Zhaoling Chen, Robert Tang, Gangda Deng, Fang Wu, Jialong Wu, Zhiwei Jiang, Viktor Prasanna, Arman Cohan, Xingyao Wang
| Challenge: | Existing approaches struggle to efficiently navigate complex codebases when identifying relevant code snippets. |
| Approach: | They propose a graph-guided agent framework that addresses code localization through a distributed graph-based agent. |
| Outcome: | The proposed framework improves accuracy on real-world benchmarks and can be used to locate code snippets at a cost of 86%. |
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Xiangyan Liu, Bo Lan, Zhiyuan Hu, Yang Liu, Zhicheng Zhang, Fei Wang, Michael Qizhe Shieh, Wenmeng Zhou
| Challenge: | Large Language Models excel in stand-alone code tasks but struggle with handling entire code repositories. |
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| Outcome: | The proposed system integrates LLM agents with graph database interfaces extracted from code repositories. |
CodeTree: Agent-guided Tree Search for Code Generation with Large Language Models (2025.naacl-long)
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| Challenge: | coding tasks require generated code to be fully executable and functionally correct . current agentic approaches struggle with multi-stage planning, generating, and debugging . |
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nvAgent: Automated Data Visualization from Natural Language via Collaborative Agent Workflow (2025.acl-long)
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| Challenge: | *Natural Language to Visualization (NL2Vis) seeks to transform natural-language descriptions into visual representations of given tables. |
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| Challenge: | Large language models (LLMs) have impressive proficiency in natural language processing, but performance in code generation tasks remains limited. |
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| Challenge: | Existing structure-aware approaches treat structure as serialized text prompts or auxiliary training objectives, failing to provide explicit guidance during inference. |
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| Challenge: | Existing large-scale (> 30 B) models are costly and collapse when downsized to small open-source models. |
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MatPlotAgent: Method and Evaluation for LLM-Based Agentic Scientific Data Visualization (2024.findings-acl)
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CodeAgent: Enhancing Code Generation with Tool-Integrated Agent Systems for Real-World Repo-level Coding Challenges (2024.acl-long)
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| 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. |
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