agent/papers/items/2026-2606-31314-a-novel-method-for-differential-algebraic-dynamic-model-discovery-in-power-syste.md

Paper: A Novel Method for Differential-Algebraic Dynamic Model Discovery in Power Systems: An LLM-Based Multi-Agent Collaborative Framework

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type: paper title: "A Novel Method for Differential-Algebraic Dynamic Model Discovery in Power Systems: An LLM-Based Multi-Agent Collaborative Framework" authors: Xinming Wang, Fan Tang, Yingli Wei, Yakun He, Zhe Liu, Ping Jiang, Haoyu Wu, Zihan Guo, et al. year: 2026 venue: arXiv url: https://arxiv.org/abs/2606.31314 code_url: source: arxiv collected_at: 2026-07-08 published_at: 2026-06-30 updated_at: 2026-06-30 status: queued relevance: high topics:

• agent-evaluation
• computer-use
• multi-agent methods:

benchmarks:

models:

datasets:

• eess.SY related_concepts:

related_jobs:

related_experiments:

related_projects:

collection_score: 14 collection_queries: multi-agent-llm

One-line Takeaway

Auto-collected from arXiv because it matched the Agent collection queries. Needs human skim.

Why Collected

• matched queries: multi-agent-llm
• inferred topics: agent-evaluation, computer-use, multi-agent
• arXiv categories: eess.SY
• collection score: 14

Review Checklist

• Does this paper directly inform Agent architecture, evaluation, memory, tools, safety, coding agents, GUI/browser agents, or multi-agent workflows?
• Does it include a benchmark, dataset, code, or reproducible experimental setup?
• Should it be promoted from queued to skimmed or summarized?

Links

• arXiv: https://arxiv.org/abs/2606.31314