It Takes Workflows to Evolve Better Workflows

arXiv:2610.01026 · cs.CL, cs.AI · Submitted 2026-10-01 · Read on arXiv

cs.CL, cs.AI

Submitted: 2026-10-01

Updated: 2026-10-01

Project page: https://xhguo7.github.io/FloWright

Terminology

• Accuracy — 29× in this paper · explained in 1 episode
• Benchmark — 15× in this paper · explained in 2 episodes
• Distillation — 15× in this paper · explained in 2 episodes
• Multi-Agent System — 14× in this paper · explained in 6 episodes
• Reinforcement Learning — 13× in this paper · explained in 4 episodes
• GRPO — 11× in this paper · explained in 6 episodes
• LLM judge — 11× in this paper · explained in 2 episodes
• Trace — 5× in this paper · explained in 2 episodes
• Ablation — 3× in this paper · explained in 1 episode
• Credit assignment — 3× in this paper · explained in 3 episodes
• Multi-Agent Framework — 3× in this paper · explained in 3 episodes
• Adaptation — 2× in this paper · explained in 1 episode
• Agentic System — 2× in this paper · explained in 2 episodes
• Aggregation — 2× in this paper · explained in 1 episode
• Fine-Tuning — 2× in this paper · explained in 5 episodes
• Steering — 2× in this paper · explained in 2 episodes
• Ablation study — 1× in this paper · explained in 1 episode
• Adaptability — 1× in this paper · explained in 1 episode
• Adaptive Strategy — 1× in this paper · explained in 1 episode
• Agentic Framework — 1× in this paper · explained in 6 episodes
• Agentic Reasoning — 1× in this paper · explained in 1 episode
• Agentic Task — 1× in this paper · explained in 1 episode
• Agentic Tasks — 1× in this paper · explained in 1 episode
• Benchmarking — 1× in this paper · explained in 3 episodes
• Core — 1× in this paper · explained in 2 episodes
• Generalizability — 1× in this paper · explained in 6 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• Grounding — 1× in this paper · explained in 3 episodes
• Large Language Model (LLM) — 1× in this paper · explained in 3 episodes
• Large Language Models (LLMs) — 1× in this paper · explained in 6 episodes
• Multi-agent Collaboration — 1× in this paper · explained in 2 episodes
• Task Decomposition — 1× in this paper · explained in 2 episodes
• Test-time Scaling — 1× in this paper · explained in 2 episodes

Sources

• Agent S2: A Compositional Generalist-Specialist Framework for Computer Use Agents
• Seeing the Whole Elephant: A Benchmark for Failure Attribution in LLM-based Multi-Agent Systems
• Beyond Trajectory-Level Attribution: Graph-Based Credit Assignment for Agentic Reinforcement Learning
• Debate or Vote: Which Yields Better Decisions in Multi-Agent Large Language Models?
• Multi-Agent Collaboration via Evolving Orchestration
• Group-in-Group Policy Optimization for LLM Agent Training
• Recursive Agent Optimization
• Anagent For Enhancing Scientific Table & Figure Analysis
• MetaGPT: Meta Programming for A Multi-Agent Collaborative Framework
• OWL: Optimized Workforce Learning for General Multi-Agent Assistance in Real-World Task Automation
• Automated Design of Agentic Systems
• R-Zero: Self-Evolving Reasoning LLM from Zero Data
• MAS-Orchestra: Understanding and Improving Multi-Agent Reasoning Through Holistic Orchestration and Controlled Benchmarks
• DS-1000: A Natural and Reliable Benchmark for Data Science Code Generation
• Agentic Reinforcement Learning with Implicit Step Rewards
• DoVer: Intervention-Driven Auto Debugging for LLM Multi-Agent Systems
• MMLongBench-Doc: Benchmarking Long-context Document Understanding with Visualizations
• MiniMax-M1: Scaling Test-Time Compute Efficiently with Lightning Attention
• MALT: Improving Reasoning with Multi-Agent LLM Training
• Weak-for-Strong: Training Weak Meta-Agent to Harness Strong Executors

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