TicTacBench：评估编程智能体 RTL 时序收敛能力的基准
TicTacBench: Benchmarking Timing Closure Capabilities of Coding Agents
AI 写代码很溜，但芯片时序收敛还是硬骨头：8 个前沿 LLM 跑了 300 多次，最强的也只过了 53.3% 的任务。
研究团队发布 TicTacBench，用 30 个任务测试编程智能体在 RTL 时序收敛上的能力，每个任务附带次优 RTL 设计、真实时序约束、功能等价性验证和时序报告，并以布局布线后（post-PnR）结果判定。在超过 300 次运行、覆盖 8 个前沿 LLM 的测试中，最好的智能体也只完成 53.3% 的任务，平均造成 7.18% 的 ADP（面积延迟积）劣化，同时带来 8.83% 的 EDDP 改善。论文归纳出智能体时序收敛失败的常见类别，并提出 TicTacSkill 方法，引导智能体遵循标准时序收敛流程，将时序收敛率提高 9%。
TicTacBench: Benchmarking Timing Closure Capabilities of Coding Agents
Recent advances in large language models (LLMs) have led to the emergence of coding agents capable of performing complex engineering tasks, including register-transfer level (RTL) design and optimization. Existing RTL benchmarks mainly evaluate functional correctness and performance, power, and area (PPA) of the generated RTL designs, leaving agents' ability for \emph{timing closure} under-evaluated. We propose TicTacBench, a benchmark specifically designed to evaluate coding agents' capabilities for RTL-level timing closure under post-place-and-route (post-PnR) evaluation. TicTacBench contains 30 diverse tasks, each provided with a suboptimal RTL design, realistic timing constraints, functional equivalence verification, and timing reports. With over 300 runs of coding agents driven by 8 frontier LLMs, we find that even the best agent can only close 53.3\% of tasks with 7.18\% area-delay product (ADP) degradation and 8.83\% energy-delay-squared product (EDDP) improvement on average. We identify common failure categories that explain why agents fail to close timing. Then we propose TicTacSkill, a new method that guides agents to follow standard timing-closure procedures and improves the Timing Closure Rate by 9\%. These results suggest that while coding agents have made significant progress in RTL design, their timing-closure capability still has substantial room for improvement.