arXiv subjects

Jiajie Yang

Publications and source records attributed to Jiajie Yang.

5 recordsLinked to original sources

arXiv2026

Line-of-sight magnetic-field propagation effects on axion-like particle constraints from GRB 221009A

High-energy photons from GRB 221009A provide a powerful opportunity to probe axion-like particles (ALPs) through photon-ALP oscillations in cosmic magnetic fields. We revisit the ALP constraints implied by the LHAASO observation of this burst, with particular emphasis on the magnetic-field environments encountered along the line of sight. We include the host-galaxy, intergalactic, and Milky-Way magnetic fields and assess their respective impacts on the photon survival probability and on the exclusion limits in the ALP mass-coupling plane. We show that the constraints are only mildly affected by the choice of host-galaxy and Galactic magnetic-field models, but can change significantly once the intergalactic magnetic field is varied. Its field strength, coherence scale, and stochastic properties can all leave visible imprints on the derived exclusion contours, and in some cases generate pronounced oscillatory features. This demonstrates that the intergalactic magnetic field constitutes the dominant astrophysical uncertainty in extracting ALP limits from GRB 221009A. Our analysis highlights the importance of realistic propagation modeling in future gamma-ray searches for ALPs.

hep-ph↗

arXiv2026

When Seekers Are Hard to Help: Evaluating Emotional Support Dialogue Systems in Worst-Case Interactions

Emotional Support Dialogue Systems (ESDSes) are increasingly evaluated and trained with LLM-simulated seekers. However, such simulated seekers often behave as cooperative, average-case users who disclose clearly, respond constructively, and accept support within a few turns. This can lead to overly optimistic evaluation and obscure whether ESDSes can handle difficult help-seeking interactions. In this work, we study ESDS evaluation under worst-case interactions, where seekers are hard to help due to low engagement, resistance, limited self-disclosure, emotional volatility, or rigid negative interpretations. We first conduct an expert simulation study with eight experienced counselling professionals, who simulate difficult seekers, interact with existing Chinese ESDSes, provide scale ratings, and participate in semi-structured interviews. Based on this study, we derive worst-case seeker behaviours and identify key limitations of current systems. We then propose a worst-case evaluation framework consisting of an LLM-based worst-case seeker simulator and four worst-case-oriented metrics: Deep Emotional Understanding, Guided Exploration, Balanced Emotional Support, and Authentic and Grounded Support. Evaluating 17 systems, we find that nearly all models suffer substantial performance drops under worst-case interactions. Large general-purpose LLMs are generally more robust than specialised ESDSes, but even the strongest models struggle to sustain engagement and improve seekers' emotional states. Finally, we show that worst-case simulation can also generate useful training data, improving the robustness of smaller models.

cs.CL↗

arXiv2025

Latent Prototype Routing: Achieving Near-Perfect Load Balancing in Mixture-of-Experts

Mixture-of-Experts (MoE) architectures have emerged as a key strategy for scaling large language models (LLMs) efficiently. However, current MoE systems suffer from severe load imbalance, where only a small subset of experts is consistently activated during training and inference, leading to significant underutilization of model capacity and computational resources. In this work, we revisit expert routing through a clustering perspective and propose Latent Prototype Routing (LPR), a novel routing framework that generalizes existing approaches while promoting balanced expert utilization without compromising downstream performance. Extensive experiments across multiple open-source MoE models -- including DeepSeek-V3, Qwen3-MoE, and Mixtral -- demonstrate that LPR reduces the Gini coefficient of expert load from 0.70 to 0.035 on average, improves the min-max expert load ratio from 1e-6 to 0.70, achieving near-perfect load balancing.

cs.LG↗

arXiv2024

Triplet: Triangle Patchlet for Mesh-Based Inverse Rendering and Scene Parameters Approximation

Recent advancements in Radiance Fields have significantly improved novel-view synthesis. However, in many real-world applications, the more advanced challenge lies in inverse rendering, which seeks to derive the physical properties of a scene, including light, geometry, textures, and materials. Meshes, as a traditional representation adopted by many simulation pipeline, however, still show limited influence in radiance field for inverse rendering. This paper introduces a novel framework called Triangle Patchlet (abbr. Triplet), a mesh-based representation, to comprehensively approximate these scene parameters. We begin by assembling Triplets with either randomly generated points or sparse points obtained from camera calibration where all faces are treated as an independent element. Next, we simulate the physical interaction of light and optimize the scene parameters using traditional graphics rendering techniques like rasterization and ray tracing, accompanying with density control and propagation. An iterative mesh extracting process is also suggested, where we continue to optimize on geometry and materials with graph-based operation. We also introduce several regulation terms to enable better generalization of materials property. Our framework could precisely estimate the light, materials and geometry with mesh without prior of light, materials and geometry in a unified framework. Experiments demonstrate that our approach can achieve state-of-the-art visual quality while reconstructing high-quality geometry and accurate material properties.

cs.GR↗

arXiv2024

Ultra-High-Resolution Image Synthesis with Pyramid Diffusion Model

We introduce the Pyramid Diffusion Model (PDM), a novel architecture designed for ultra-high-resolution image synthesis. PDM utilizes a pyramid latent representation, providing a broader design space that enables more flexible, structured, and efficient perceptual compression which enable AutoEncoder and Network of Diffusion to equip branches and deeper layers. To enhance PDM's capabilities for generative tasks, we propose the integration of Spatial-Channel Attention and Res-Skip Connection, along with the utilization of Spectral Norm and Decreasing Dropout Strategy for the Diffusion Network and AutoEncoder. In summary, PDM achieves the synthesis of images with a 2K resolution for the first time, demonstrated on two new datasets comprising images of sizes 2048x2048 pixels and 2048x1024 pixels respectively. We believe that this work offers an alternative approach to designing scalable image generative models, while also providing incremental reinforcement for existing frameworks.

cs.CV↗