Recent multi-agent code generation frameworks often verify natural-language plans by simulating their behavior on test cases before code generation. However, such verification remains unreliable because model hallucination may cause flawed plans to be incorrectly accepted. Inspired by neuro-symbolic reasoning, we propose PaveDSL, which transforms natural-language plans into structured designs expressed in a domain-specific language. Based on this structured representation, PaveDSL combines symbolic checks with neural inference to examine plans before code generation. Specifically, PaveDSL applies two complementary verifiers in parallel. The deterministic symbolic verifier checks structural constraints without invoking an LLM, while the agent verifier performs graph-guided semantic reasoning over the structured design. If verification fails, a repair agent localizes errors and revises the design before code generation. Experiments on multiple benchmarks with different LLMs show that PaveDSL consistently improves the performance of baseline code generation methods.

Code refactoring is essential for software maintainability, yet current Large Language Model (LLM) based frameworks primarily focus on syntax and neglect the vital semantic signals in code comments. As pointed out in Fowler’s refactoring theory, explanatory comments function as semantic anchors that provide necessary guidance for method extraction. Moreover, research reports show that more than 84 percent of codebases lack appropriate code comments, which hinders the leverage of such guidance. To bridge this gap, we propose MACOR, a Multi-Agent framework for COmment-guided code Refactoring. In specific, MACOR populates original code with precise comments to provide necessary semantic guidance for the subsequent refactoring process. These generated signals are employed to retrieve expert examples. An iterative feedback is incorporated loop for validation. Experiments are conducted on three benchmarks using three base LLMs. Experimental results show that MACOR significantly optimizes code quality and achieves higher developer acceptance compared to the representative baselines.