.

At least 19 recordsLinked to original sources

arXiv2026

A universal relation predicts the observed reversal of a supernova chirp

Superluminous supernovae, among the brightest stellar explosions, are thought to be powered by newborn magnetars. One such event, SN 2024afav, ``chirped'', displaying sinusoidal, increasing-frequency ($P(99\textrm{ d})\sim39\pm8$ d to $P(166\textrm{ d})\sim17\pm5$ d) light-curve modulations. The model explaining this chirp, a precessing disk falling onto the dimming magnetar, generalizes to a normalization-independent universal relation between the period derivative and the dimming rate of the light curve. Applied to the published chirp alone, with the spin-down time measured from the unmodulated light curve, the relation predicts that the chirp must halt near $t \approx 150$ d and reverse. With continued follow-up of SN 2024afav, we report the discovery of five new modulations in which the chirp reverses ($P(177\textrm{ d})\sim24\pm4$ d to $P(208\textrm{ d})\sim38\pm2$ d) at $t=147\pm3$ d, as predicted. Applied to chirping events from upcoming surveys, the relation could identify the precession mechanism and disk physics of each engine.

astro-ph.HE↗

arXiv2026

More Is Better? Exploring Latent Correspondence in Federated Hypergraph Learning

Federated hypergraph learning with latent cross-client hyperedge correspondence poses fundamental identifiability and computational challenges, as the same local release can correspond to multiple global hypergraph structures. We characterize the tractability of recovering these latent correspondences, showing tractable pairwise assignment but generally intractable multi-client hyperedge matching. Building on this characterization, we derive a cross-entropy decomposition that reveals when local prediction can outperform predictors fitted with additional cross-client information. Motivated by this principle, we introduce Federated Adaptive Spectral Training with Representation Alignment (FedASTRA), which learns correspondence-invariant diffusion representations through adaptive heat diffusion and masked latent prediction while communicating model parameters only. We establish its asymptotic optimality with respect to the permitted local information and prove global population-risk convergence under explicit conditions on recovery, representation, and update alignment. Experiments on eight real-world datasets demonstrate the effectiveness of FedASTRA against identifier-free federated graph and hypergraph methods. On ogbn-papers100M, FedASTRA reaches the performance-communication Pareto frontier, achieving 50.07 Accuracy and 29.73 Macro-F1 while communicating only 59.60 MiB per round.

cs.LG↗

arXiv2026

A Conservative Multi-Level Adaptive Velocity-Space Method for the Discrete Unified Gas-Kinetic Scheme

Multiscale gas flows often span a wide range of Knudsen numbers, from near-continuum to highly rarefied and strongly nonequilibrium conditions. Such flows require kinetic solvers that remain accurate across these regimes. The discrete unified gas-kinetic scheme (DUGKS) provides a unified description, but its computational cost and memory consumption are dominated by the size of the discrete velocity space. To alleviate this bottleneck, this work develops the multi-level velocity-space adaptive discrete unified gas-kinetic scheme (MLVS-DUGKS), reducing velocity-space redundancy while preserving accuracy and conservation. The method identifies the velocity-space requirements of individual physical cells from their distribution functions and groups these requirements into a small number of shared representative velocity spaces. This organization retains sensitivity to different local flow states without constructing an independent velocity space for every physical cell. A linear moment-constrained correction is incorporated to maintain discrete compatibility and conservative data transfer between representative spaces. Benchmark simulations spanning near-continuum to highly rarefied regimes show close agreement with the uniform-velocity-space DUGKS (UVS-DUGKS). For the cylinder and two-cavity problems, the average number of discrete velocity points is reduced by approximately one order of magnitude, yielding speedups of $7.00$--$7.93$ and GPU-memory reductions by factors of $6.73$--$7.49$. As a result, the MLVS-DUGKS provides an efficient tool for the simulation of multiscale nonequilibrium gas flows.

physics.flu-dyn↗

arXiv2026

Image-Domain CT Metal Artifact Reduction Using an Endpoint-Conditioned Diffusion Bridge

Background: Metal implants cause streaking and shading artifacts in computed tomography (CT), obscuring anatomy and distorting Hounsfield-unit (HU) values. Conventional diffusion models reconstruct images from Gaussian noise rather than directly exploiting anatomical information retained in artifact-corrupted CT. Purpose: To develop an image-domain metal artifact reduction (MAR) method connecting artifact-corrupted and metal-free CT images through an endpoint-conditioned diffusion bridge. Methods: The proposed RDBM-MAR method uses an endpoint-conditioned diffusion bridge and initializes reconstruction from the artifact CT image. A conditional U-Net predicts an image-domain correction from intermediate bridge states. Inference uses 10 deterministic reverse steps. We used 14,000 paired CT slices from the AAPM CT-MAR Grand Challenge, including 12,045 training, 633 validation, and 1,322 held-out test slices. One clinical benchmark case was assessed qualitatively. We compared RDBM-MAR with CycleGAN, Pix2Pix, Res-SRDiff, I2I-Mamba, and I2SB using PSNR, SSIM, MAE, and LPIPS. Statistical analysis used Friedman and paired Wilcoxon signed-rank tests with Holm correction. Results: RDBM-MAR achieved the highest PSNR (43.67 +/- 4.56 dB) and SSIM (0.9856 +/- 0.0138) and the lowest MAE (14.74 +/- 8.68 HU). All four metrics differed significantly among methods (p < 0.001). RDBM-MAR outperformed all five comparators in PSNR, SSIM, and MAE (adjusted p < 0.001); Res-SRDiff achieved the lowest LPIPS. Difference maps, HU profiles, and the clinical case showed reduced artifacts with preservation of surrounding anatomy. Conclusions: RDBM-MAR provides a direct restoration trajectory from artifact CT toward metal-free CT, achieving strong structural fidelity and HU accuracy using 10 reverse steps.

physics.med-ph↗

arXiv2026

AlphaMemo: Structured Search-Process Memory for Self-Evolving Alpha Mining Agents

LLM agents are promising for alpha mining via combining financial priors, symbolic reasoning, executable factor generation, and feedback-driven refinement. Yet, they face a combinatorial search space, noisy non-stationary feedback, redundant discoveries, and overfitting risks from naively reusing past successes. To address these challenges, we propose AlphaMemo, a self-evolving alpha mining agent with Structured Search-Process Memory. Rather than memorizing only final factors or full trajectories, AlphaMemo records reusable evidence about which edit motifs work or fail under specific parent-factor contexts. It extracts motifs from Abstract Syntax Tree (AST) differences, applies confidence-gated residual memory on top of a search-ledger prior, and uses asymmetric veto control to suppress high-confidence failure patterns. Experiments on CSI 500 and S&P 500 show improved out-of-sample performance and fixed-budget discovery efficiency, with ablations validating the roles of residual learning, confidence gating, AST-diff motifs, and veto memory. Code is at https://github.com/jarrettyu/sspm.

cs.AI↗

arXiv2026

GIFT: Gradient Informed Filtering for Distillation Augmented Reasoning RL

Training large language models for complex reasoning tasks relies on two complementary signals: sparse, outcome-grounded reinforcement learning (RL) for correctness, and dense, token-level distillation for guidance. However, complementarity in supervision does not guarantee compatibility in optimization; imitation-focused distillation can conflict with the outcome-driven RL objective, and existing hybrids lack a principled resolution. We propose \textbf{GIFT} (\textbf{G}radient-\textbf{I}nformed \textbf{F}il\textbf{T}ering for Distillation-Augmented Reasoning RL), a novel method that directly addresses the allocation problem of which teacher updates to apply. GIFT performs a token-level geometric analysis, decomposing the distillation gradient into a component \textbf{parallel} to the RL gradient, which measures first-order conflict and provides a \textbf{safety} filter, and a component \textbf{orthogonal} to it, which quantifies novel directional guidance and provides a measure of \textbf{value}. This decomposition yields a principled scoring rule to rank token-level guidance. We combine this with a progressive \textbf{coverage annealing} schedule that shrinks the budget of tokens receiving distillation, ensuring that the most conflicting updates are discarded first while the most complementary ones are retained at full strength. This facilitates a smooth transition from dense, geometrically-filtered guidance to a pure, outcome-grounded objective at convergence. Extensive experiments on mathematical and logical reasoning benchmarks show that GIFT consistently outperforms strong baselines across multiple model scales, achieving relative gains of up to 9.5\% on difficult mathematical reasoning tasks. Code: https://github.com/lilifree/GIFT

cs.LG↗

arXiv2026

Echo: Coupled Action and Task Status Signal Denoising for Vision Language Action Models

Recognizing task completion and failure enables robots to stop upon success and initiate recovery or replanning after failure. Existing methods augment Vision Language Action (VLA) policies with task status signal prediction to support these execution decisions. However, learning and integrating these signals can require fine-grained supervision, external monitoring models, or complex architectural designs. Moreover, the potential of task status signals to directly guide action generation remains underexplored. We present Echo, a framework that combines task status signal learning without frame-level progress annotations with a compact design for status prediction and action generation within a single VLA. Specifically, Echo represents task status with stop and failure signals, added as two dimensions of each action chunk and denoised together with robot actions. Their combination distinguishes ongoing execution, successful completion, and failure. To learn these signals without fine-grained progress annotations, Echo constructs successful, failed, and recovery trajectories with complementary supervision properties. To enable status signal-guided action generation, Echo controls interactions between the signals and actions and denoises the signals faster than the actions. These allow later action denoising steps to condition on cleaner status estimates while preserving accurate signal prediction. Extensive real-world manipulation experiments show that Echo accurately predicts task status and consistently improves policy performance.

cs.RO↗

arXiv2026

DriveMLP: When Training Strategy Outperforms Model Complexity in Autonomous Driving

Learning motion planning policies that remain reliable under closed-loop execution is a central challenge in Autonomous Driving (AD). Imitation Learning (IL) scales well to large AD datasets but suffers from distribution shift, causal confusion, and compounding errors under closed-loop execution. In contrast, Reinforcement Learning (RL) typically requires handcrafted rule-based rewards and massive simulation. We study a simple closed-loop IL framework that uses RL purely as an optimization tool, guided by a trajectory-distance reward derived from logged expert data. We refer to this training strategy as Expert-Guided RL (EGRL). Through a controlled empirical study we find evidence that the training procedure, rather than model size and complexity, accounts for a substantial part of the performance gains. We then introduce DriveMLP, a lightweight MLP policy trained with EGRL, as a simple instantiation of these findings. It achieves a score of 86.51 on the nuPlan Val14 Reactive benchmark, a +3.2 point improvement over the previous best IL planner (Flow Planner), with >15x faster inference. Code is available at: https://github.com/alessandro-canevaro/DriveMLP .

cs.LG↗

arXiv2026

Learning Where to Look: Anatomical Grounding and Guided Attention for Cardiac MRI Vision-Language Models

Cardiac magnetic resonance imaging (CMR) enables assessment of cardiac anatomy, ventricular function, and myocardial tissue characteristics. Clinicians interpret these images by identifying cardiac structures and focusing on the regions relevant to each clinical question, motivating anatomically guided vision-language models (VLMs). Yet CMR-specific supervision for anatomical localisation and clinical question answering remains limited. To address this gap, we investigate fine-grained CMR visual question answering through anatomical grounding and guided attention. We construct 128,915 anatomical-grounding and 42,799 clinical QA pairs across short-axis cine, late gadolinium enhancement, and long-axis cine. These datasets support anatomical recognition, localisation, and clinical assessment without requiring paired reports for individual training images. To help the model learn where to look, we introduce Cardiac Anatomy-Routed Attention (CARA), which selects predicted anatomical priors according to the question and guides decoder attention with learned task-specific strengths. Combining anatomical grounding pretraining with CARA yields our model, CARA-VL. Experiments demonstrate CARA-VL's strengths in clinical assessment and regional localisation across CMR imaging settings, with promising generalization to an external clinical cohort. Together, our data and method provide a practical framework for studying and advancing cardiac visual understanding in VLMs. We will release the QA data derived from public datasets upon publication.

cs.CV↗

arXiv2026

SonoWorld: Towards Interactive Pose-Controllable Ultrasound World Models

Robotic ultrasound can standardize image acquisition and reduce operator workload and variability, but policy learning remains limited by the lack of realistic, controllable simulation environments. While controllable world models have advanced rapidly in natural-image domains, ultrasound world modeling remains challenging because each frame captures only a cross-section of unobserved 3D anatomy, causing small probe motions to induce large appearance changes. Existing approaches rely on robot-tracked poses and latent-space prediction, but do not directly generate the observations required for interactive control. We propose SonoWorld, a causal ultrasound world model that learns video dynamics from unlabeled clinical videos and grounds them to 6-DoF control using a small set of freehand clinical scans tracked with an EM sensor. SonoWorld is trained in three stages: pretraining a latent-action-conditioned video diffusion model on around 6.9 million unlabeled multi-organ ultrasound frames; grounding the learned video dynamics to metric probe poses with a small EM-tracked thyroid dataset; and applying distribution matching distillation to enable causal, long-horizon autoregressive generation with selectively caching eviction tokens at inference time. We evaluate controllability through pose-following fidelity, round-trip consistency, and axis-specific control under synthetic actions on held-out tracked scans, and further validate it with a user study. In model-based action discrimination, SonoWorld correctly ranks the target-directed probe motion over an equal-magnitude opposite motion in up to 74.8% of trials. To our knowledge, SonoWorld is the first causal, pose-controllable ultrasound world model learned primarily from freehand clinical videos, providing a data-driven interactive environment for robotic ultrasound data acquisition and policy evaluation.

cs.CV↗

arXiv2026

A Simple and Unified Method for Gradient-Variation Regret

Gradient-variation online learning has attracted increasing attention for its connections to game theory and optimization. It has been studied in both non-stationary online learning, which aims to minimize dynamic regret against time-varying comparator sequences, and universal online learning, which seeks to adapt to unknown loss curvature. Prior work obtained gradient-variation bounds using different techniques tailored to each setting. We propose a simple and unified method based on surrogate losses with a quadratic penalty on decision movement, yielding gradient-variation bounds in both settings. For non-stationary online learning, the method induces an optimistic-to-gradient-variation reduction, enabling a broad class of algorithms to match the rate established by Zhao et al. (2024). For universal online learning, it yields a simple algorithm that matches the optimal universal regret bounds of Yan et al. (2024) while relaxing the extended smoothness assumption. Finally, we apply our method to online games and improve on existing guarantees.

cs.LG↗

arXiv2026

Higher-Order Magnus Corrections to Leakage Elimination Operator-Based Quantum Control Pulse Design in the Low-Frequency Regime

Effective quantum control is essential for quantum computing and information processing, yet level leakage remains a bottleneck limiting control fidelity. The leakage elimination operator (LEO) approach is highly effective at suppressing leakage from a target subspace to other subspaces. Analytical control conditions under the high-frequency driving limit have been derived via the Feshbach PQ partitioning technique. However, the driving intensity is subject to fundamental physical bounds, and low-frequency driving is experimentally more feasible. In this work, we reformulate LEO within the Magnus framework and extend it beyond the high-frequency limit. We show that the analytical control conditions obtained from the Feshbach PQ partitioning technique are equivalent to the zeroth-order Magnus expansion. We then analyze the higher-order Magnus corrections. By minimizing the leakage, we obtain a modified pulse condition suitable for the low-frequency regime. As an illustrative example, we study near-perfect state transfer through a one-dimensional spin chain under LEO control. We find that the second-order Magnus correction improves the transmission fidelity at low-frequency driving, while the improvement becomes negligible at high frequencies. Our results provide an effective route for designing control pulses in the low-frequency regime, with potential applications in superconducting qubits, trapped ions, and solid-state spin systems.

quant-ph↗

arXiv2026

Quantitative validation of SMBI deposition profiles on EAST using experimentally constrained simulations

Quantitative comparison between SMBI fueling simulations and experiments has long been hampered by the poor constraint on the effective source. Here, the particle increment per pulse is extracted experimentally and used to calibrate the simulation source. For EAST shot 71438, the density evolution from microwave reflectometry is analyzed for nine SMBI pulse groups. The SMBI-induced density increase is isolated from the background by comparing the rising and falling phases around each pulse group. Its radial profile peaks in the mid-pedestal region for every pulse group. Volume integration of this increment provides the experimental reference. The simulations use a multi-fluid model in the BOUT++ framework in which two transport operators are recast into conservative flux form, with the particle balance residual reduced to about 2% of the injected amount, and the perpendicular transport coefficients are constructed from the discrete radial flux balance, with the unphysical changes of the initial density and temperature profiles in source-free evolution reduced by an order of magnitude. For three cases sharing the same nominal molecular flux, the simulated deposition peak agrees with the experimental one in the relative pedestal coordinate. The simulated conversion of the injected particles at the end of the injection is slightly lower than the value inferred from the experiment, while the fraction retained at the end of the pulse is higher. The perturbation thus decays more slowly in the simulation, which we attribute to fast loss channels not represented in the model, such as parallel losses to the scrape-off layer. The edge electron temperature stays on the high-temperature plateau of the dissociation and ionization rate coefficients throughout the pulse, and the inward shift of the deposition with edge cooling reported earlier does not occur under the present conditions.

physics.plasm-ph↗

arXiv2026

C2GA: A Class-Controllable Generative Augmentation Framework for Respiratory Sound Classification

Background: Respiratory sound classification plays a critical role in the clinical identification of pulmonary pathologies. However, its performance is often hindered by the limited size, severe noise, and class imbalance of real-world auscultation datasets. Although conventional audio augmentation techniques are easy to implement, they may inadvertently distort subtle pathological characteristics. Meanwhile, existing Variational Autoencoder (VAE)- or Generative Adversarial Network (GAN)-based generative approaches often suffer from limited sample fidelity and insufficient controllability over class semantics, particularly under conditions of scarce supervision. Methods: To overcome these limitations, we propose C2GA, a class-controllable generative augmentation framework. C2GA first constructs a semantically rich discrete latent space using a conditional Vector-Quantized Variational Autoencoder (VQ-VAE), in which local acoustic tokens are explicitly decoupled from global class prototypes. Subsequently, a Transformer-based autoregressive prior is trained to generate label-consistent token sequences. These generated tokens are then fused with the corresponding class prototypes and decoded into high-fidelity Mel-spectrograms for data augmentation. Conclusion: These results indicate that C2GA provides an effective and semantically reliable augmentation strategy for respiratory sound analysis. By enabling controllable and high-quality data generation, the proposed framework offers a promising solution for improving the robustness and generalization of respiratory sound classification in realistic clinical scenarios.

cs.SD↗

arXiv2026

AdCoEvo: A Co-Evolving Multi-Agent Advertising Delivery System

Digital advertising is an important means for platforms to reach potential users and drive business growth. Advertising delivery is the core link between marketing objectives and actual returns. Existing methods have achieved partial automation in individual tasks such as bidding and budget allocation, but they still rely on human operators for overall coordination, making it difficult to support large-scale operations. They also often fail to achieve optimal performance, and individual experience remains difficult to turn into reusable operational capabilities. In addition, as advertising markets change, advertising delivery systems need timely strategy updates to remain effective in dynamic markets. To automate the entire advertising operations process and improve adaptability to market changes, we propose AdCoEvo, a co-evolving multi-agent advertising delivery system. Specifically, the system connects delivery decisions across different timescales and operational granularities through role specialization. Diagnostic results guide account planning and strategy adjustment in the next period. The multi-agent system updates its model parameters based on actual delivery outcomes and continually improves delivery strategies through mutual feedback among planning, adjustment, and diagnosis, thereby achieving co-evolution. Experiments show that, relative to the rule-based operational baseline (Rule), AdCoEvo increases advertiser value by 14.02% while reducing cost per acquisition by 7.07%. Compared with a multi-agent system with fixed parameters, AdCoEvo increases advertiser value by 3.96% and reduces cost per acquisition by 1.24%, indicating that co-evolution can yield greater delivery returns.

cs.AI↗

arXiv2026

LLM-OSDA: An Optimal-Stopping Dynamic Auction for Native Advertising in Multi-Turn LLM Conversations

LLM-native advertising embeds sponsored content directly into model-generated responses, shifting the unit of sale from a fixed slot to a moment within an evolving conversation. Existing LLM ad-auction mechanisms primarily operate within a single response, settling the winner but not the timing. The extension is nontrivial: with one native insertion opportunity per session, the stopping time depends on bids, coupling timing with allocation, so static truthfulness arguments no longer apply. We propose the LLM-based Optimal Stopping Dynamic Auction (LLM-OSDA), a dynamic cost-per-click auction that integrates Bellman optimal stopping, winner allocation, and envelope pricing. A bid-independent LLM layer estimates contextual click quality and seamlessly renders the winning ad, while bids enter only the committed auction mechanism. Under an exact Bellman oracle, the expected discounted-click allocation is monotone in each advertiser's bid, and the corresponding envelope payment makes truthful bidding weakly dominant in expectation. For practical deployment, a learned StopNet approximates the Bellman action values. We show that its decisions differ from the optimal policy only near the stopping boundary and bound the resulting incentive loss in terms of its approximation error. Experiments on a simulated conversational advertising corpus show that LLM-OSDA improves net revenue by 11 percent over the strongest fixed-timing baseline while maintaining comparable user retention. Code is at https://github.com/2025Fang2025/llm-osda.

cs.CL↗

arXiv2026

Nonlinear Perturbations of Relativistic Stars

We extend the general relativistic Lagrangian perturbation formalism of Friedman and Schutz [Astrophys. J. 200, 204 (1975)] beyond linear order, retaining full time dependence of the perturbations. We provide general formulae to relate the Lagrangian and Eulerian perturbations to any general perturbative order. As a proof of principle, we apply the framework to tidally perturbed relativistic stars in Regge-Wheeler gauge and reduce the second-order field and conservation equations to master equations with a nonlinear source. Projecting the source onto the star's eigenmodes, we obtain a nonlinear equation of motion for the mode amplitudes and the coupling coefficients, which describe the interactions among the eigenmodes and with the external tidal field. Approximating the tidal field and the coupling coefficients, we compute the static quadratic Love number from the quadrupolar fundamental mode alone and find that it reproduces the direct metric-matching value to a reasonable accuracy of $4\%$, up to a further correction arising from the Hamiltonian and momentum constraints of the second-order tidal field.

gr-qc↗

arXiv2026

MENTOR: Learning from Failure with On-Policy Pedagogical Intervention

Reinforcement Learning from Verifiable Rewards (RLVR) is a key driver for Large Reasoning Models, but it is vulnerable to "zero-reward collapse", a training failure where a sudden increase in task difficulty causes the learning signal to vanish. We argue that the conventional approach of discarding these failed trajectories is wasteful, as they contain valuable diagnostic information about the model's cognitive gaps, a bright side that can be leveraged for recovery. Existing solutions, however, often regress to static, "off-policy-style" interventions like pre-defined hint libraries or perform post-hoc refinement, failing to provide the adaptive, forward-looking guidance needed to escape the collapse. To address this, we propose a paradigm shift towards on-policy pedagogical intervention and introduce MENTOR, a multi-agent framework that acts as a live mentor. MENTOR diagnoses the root cause of failure in real-time and dynamically generates targeted, instructional prompts to guide the learning agent's next attempt. As a plug-and-play module, MENTOR integrates seamlessly into existing policy optimization pipelines like GRPO without modifying the training infrastructure. Extensive experiments on math and code generation show that MENTOR effectively mitigates zero-reward collapse, outperforming state-of-the-art baselines. Notably, it achieves massive average relative gains of up to 78.3% on Llama-3.2-3B-Instruct over its base model.

cs.AI↗