Mixture-of-Minds: Multi-Agent Reinforcement Learning for Table Understanding (2026.acl-long)
Copied to clipboard
Yuhang Zhou, Mingrui Zhang, Ke Li, Mingyi Wang, Qiao Liu, Qifei Wang, Jiayi Liu, Fei Liu, Serena Li, Weiwei LI, Mingze Gao, Abhishek Kumar, Xiangjun Fan, Zhuokai Zhao, Lizhu Zhang
| Challenge: | Large language models (LLMs) have shown promise on understanding and reasoning over tables, but current approaches remain limited. |
| Approach: | They propose a multi-agent framework that decomposes table reasoning into three specialized roles: planning, coding, and answering. |
| Outcome: | The proposed framework decomposes table reasoning into three specialized roles: planning, coding, and answering. |
Similar Papers
Table-based Fact Verification with Self-adaptive Mixture of Experts (2022.findings-acl)
Copied to clipboard
| Challenge: | Existing research focuses on table-based fact verification, but a new trend is extending the scope to structured evidence. |
| Approach: | They propose a mixture-of-experts neural network to recognize and execute different types of reasoning . they use a management module to decide the contribution of each expert network to the verification result . |
| Outcome: | The proposed method achieves 85.1% accuracy on the TabFact dataset, comparable with the previous state-of-the-art models. |
Multi-Agent Reasoning Improves Compute Efficiency: Pareto-Optimal Test-Time Scaling (2026.acl-srw)
Copied to clipboard
| Challenge: | Inference methods that prioritize raw performance over cost-effective compute usage are not efficient for real-world applications. |
| Approach: | They evaluate inference scaling strategies to determine their computational efficiency tradeoffs . they find debate and mixture-of-agents outperform self-consistency by 1.3% and 2.7% points . |
| Outcome: | The proposed scaling strategies outperform self-consistency, self-refinement, multi-agent debate and mixture-of-a agents on reasoning tasks. |
Multi-LLM Collaborative Search for Complex Problem Solving (2026.findings-acl)
Copied to clipboard
| Challenge: | Large language models (LLMs) often struggle with complex reasoning tasks due to the vast reasoning space inherent in the complexity and inherent ambiguities of natural languages. |
| Approach: | They propose a mixture-of-search-agents paradigm that integrates diverse reasoning pathways by combining independent exploration and iterative refinement among multiple LLMs. |
| Outcome: | The proposed approach improves performance over single-agent and multi-agend baselines in complex mathematical and commonsense reasoning tasks. |
SQLAgent: Learning to Explore Before Generating as a Data Engineer (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing approaches to large language models fail to generalize in complex, real-world settings due to database-specific nature of SQL reasoning. |
| Approach: | They propose a two-stage LLM-based framework that decouples knowledge acquisition from query generation. |
| Outcome: | The proposed framework significantly improves accuracy over baselines on large-scale benchmarks. |
Table-R1: Region-based Reinforcement Learning for Table Understanding (2026.findings-acl)
Copied to clipboard
Zhenhe Wu, Jian Yang, Zhongjiang He, Changzai Pan, Jiaheng Liu, Xianjie Wu, Yu Zhao, Shuangyong Song, Yongxiang Li, Zhoujun Li, Xuelong Li
| Challenge: | Tables are a widely used data format that poses unique challenges for language models due to their structured row-column interactions. |
| Approach: | They propose a region-based reinforcement learning approach that integrates region evidence into reasoning steps. |
| Outcome: | The proposed method outperforms baseline models on three benchmark datasets and significantly reduces the reasoning token consumption by 67.5%. |
Mixture-of-Skills: Learning to Optimize Data Usage for Fine-Tuning Large Language Models (2024.emnlp-main)
Copied to clipboard
| Challenge: | Large language models are fine-tuned on diverse datasets to develop a range of skills . each skill has unique characteristics, and datasets are heterogeneous and imbalanced . a general, model-agnostic, reinforcement learning framework is proposed to optimize data usage . |
| Approach: | They propose a general, model-agnostic, reinforcement learning framework that optimizes data usage automatically during the fine-tuning process. |
| Outcome: | The proposed framework optimizes data usage automatically during the fine-tuning process. |
MATA: Multi-Agent Framework for Reliable and Flexible Table Question Answering (2026.findings-acl)
Copied to clipboard
| Challenge: | Recent advances in Large Language Models have significantly improved table understanding tasks . practical deployment of TableQA systems presents several persistent challenges . |
| Approach: | They propose a multi-agent TableQA framework that leverages multiple reasoning paths and tools built with small language models. |
| Outcome: | The proposed framework achieves state-of-the-art accuracy and efficient reasoning while avoiding excessive LLM inference. |
Agentic Reasoning: A Streamlined Framework for Enhancing LLM Reasoning with Agentic Tools (2025.acl-long)
Copied to clipboard
| Challenge: | Existing reasoning methods excel in structured domains like math and code, but they are not all effective in knowledge-intensive tasks. |
| Approach: | They introduce a framework that enhances large language model reasoning by integrating external tool-using agents. |
| Outcome: | The proposed framework achieves state-of-the-art among public models and delivers comparable performance to OpenAI Deep Research. |
Can GRPO Boost Complex Multimodal Table Understanding? (2025.emnlp-main)
Copied to clipboard
Xiaoqiang Kang, Shengen Wu, Zimu Wang, Yilin Liu, Xiaobo Jin, Kaizhu Huang, Wei Wang, Yutao Yue, Xiaowei Huang, Qiufeng Wang
| Challenge: | Existing table understanding methods struggle with low initialization accuracy and coarse rewards in tabular contexts. |
| Approach: | They propose a three-stage RL framework that enhances multimodal table understanding through: (1) Warm-up that prompts initial perception and reasoning capabilities; (2) Perception Alignment GRPO (PA-GRPO); (3) Hint-Completion GR PO (HC-GRP); |
| Outcome: | The proposed framework outperforms existing models on held-in and held-out datasets, outperforming SFT and GRPO largely. |
Enhancing the General Agent Capabilities of Low-Paramter LLMs through Tuning and Multi-Branch Reasoning (2024.findings-naacl)
Copied to clipboard
| Challenge: | Open-source pre-trained Large Language Models exhibit strong language understanding and generation capabilities, making them highly successful in a variety of tasks. |
| Approach: | They propose a method to construct agent-specific data using GPT-4 and supervised fine-tuning . they find that supervised tunning can significantly reduce hallucination outputs and formatting errors in agent tasks . |
| Outcome: | The proposed method improves on five agent tasks of AgentBench. |