Papers with Multi-Agent framework

5 papers
A Multi-Agent Framework for Quantitative Finance : An Application to Portfolio Management Analytics (2025.emnlp-industry)

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Challenge: Recent advances in Large Language Models (LLMs) have opened up promising new avenues by enhancing reasoning and inference capabilities across diverse data and information sources.
Approach: They propose a multi-agent framework that facilitates mathematical modeling and data analytics by dynamically generating executable code.
Outcome: The proposed framework outperforms existing models on portfolio management tasks and provides human-readable explanations for its predictions.
The Power of Many: Multi-Agent Multimodal Models for Cultural Image Captioning (2025.naacl-long)

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Challenge: Large Multimodal Models exhibit impressive performance across multimodal tasks . effectiveness in cross-cultural contexts limited due to predominantly Western-centric nature of data and models . multi-agent models have shown significant capability in solving complex tasks despite limitations in crosscultural context .
Approach: They propose to use a multi-agent framework to enhance cross-cultural image captioning using LMMs with distinct cultural personas to evaluate cultural information within image captions.
Outcome: The proposed model outperforms single-agent models across different metrics and offers valuable insights for future research.
Can Large Language Models Grasp Legal Theories? Enhance Legal Reasoning with Insights from Multi-Agent Collaboration (2024.findings-emnlp)

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Challenge: Existing studies have found that when LLMs are given criminal facts and legal rules, then asked whether cases constitute a certain charge, they struggle to understand legal theories and perform basic legal reasoning tasks.
Approach: They propose a task to assess LLMs' understanding of legal theories and reasoning capabilities by using a novel framework: Multi-Agent framework for improving complex legal reasoning capability.
Outcome: The proposed framework improves LLMs' understanding of legal theories and reasoning abilities in real-world scenarios.
Let the Comments Speak: A Multi-Agent Framework based on Large Language Model for Comment-Guided Code Refactoring (2026.findings-acl)

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Challenge: Current Large Language Models focus on syntax and ignore the vital semantic signals in code comments.
Approach: They propose a Multi-Agent framework for COmment-guided code Refactoring that populates original code with precise comments to provide necessary semantic guidance for subsequent refactoring.
Outcome: The proposed framework significantly improves code quality and achieves higher developer acceptance compared to baselines.
When Cultures Meet: Multicultural Text-to-Image Generation (2026.findings-acl)

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Challenge: a new task to evaluate text-to-image generation models for multicultural scenes is unexplored.
Approach: They propose a benchmark task to evaluate text-to-image models in multicultural settings . they use a dataset of 9,000 images spanning five countries, three age groups, two genders, 25 historical landmarks, and five languages to analyze behavior .
Outcome: The proposed benchmark analyzes the behavior of state-of-the-art models across multiple dimensions including alignment, image quality, aesthetics, knowledge, and fairness.

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