DocAgent: A Multi-Agent System for Automated Code Documentation Generation (2025.acl-demo)
Copied to clipboard
| Challenge: | Existing methods for generating documentation using Large Language Models (LLMs) produce incomplete, unhelpful, or factually incorrect outputs. |
| Approach: | They propose a novel collaborative system that uses topological code processing for incremental context building to generate documentation by agents. |
| Outcome: | The proposed system outperforms baselines in completeness, helpfulness, and truthfulness evaluations. |
Similar Papers
CodeAgent: Enhancing Code Generation with Tool-Integrated Agent Systems for Real-World Repo-level Coding Challenges (2024.acl-long)
Copied to clipboard
| Challenge: | Large Language Models excel in simple tasks such as generating standalone code units, but real-world software development often involves complex code repositories with complex dependencies and extensive documentation. |
| Approach: | They propose a novel LLM-based agent framework that employs external tools for effective repo-level code generation. |
| Outcome: | The proposed framework outperforms commercial products like Github Copilot in the humanEval benchmark and shows that it is adaptable and efficient across multiple code generation tasks. |
DocAgent: An Agentic Framework for Multi-Modal Long-Context Document Understanding (2025.emnlp-main)
Copied to clipboard
| Challenge: | Existing approaches to document understanding are limited due to limited context length or fail to fully leverage multi-modal information. |
| Approach: | They propose a multi-agent framework for long-context document understanding that imitates human reading practice. |
| Outcome: | The proposed framework surpasses human-level benchmarks on long-context document understanding while maintaining a short context length. |
CodeAgent: Autonomous Communicative Agents for Code Review (2024.emnlp-main)
Copied to clipboard
Xunzhu Tang, Kisub Kim, Yewei Song, Cedric Lothritz, Bei Li, Saad Ezzini, Haoye Tian, Jacques Klein, Tegawendé Bissyandé
| Challenge: | Existing methods for code review rely on single input-output generative models and thus lack the collaborative nature of code review. |
| Approach: | They propose a multi-agent Large Language Model (LLM) system for code review automation that incorporates a supervisory agent to ensure that all the agents’ contributions address the initial review question. |
| Outcome: | The proposed system detects inconsistencies between code changes and commit messages, identify vulnerabilities, validates code style adherence, and suggests code revisions. |
MapCoder: Multi-Agent Code Generation for Competitive Problem Solving (2024.acl-long)
Copied to clipboard
| Challenge: | Large language models (LLMs) have impressive proficiency in natural language processing, but performance in code generation tasks remains limited. |
| Approach: | They propose a framework that emulates the full cycle of program synthesis as observed in humans. |
| Outcome: | The proposed framework replicates the full cycle of program synthesis as observed in human developers. |
AutoAgent: A Fully-Automated and Zero-Code Framework for LLM Agents (2026.findings-acl)
Copied to clipboard
| Challenge: | Large Language Model (LLM) agents have demonstrated remarkable capabilities in task automation and intelligent decision-making. |
| Approach: | They propose a Fully-Automated and highly Self-Developing framework that enables users to create and deploy LLM agents using natural language alone. |
| Outcome: | AutoAgent is a fully-automated and highly self-developing framework that enables users to create and deploy LLM agents using natural language alone. |
DocCGen: Document-based Controlled Code Generation (2024.emnlp-main)
Copied to clipboard
Sameer Pimparkhede, Mehant Kammakomati, Srikanth Tamilselvam, Prince Kumar, Ashok Kumar, Pushpak Bhattacharyya
| Challenge: | Large language models (LLMs) produce state-of-the-art performance on natural language to code generation for resource-rich general-purpose languages like C++, Java, and Python. |
| Approach: | They propose a framework that breaks the NL-to-Code generation task into two steps . they use library documentation to detect the correct libraries and schema rules extracted from the documentation to constrain the decoding . |
| Outcome: | The proposed framework improves different sized language models across all six evaluation metrics, reducing syntactic and semantic errors in structured code. |
UIOrchestra: Generating High-Fidelity Code from UI Designs with a Multi-agent System (2025.findings-emnlp)
Copied to clipboard
Chuhuai Yue, Jiajun Chai, Yufei Zhang, Zixiang Ding, Xihao Liang, Peixin Wang, Shihai Chen, Wang Yixuan, null Wangyanping, Guojun Yin, Wei Lin
| Challenge: | Recent advances in large language models have significantly improved automated code generation . however, the translation of complex mobile UI designs into high-fidelity front-end code remains a challenge . |
| Approach: | They propose a collaborative multi-agent system to reconstruct static single-page apps from mockups. |
| Outcome: | The proposed system outperforms existing methods in reconstructing complex app pages . the code and data will be released upon paper acceptance . |
PresentAgent: Multimodal Agent for Presentation Video Generation (2025.emnlp-demos)
Copied to clipboard
| Challenge: | Existing methods for generating static slides or text summaries are limited to producing narrated presentations. |
| Approach: | They propose a multimodal agent that transforms long-form documents into narrated presentations. |
| Outcome: | The present agent produces fully synchronized visual and spoken content that closely mimics human-style presentations. |
Paper2Rebuttal: A Multi-Agent Framework for Transparent Author Response Assistance (2026.acl-long)
Copied to clipboard
| Challenge: | Current approaches to writing effective rebuttals are limited by the direct-to-text generation problem . authors must accurately decipher reviewer intent while ensuring every response is firmly anchored in verifiable manuscript details. |
| Approach: | They propose a framework that reframes rebuttal generation as an evidence-centric planning task. |
| Outcome: | The proposed framework outperforms baselines in coverage, faithfulness, and strategic coherence. |
CodeWiki: Evaluating AI’s Ability to Generate Holistic Documentation for Large-Scale Codebases (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing automated methods struggle to capture rich semantic dependencies and architectural structure. |
| Approach: | They propose a framework for automated repository-level documentation across seven programming languages. |
| Outcome: | The proposed framework outperforms the closed-source DeepWiki benchmark by 68.79% and is open source to support future research. |