ComfyUI-R1: Exploring Reasoning Models for Workflow Generation (2026.findings-acl)
Copied to clipboard
| Challenge: | ComfyUI-R1 is the first large reasoning model for automated workflow generation. |
| Approach: | They propose a large reasoning model for automated workflow generation that builds on curated knowledge bases and a two-stage framework to fine-tune models for cold start and reinforcement learning for incentivizing reasoning capability. |
| Outcome: | The proposed model achieves 97% format validity rate, high pass rate, node-level and graph-level F1 scores, surpassing prior state-of-the-art methods that employ leading closed-source models such as GPT-4o and Claude series. |
Similar Papers
ComfyFlow: Benchmarking LLMs for AIGC Workflow Generation (2026.findings-acl)
Copied to clipboard
Zhenran Xu, Yiyu Wang, Yunxin li, Muyang Ye, null Yangxue, Kai Chen, Longyue Wang, Weihua Luo, Baotian Hu, Min Zhang
| Challenge: | Large language models (LLMs) have shown promising advances in tackling human-level tasks, but generating workflows for collaborative AI systems remains a critical and challenging step. |
| Approach: | They propose a benchmark to evaluate LLMs’ ability to generate executable and instruction-following AIGC workflows in ComfyUI. |
| Outcome: | The proposed benchmarks show that LLMs can generate executable and instruction-following AIGC workflows in ComfyUI. |
ComfyUI-Copilot: An Intelligent Assistant for Automated Workflow Development (2025.acl-demo)
Copied to clipboard
Zhenran Xu, Yangxue Yangxue, Yiyu Wang, Qingli Hu, Zijiao Wu, Baotian Hu, Longyue Wang, Weihua Luo, Kaifu Zhang
| Challenge: | ComfyUI-Copilot is a large language model-powered plugin for AI-driven art creation. |
| Approach: | They propose a large language model-powered plugin to enhance the usability of ComfyUI. |
| Outcome: | The new plugin improves the usability and efficiency of ComfyUI . it offers intelligent node and model recommendations and automated one-click workflow construction. |
One Missing Piece for Open-Source Reasoning Models: A Dataset to Mitigate Cold-Starting Short CoT LLMs in RL (2025.acl-industry)
Copied to clipboard
Hyungjoo Chae, Dongjin Kang, Jihyuk Kim, Beong-woo Kwak, Sunghyun Park, Haeju Park, Jinyoung Yeo, Moontae Lee, Kyungjae Lee
| Challenge: | Existing large reasoning models are limited by their closed nature and high API costs and safety issues. |
| Approach: | They propose to build a long CoT dataset with existing short CoT LLMs that are not trained for inference-time scaling. |
| Outcome: | The proposed model achieves quality comparable to—or slightly below—R1 and is able to think longer and provide control over the thought budget to better manage the overthinking problem. |
CoT-RAG: Integrating Chain of Thought and Retrieval-Augmented Generation to Enhance Reasoning in Large Language Models (2025.findings-emnlp)
Copied to clipboard
Feiyang Li, Peng Fang, Zhan Shi, Arijit Khan, Fang Wang, Weihao Wang, null Zhangxin-hw, Cui Yongjian
| Challenge: | Chain-of-thought reasoning has two key limitations: lack of reliability when solely relying on LLM-generated reasoning chains and interference from natural language reasoning steps with the models’ inference logic. |
| Approach: | They propose a chain-of-thought reasoning framework with three key designs to address these issues. |
| Outcome: | The proposed framework improves the performance of large language models on complex tasks by incorporating knowledge graphs and learnable knowledge case-aware RAG. |
DecisionFlow: Advancing Large Language Model as Principled Decision Maker (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Current language models lack the structured deliberation needed for high-stakes tasks such as healthcare and finance. |
| Approach: | They propose a decision-making framework that guides models to reason over structured representations of actions, attributes, and constraints. |
| Outcome: | The proposed framework achieves up to 30% accuracy gains over strong prompting baselines and enhances alignment in outcomes. |
Reasoning with OmniThought: A Large CoT Dataset with Verbosity and Cognitive Difficulty Annotations (2026.acl-long)
Copied to clipboard
| Challenge: | Existing resources often fail to provide extensive reasoning problems with coherent CoT processes distilled from multiple teacher models. |
| Approach: | They propose a large-scale dataset featuring 2 million CoT processes generated by multiple powerful LRMs. |
| Outcome: | The proposed dataset features 2 million CoT processes and is validated by multiple powerful LRMs. |
ChartM3: A Multi-Stage Code-Driven Pipeline for Constructing Multi-Dimensional and Multi-Step Visual Reasoning Data in Chart Comprehension (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Currently, research on complex chart understanding tasks is limited . a pipeline for visual reasoning datasets addresses these limitations . |
| Approach: | They propose a code-driven pipeline for generating visual reasoning datasets . pipeline integrates retrieval-augmented generation to retrieve professional chart templates . |
| Outcome: | The proposed pipeline enhances chart diversity and data quality through model-based evaluation. |
FusionFlow: Enabling Deep Structural Exploration for Automated Agentic Workflow Generation (2026.acl-long)
Copied to clipboard
| Challenge: | Existing workflow generation methods rely on incremental refinement or tree-based search over a single evolving workflow. |
| Approach: | They propose a framework centered on workflow fusion that synthesizes multiple independently evolved workflows and allows exploration of deeper regions of the workflow space within a finite budget. |
| Outcome: | Experiments show that FusionFlow outperforms existing workflow generation methods on six reasoning benchmarks. |
LMFlow: An Extensible Toolkit for Finetuning and Inference of Large Foundation Models (2024.naacl-demo)
Copied to clipboard
| Challenge: | Foundation models have demonstrated a great ability to achieve general human-level intelligence far beyond traditional approaches. |
| Approach: | They propose a toolkit to simplify the finetuning of general foundation models. |
| Outcome: | The proposed toolkit simplifies the domain- and task-aware finetuning of general foundation models with limited computing resources. |
AdaptFlow: Adaptive Workflow Optimization via Meta-Learning (2025.findings-emnlp)
Copied to clipboard
Runchuan Zhu, Bowen Jiang, Lingrui Mei, Fangkai Yang, Lu Wang, Haoxiang Gao, Fengshuo Bai, Pu Zhao, Qingwei Lin, Saravan Rajmohan, Dongmei Zhang
| Challenge: | Existing approaches to large language models rely on static templates or manual workflows. |
| Approach: | AdaptFlow is a language-based meta-learning framework inspired by model-agnostic meta- learning. |
| Outcome: | AdaptFlow outperforms manual and automated workflows on question answering, code generation and mathematical reasoning benchmarks. |