Papers with automating code generation

2 papers
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.
Approach: They propose a collaborative agent workflow for NL2Vis that incorporates three agents . the model is called **nvAgent** and comprises a processor agent for database processing and context filtering, a composer agent for planning visualization generation and a validator agent for code translation and output verification.
Outcome: The proposed workflow surpasses state-of-the-art models on the VisEval benchmark.
SeDev: Structured Semantic Exploration for LLM-Driven Code Generation (2026.acl-long)

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Challenge: Large Language Models (LLMs) have shown remarkable capabilities in automating code generation, but they suffer from insufficient exploration of the vast solution space.
Approach: They propose a large-scale LLM-driven code generation framework that efficiently finds high-quality solutions in only a few iterations.
Outcome: The proposed framework outperforms baselines while maintaining reasonable time and computational costs.

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