arXiv subjects

Ziyao Tang

Publications and source records attributed to Ziyao Tang.

7 recordsLinked to original sources

arXiv2026

Damping dynamics of the centroid oscillation of a relativistic laser pulse in a plasma channel

The centroid oscillation of an offset laser pulse propagating in a preformed plasma channel is investigated through theoretical analysis and three-dimensional particle-in-cell simulations. For non-relativistic laser pulses, the mode leakage of a finite channel and the temporal walk-off between the fundamental and high order modes of a finite-duration laser induce a decay in the laser centroid oscillation. An analytical model characterizing these decay mechanisms is derived and validated by simulations. For relativistic laser pulses, the slice-based centroid oscillation frequency develops an axial chirp due to relativistic channel modification and photon deceleration. This chirp leads to phase mixing across different axial slices of the pulse, resulting in a rapid damping of the overall centroid oscillation. Understanding this oscillation damping is crucial for mitigating electron beam pointing jitter and maintaining beam quality in high-energy, channel-guided laser wakefield accelerators.

physics.acc-ph↗

arXiv2026

Spatiotemporal Sycophancy: Negation-Based Gaslighting in Video Large Language Models

Video Large Language Models (Vid-LLMs) have demonstrated remarkable performance in video understanding tasks, yet their robustness under conversational interaction remains largely underexplored. In this paper, we identify spatiotemporal sycophancy, a failure mode in which Vid-LLMs retract initially correct, visually grounded judgments and conform to misleading user feedback under negation-based gaslighting. Rather than merely changing their answers, the models often fabricate unsupported temporal or spatial explanations to justify incorrect revisions. To systematically investigate this phenomenon, we propose a negation-based gaslighting evaluation framework and introduce GasVideo-1000, a curated benchmark designed to probe spatiotemporal sycophancy with clear visual grounding and temporal reasoning requirements. We evaluate a broad range of state-of-the-art open-source and proprietary Vid-LLMs across diverse video understanding tasks. Extensive experiments reveal that vulnerability to negation-based gaslighting is pervasive and severe, even among models with strong baseline performance. While prompt-level grounding constraints can partially mitigate this behavior, they do not reliably prevent hallucinated justifications or belief reversal. Our results indicate that current Vid-LLMs lack robust mechanisms for maintaining grounded spatiotemporal beliefs under adversarial conversational feedback.

cs.CV↗

arXiv2026

Effects of realistic laser intensity and phase distribution on high-charge laser wakefield acceleration

Laser wakefield acceleration (LWFA) can produce relativistic electron beams and various secondary particles in centimeter-long plasmas, making it a valuable particle source with important applications in many disciplines. In this work, we examine the effects of non-ideal transverse intensity and phase distribution of laser pulses on LWFA through both experimental measurements and particle-in-cell simulations. The complex transverse profile of the 75 TW laser pulses reduces the self-focused intensity in plasma compared with a transversely Gaussian laser. Furthermore, the sheath structure of the nonlinear plasma wake excited by realistic laser pulses is wider and more complicated than that of a Gaussian laser. These hinder the injection of the plasma electrons. As the laser pulse propagates through the plasma, its intensity profile gradually becomes elliptical and drives a plasma wake with a sharp sheath near the azimuths of the major axis, leading to an injection. When using a realistic laser profile in simulations, both the charge and energy of injected electrons closely match experimental results ($\sim200$ pC of charge and $\sim 200$ MeV peak energy), whereas the Gaussian laser simulations produce much higher charge ($\sim500$ pC). Our findings reveal the difference in injection dynamics between LWFA driven by non-ideal laser pulses and those driven by Gaussian pulses, and are useful for applications of LWFA which demand high-charge electron beams.

physics.plasm-ph↗

arXiv2026

Predicting Future Utility: Global Combinatorial Optimization for Task-Agnostic KV Cache Eviction

Given the quadratic complexity of attention, KV cache eviction is vital to accelerate model inference. Current KV cache eviction methods typically rely on instantaneous heuristic metrics, implicitly assuming that score magnitudes are consistent proxies for importance across all heads. However, this overlooks the heterogeneity in predictive fidelity across attention heads. While certain heads prioritize the instantaneous contribution of tokens, others are dedicated to capturing long-horizon utility. In this paper, we propose that optimal budget allocation should be governed by the marginal utility in preserving long-term semantic information. Building on this insight, we propose LU-KV, a novel framework that formulates head-level budget allocation as a global combinatorial optimization problem to maximize the long-horizon marginal contribution of reserved tokens. To solve this non-convex problem, we employ a convex-hull relaxation and a marginal-utility-based greedy solver, achieving near-optimal solutions. Furthermore, we implement a data-driven offline profiling protocol to facilitate the practical deployment of LU-KV. Evaluations on LongBench and RULER benchmarks demonstrate that LU-KV reduces KV cache size by 80% with minimal performance degradation, while also decreasing inference latency and GPU memory footprint.

cs.LG↗

arXiv2025

TPG-INR: Target Prior-Guided Implicit 3D CT Reconstruction for Enhanced Sparse-view Imaging

X-ray imaging, based on penetration, enables detailed visualization of internal structures. Building on this capability, existing implicit 3D reconstruction methods have adapted the NeRF model and its variants for internal CT reconstruction. However, these approaches often neglect the significance of objects' anatomical priors for implicit learning, limiting both reconstruction precision and learning efficiency, particularly in ultra-sparse view scenarios. To address these challenges, we propose a novel 3D CT reconstruction framework that employs a 'target prior' derived from the object's projection data to enhance implicit learning. Our approach integrates positional and structural encoding to facilitate voxel-wise implicit reconstruction, utilizing the target prior to guide voxel sampling and enrich structural encoding. This dual strategy significantly boosts both learning efficiency and reconstruction quality. Additionally, we introduce a CUDA-based algorithm for rapid estimation of high-quality 3D target priors from sparse-view projections. Experiments utilizing projection data from a complex abdominal dataset demonstrate that the proposed model substantially enhances learning efficiency, outperforming the current leading model, NAF, by a factor of ten. In terms of reconstruction quality, it also exceeds the most accurate model, NeRP, achieving PSNR improvements of 3.57 dB, 5.42 dB, and 5.70 dB with 10, 20, and 30 projections, respectively. The code is available at https://github.com/qlcao171/TPG-INR.

cs.CV↗

arXiv2019

Implicit Label Augmentation on Partially Annotated Clips via Temporally-Adaptive Features Learning

Partially annotated clips contain rich temporal contexts that can complement the sparse key frame annotations in providing supervision for model training. We present a novel paradigm called Temporally-Adaptive Features (TAF) learning that can utilize such data to learn better single frame models. By imposing distinct temporal change rate constraints on different factors in the model, TAF enables learning from unlabeled frames using context to enhance model accuracy. TAF generalizes "slow feature" learning and we present much stronger empirical evidence than prior works, showing convincing gains for the challenging semantic segmentation task over a variety of architecture designs and on two popular datasets. TAF can be interpreted as an implicit label augmentation method but is a more principled formulation compared to existing explicit augmentation techniques. Our work thus connects two promising methods that utilize partially annotated clips for single frame model training and can inspire future explorations in this direction.

cs.CV↗

arXiv2018

Efficient Video Understanding via Layered Multi Frame-Rate Analysis

One of the greatest challenges in the design of a real-time perception system for autonomous driving vehicles and drones is the conflicting requirement of safety (high prediction accuracy) and efficiency. Traditional approaches use a single frame rate for the entire system. Motivated by the observation that the lack of robustness against environmental factors is the major weakness of compact ConvNet architectures, we propose a dual frame-rate system that brings in the best of both worlds: A modulator stream that executes an expensive models robust to environmental factors at a low frame rate to extract slowly changing features describing the environment, and a prediction stream that executes a light-weight model at real-time to extract transient signals that describes particularities of the current frame. The advantage of our design is validated by our extensive empirical study, showing that our solution leads to consistent improvements using a variety of backbone architecture choice and input resolutions. These findings suggest multiple frame-rate systems as a promising direction in designing efficient perception for autonomous agents.

cs.CV↗