物理与社会：2025–2026 年度综述

An annual survey of the physics.soc-ph arXiv category, covering research published from September 2025 – August 2026. Built on a close-reading set of 120 papers and organised into 44 chapters, it maps the year by the field's research sub-directions.

Book framework (share of the year's output):

• Urban Scaling and Spatial Mobility Modeling (18.5%) — Focuses on modeling urban street networks, mobility patterns, and scaling laws using large-scale spatial data to understand city structure and dynamics.
• Complex Network Topology and Robustness (14.9%) — Investigates the structural properties, percolation thresholds, and resilience of complex networks under various degree distributions and modular structures.
• Higher-Order Interactions and Hypergraph Dynamics (13.1%) — Explores collective dynamics, contagion processes, and synchronization phenomena on higher-order networks such as hypergraphs and simplicial complexes.
• Opinion Dynamics and Social Contagion Models (10.2%) — Analyzes the spread of opinions, prejudice, and social behaviors through stochastic models, voter models, and interaction-driven phase transitions.
• Agent-Based Modeling and Multi-Agent Systems (9.2%) — Utilizes agent-based frameworks and multi-agent simulations to study social systems, economic behaviors, and the integration of LLM agents in research.
• Network Reconstruction and Epidemic Control (9.0%) — Develops methods for reconstructing network structures from data and applying them to epidemic modeling and control strategies on evolving networks.
• Collective Behavior and Social Phase Transitions (8.1%) — Examines emergent collective phenomena, including consensus formation, dissent, and phase transitions in social systems driven by interaction rules.
• AI and Large Language Models in Science (6.3%) — Assesses the impact of AI and Large Language Models on scientific production, research funding, and human-AI collaboration at scale.
• Physics-Informed AI and Collective Intelligence (6.1%) — Integrates physics principles with AI agents to model collective behavior, traffic phases, and scientific discovery processes.
• Socio-Economic Dynamics and Inequality (4.6%) — Studies wealth distribution, inequality measures, and economic systems using kinetic exchange models and Fokker-Planck frameworks.

Each sub-direction is covered across three chapters — how it framed its questions, the strength of its key findings, and its open debates and frontiers — so the reader gets a traceable picture of what this part of the field actually produced over the year.