arXiv subjects

Binghang Li

Publications and source records attributed to Binghang Li.

4 recordsLinked to original sources

arXiv2026

Understanding mechanisms underlying solar cycle predictability with a general framework

The large-scale magnetic field observed at the solar surface is produced by the interior dynamo process. Whether this surface field also provides the dominant seed for the subsequent dynamo cycle, however, remains controversial, with important consequences for the predictive skill of solar dynamo models.We investigate the physical conditions under which this predictive skill of the surface field arises in dynamo models within a general framework.By applying Stokes' theorem to the magnetic induction equation, we establish a direct physical link between the surface magnetic field and the subsequent dynamo process. The dominance of the surface induction integral in the net toroidal flux generation rate provides a quantitative criterion for assessing dynamo predictability, which we apply to five representative dynamo models.This general framework shows that the surface magnetic field acquires predictive power when the surface poloidal field is efficiently coupled back into the dynamo loop through flux-transport processes (e.g., meridional circulation), a condition that can be satisfied in both Babcock-Leighton (BL)-type and $\alpha-\Omega$ mean-field dynamo models. The framework further identifies a new condition under which the surface magnetic field acquires predictive power: namely, that it represents the radial component of the interior poloidal field, as in the original BL-type dynamo scenario. In addition, the non-zero net toroidal flux across different dynamo models supports its use as a proxy linking the interior toroidal field to surface flux emergence.

astro-ph.SR↗

arXiv2026

PsychePass: Calibrating LLM Therapeutic Competence via Trajectory-Anchored Tournaments

While large language models show promise in mental healthcare, evaluating their therapeutic competence remains challenging due to the unstructured and longitudinal nature of counseling. We argue that current evaluation paradigms suffer from an unanchored defect, leading to two forms of instability: process drift, where unsteered client simulation wanders away from specific counseling goals, and standard drift, where static pointwise scoring lacks the stability for reliable judgment. To address this, we introduce Ps, a unified framework that calibrates the therapeutic competence of LLMs via trajectory-anchored tournaments. We first anchor the interaction trajectory in simulation, where clients precisely control the fluid consultation process to probe multifaceted capabilities. We then anchor the battle trajectory in judgments through an efficient Swiss-system tournament, utilizing dynamic pairwise battles to yield robust Elo ratings. Beyond ranking, we demonstrate that tournament trajectories can be transformed into credible reward signals, enabling on-policy reinforcement learning to enhance LLMs' performance. Extensive experiments validate the effectiveness of PsychePass and its strong consistency with human expert judgments.

cs.CL↗

arXiv2025

Constraining the outer boundary condition for the Babcock-Leighton dynamo models

The evolution of the Sun's large-scale surface magnetic field is well captured by surface flux transport models, which can therefore provide a natural constraint on the outer boundary condition (BC) of Babcock-Leighton (BL) dynamo models. For the first time, we propose a zero radial diffusion BC for BL dynamo models, enabling their surface field evolution to align consistently with surface flux transport simulations. We derive a zero radial diffusion BC from the Magnetohydrodynamic induction equation and evaluate its effects in comparison with two alternatives: (i) a radial outer BC, and (ii) a radial outer BC combined with strong near-surface radial pumping. The comparison is carried out both for the evolution of a single bipolar magnetic region and within a full BL dynamo model. The zero radial diffusion outer BC effectively suppresses radial diffusion across the surface, ensuring consistency between the evolution of the bipolar magnetic region in the BL dynamo and the surface flux transport model. With this outer BC, the full BL dynamo model successfully reproduces the fundamental properties of the solar cycle. In addition, the model naturally produces a surface magnetic field that is not purely radial, in closer agreement with solar observations. The physically motivated zero radial diffusion boundary condition paves the way for deeper insight into the solar and stellar cycles.

astro-ph.SR↗

arXiv2025

{\Psi}-Arena: Interactive Assessment and Optimization of LLM-based Psychological Counselors with Tripartite Feedback

Large language models (LLMs) have shown promise in providing scalable mental health support, while evaluating their counseling capability remains crucial to ensure both efficacy and safety. Existing evaluations are limited by the static assessment that focuses on knowledge tests, the single perspective that centers on user experience, and the open-loop framework that lacks actionable feedback. To address these issues, we propose {\Psi}-Arena, an interactive framework for comprehensive assessment and optimization of LLM-based counselors, featuring three key characteristics: (1) Realistic arena interactions that simulate real-world counseling through multi-stage dialogues with psychologically profiled NPC clients, (2) Tripartite evaluation that integrates assessments from the client, counselor, and supervisor perspectives, and (3) Closed-loop optimization that iteratively improves LLM counselors using diagnostic feedback. Experiments across eight state-of-the-art LLMs show significant performance variations in different real-world scenarios and evaluation perspectives. Moreover, reflection-based optimization results in up to a 141% improvement in counseling performance. We hope PsychoArena provides a foundational resource for advancing reliable and human-aligned LLM applications in mental healthcare.

cs.CL↗