Xinghong Li | Searcharxiv

arXiv2026

Dispersion-managed octave soliton microcombs in heterostructured microresonators

Controlling group velocity dispersion is of fundamental importance in ultrafast optics, particularly for supercontinuum generation and optical frequency comb synthesis. However, the simultaneous, independent tailoring of multiple dispersion coefficients over an ultra-broad bandwidth remains a formidable challenge in conventional nanophotonic platforms. Here, we demonstrate a robust strategy for broadband dispersion management using heterostructured microresonators comprised of adiabatically concatenated waveguides with distinct geometries. By meticulously engineering the local dispersion profiles, we can flexibly synthesize the global effective dispersion coefficients of different orders, effectively expanding design degrees of freedom beyond conventional limits. As a benchmarking demonstration, we fabricate heterostructured silicon nitride microresonators using a commercial foundry process and successfully generate octave-spanning soliton microcombs with a repetition rate as low as 118 GHz. These microcombs feature deterministically tunable dispersive waves operating across the 290-310 THz range. Such octave-spanning microcombs with detectable repetition rates and carrier-envelope offset frequencies are readily applicable to f-2f self-referencing in optical clocks and frequency synthesizers. The proposed heterostructured architecture establishes a versatile paradigm for generating ultrawideband soliton microcombs with tailorable spectral profiles.

physics.optics↗

arXiv2025

Psychology-driven LLM Agents for Explainable Panic Prediction on Social Media during Sudden Disaster Events

During sudden disaster events, accurately predicting public panic sentiment on social media is crucial for proactive governance and crisis management. Current efforts on this problem face three main challenges: lack of finely annotated data hinders emotion prediction studies, unmodeled risk perception causes prediction inaccuracies, and insufficient interpretability of panic formation mechanisms. We address these issues by proposing a Psychology-driven generative Agent framework (PsychoAgent) for explainable panic prediction based on emotion arousal theory. Specifically, we first construct a fine-grained open panic emotion dataset (namely COPE) via human-large language models (LLMs) collaboration to mitigate semantic bias. Then, we develop a framework integrating cross-domain heterogeneous data grounded in psychological mechanisms to model risk perception and cognitive differences in emotion generation. To enhance interpretability, we design an LLM-based role-playing agent that simulates individual psychological chains through dedicatedly designed prompts. Experimental results on our annotated dataset show that PsychoAgent improves panic emotion prediction performance by 12.6% to 21.7% compared to baseline models. Furthermore, the explainability and generalization of our approach is validated. Crucially, this represents a paradigm shift from opaque "data-driven fitting" to transparent "role-based simulation with mechanistic interpretation" for panic emotion prediction during emergencies. Our implementation is publicly available at: https://anonymous.4open.science/r/PsychoAgent-19DD.

cs.AI↗