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Agents
An annotated collection of 2 papers on agents, spanning 2025 to 2026. Featuring works by Yubin Kim, Dat Tran & Douwe Kiela — each with editorial commentary oriented to digital product practice.
Towards a Science of Scaling Agent Systems
Yubin Kim, Ken Gu, Chanwoo Park, Chunjong Park, Samuel Schmidgall, A. Ali Heydari, Yao Yan, Zhihan Zhang, Yuchen Zhuang, Yun Liu, Mark Malhotra, Paul Pu Liang, Hae Won Park, Yuzhe Yang, Xuhai Xu, Yilun Du, Shwetak Patel, Tim Althoff, Daniel McDuff & Xin Liu, 2025 · arXiv preprint (2512.08296)
The first quantitative scaling principles for multi-agent AI systems, derived from 260 configurations across six benchmarks. Three findings matter: independent agent swarms can amplify baseline errors up to 17 times; too…
Single-Agent LLMs Outperform Multi-Agent Systems on Multi-Hop Reasoning Under Equal Thinking Token Budgets
Dat Tran & Douwe Kiela, 2026 · arXiv preprint (2604.02460)
The paper that forced the multi-agent debate to control for what it should have controlled from the start: computational budget. Tran and Kiela gave single and multi-agent LLM systems identical reasoning token budgets an…