Laifu Man | Searcharxiv

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↗

arXiv2025

PHYBench: Holistic Evaluation of Physical Perception and Reasoning in Large Language Models

Current benchmarks for evaluating the reasoning capabilities of Large Language Models (LLMs) face significant limitations: task oversimplification, data contamination, and flawed evaluation items. These deficiencies necessitate more rigorous assessment methods. To address these limitations, we introduce PHYBench, a benchmark of 500 original physics problems ranging from high school to Physics Olympiad difficulty. PHYBench addresses data contamination through original content and employs a systematic curation pipeline to eliminate flawed items. Evaluations show that PHYBench activates more tokens and provides stronger differentiation between reasoning models compared to other baselines like AIME 2024, OlympiadBench and GPQA. Even the best-performing model, Gemini 2.5 Pro, achieves only 36.9% accuracy compared to human experts' 61.9%. To further enhance evaluation precision, we introduce the Expression Edit Distance (EED) Score for mathematical expression assessment, which improves sample efficiency by 204% over binary scoring. Moreover, PHYBench effectively elicits multi-step and multi-condition reasoning, providing a platform for examining models' reasoning robustness, preferences, and deficiencies. The benchmark results and dataset are publicly available at https://www.phybench.cn/.

cs.CL↗