Clover：一种基于随机树-of-thoughts的神经符号代理体系用于验证RTL修复
Clover: A Neural-Symbolic Agentic Harness with Stochastic Tree-of-Thoughts for Verified RTL Repair
AI总结：
Clover通过结构化搜索代码操作解决RTL修复问题，结合LLM和符号求解器动态调度任务，实现高可靠性修复，修复率高达96.8%。
摘要：
RTL程序修复仍是硬件设计和验证的关键瓶颈。传统自动程序修复方法依赖预定义模板和合成，限制了其故障覆盖范围。大型语言模型（LLMs）和基于它们的编码代理提供了灵活性，但在处理长RTL代码和波形时存在随机性和上下文腐蚀问题。我们提出了Clover，一种神经符号代理体系，通过在代码操作上进行结构化搜索来探索验证解决方案。认识到不同的修复操作偏好不同的策略，Clover动态调度任务给专门的LLM代理或符号求解器。其核心是随机树-of-thoughts，一种测试时间扩展机制，管理主代理的上下文作为搜索树，平衡探索与利用以获得可靠结果。一个RTL专用工具箱进一步使代理能够与调试环境交互。在RTL修复基准测试中，Clover在固定时间限制内修复了96.8%的故障，覆盖比纯传统和LLM基基线分别多94%和63%，同时实现了平均pass@1率为87.5%，展示了高可靠性和有效性。
英文摘要：
RTL program repair remains a critical bottleneck in hardware design and verification. Traditional automatic program repair (APR) methods rely on predefined templates and synthesis, limiting their bug coverage. Large language models (LLMs) and coding agents based on them offer flexibility but suffer from randomness and context corruption when handling long RTL code and waveforms. We present Clover, a neural-symbolic agentic harness that orchestrates RTL repair as a structured search over code manipulations to explore a validated solution for the bug. Recognizing that different repair operations favor distinct strategies, Clover dynamically dispatches tasks to specialized LLM agents or symbolic solvers. At its core, Clover introduces stochastic tree-of-thoughts, a test-time scaling mechanism that manages the main agent's context as a search tree, balancing exploration and exploitation for reliable outcomes. An RTL-specific toolbox further empowers agents to interact with the debugging environment. Evaluated on the RTL-repair benchmark, Clover fixes 96.8% of bugs within a fixed time limit, covering 94% and 63% more bugs than both pure traditional and LLM-based baselines, respectively, while achieving an average pass@1 rate of 87.5%, demonstrating high reliability and effectiveness.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
