Chaobin Yang | Searcharxiv

arXiv2026

Graph Engineering in the Era of LLM Agents: From Individual Intelligence to System Intelligence

LLMs have evolved from language generators to autonomous agents capable of complex, long-horizon tasks. This evolution has produced paradigms including Prompt Engineering to elicit model capabilities, Context Engineering to manage information access, Harness Engineering to organize external tools and resources, and Loop Engineering to support continual reflection and self-improvement. Yet as tasks grow more complex, individual intelligence faces a fundamental limit: many tasks require heterogeneous expertise, interdependent subtasks, parallel execution, independent verification, and persistent state, exceeding any single agent's organizational capacity. Augmenting one agent's capabilities or context cannot resolve this architectural mismatch; intelligence must instead be distributed across specialized agents and organized at the system level. We call this System Intelligence: an agent system's ability to organize and coordinate multiple intelligent components into a coherent, adaptive whole pursuing a shared objective. Achieving it requires more than adding agents; it demands explicit structures to organize work, coordinate heterogeneous agents, and maintain evolving execution states. We introduce Graph Engineering, an emerging paradigm for next-generation agent systems. Unlike prior paradigms that mainly optimize individual interactions or agent-level behavior, Graph Engineering constructs explicit, dynamic, evolving graph structures representing tasks, agents, and system states. These abstractions provide a unified foundation for organizing complex objectives, orchestrating heterogeneous agents, modeling system dynamics, and enabling scalable agent evolution. We systematically review the principles, methodologies, and applications of Graph Engineering for LLM agents. Related papers, open-source data, and projects are collected at https://github.com/DEEP-JLU/Awesome-Graph-Engineering.

cs.IR↗

arXiv2026

Machine Learning to Foundation Models: Artificial Intelligence for Nanophotonic Modeling and Scientific Discovery

Artificial intelligence (AI) is increasingly used to model, design, and study nanophotonic systems. This review traces the development of the field from classical machine learning and deep learning to generative models, transfer learning, transformers, and emerging foundation models. It first introduces major nanophotonic platforms, including nanoparticles, nanoholes, metasurfaces, photonic crystals, multilayer thin films, and integrated photonic devices, together with their main forward and inverse problems. It then reviews data-driven methods for predicting optical spectra and fields, generating structures from target responses, improving designs through optimization, and accounting for fabrication constraints. Generative models are discussed as a way to produce multiple valid solutions to nonunique inverse problems, while transfer learning, few-shot learning, and physics-aware training help reduce data requirements and improve generalization. Recent domain-specific foundation models show that different optical structures and responses can be handled within shared representations, but current systems remain limited in scope and physical grounding. Future progress will depend on multimodal models that connect geometry, materials, spectra, electromagnetic (EM) fields, fabrication data, experiments, and scientific literature with reliable simulation and validation tools. Current foundation models remain domain-specific, and their extension to broader nanophotonic tasks will require stronger physical grounding and validation.

physics.optics↗