Can LLMs truly master long-horizon reasoning without crumbling...
机器之心 JIQIZHIXIN@jiqizhixin
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Oct 30, 2025
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Can LLMs truly master long-horizon reasoning without crumbling under complexity?
This study says yes with AgentFlow.
It's a trainable agentic framework that decomposes tasks across planner, executor, verifier & generator modules, optimized in-the-flow via Flow-GRPO.
A 7B model beats GPT-4o by up to 14.9% across search, math & science benchmarks.
In-the-Flow Agentic System Optimization for Effective Planning and Tool Use
Stanford, Texas A&M, UC San Diego, Lambda
Project: agentflow.stanford.edu
Paper: huggingface.co/papers/2510.05…
Code: github.com/lupantech/Agen…
Model: huggingface.co/AgentFlow
Demo: huggingface.co/spaces/AgentFl…
Our report: mp.weixin.qq.com/s/30cvMoQADYj1…
📬 #PapersAccepted by Jiqizhixin
This study says yes with AgentFlow.
It's a trainable agentic framework that decomposes tasks across planner, executor, verifier & generator modules, optimized in-the-flow via Flow-GRPO.
A 7B model beats GPT-4o by up to 14.9% across search, math & science benchmarks.
In-the-Flow Agentic System Optimization for Effective Planning and Tool Use
Stanford, Texas A&M, UC San Diego, Lambda
Project: agentflow.stanford.edu
Paper: huggingface.co/papers/2510.05…
Code: github.com/lupantech/Agen…
Model: huggingface.co/AgentFlow
Demo: huggingface.co/spaces/AgentFl…
Our report: mp.weixin.qq.com/s/30cvMoQADYj1…
📬 #PapersAccepted by Jiqizhixin