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In-the-Flow Agentic System Optimization for Effective Planning and Tool Use research paper by Stanford University, 2025

Stanford University · Oct 7, 2025 · Agents and evaluation · 58 citations · 113 upvotes · unverified

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What it shows

AgentFlow, a trainable agentic framework with in-the-flow optimization, enhances reasoning in large language models by coordinating specialized modules and outperforms top baselines across various tasks.

UnverifiedHugging Face's summary; not yet checked by hand.

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About this paper
Authors
Zhuofeng Li, Haoxiang Zhang, Seungju Han and 6 more
arXiv
2510.05592 · PDF
Venue
arXiv.org
Citations
58, 5 influential · Semantic Scholar
Upvotes
113 · Hugging Face
Code
github.com/lupantech/AgentFlow
Lab
Stanford University

Changes

What changed
Influential citationsfirst count: 5Sep 25, 2026
Citationsfirst count: 58Sep 25, 2026
New paperFound by the weekly scan, unverifiedSep 25, 2026

Sources: each lab's own papers and arXiv, with citation and upvote counts from Semantic Scholar and Hugging Face. One-line summaries are for orientation, not a substitute for the paper. Logos via logo.dev; trademarks belong to their owners.

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