# GoGoTB: Agentic RTL Verification with Specification-Grounded Coverage Closure

arXiv: https://arxiv.org/abs/2607.26181 · HTML: https://mlsanity.com/paper/2607.26181/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Software engineering coding agents, one of 16 directions in LLM agents and tool use.
- Builds on 9 of its 24 references that are in the map.
- First posted on arXiv 28 Jul 2026.

## Placed in

- LLM agents and tool use / [Software engineering coding agents](https://mlsanity.com/agents/software-engineering-coding-agents/)

## Builds on (most cited first, 9 of 9)

- [Reflexion: Language Agents with Verbal Reinforcement Learning](https://mlsanity.com/paper/2303.11366/) (2023)
- [A Survey on Large Language Model based Autonomous Agents](https://mlsanity.com/paper/2308.11432/) (2023)
- [AutoBench: Automatic Testbench Generation and Evaluation Using LLMs for HDL Design](https://mlsanity.com/paper/2407.03891/) (2024)
- [AssertLLM: Generating Hardware Verification Assertions from Design Specifications via Multi-LLMs](https://mlsanity.com/paper/2411.14436/) (2024)
- [CorrectBench: Automatic Testbench Generation with Functional Self-Correction using LLMs for HDL Design](https://mlsanity.com/paper/2411.08510/) (2024)
- [Using LLMs to Facilitate Formal Verification of RTL](https://mlsanity.com/paper/2309.09437/) (2023)
- [UVLLM: An Automated Universal RTL Verification Framework using LLMs](https://mlsanity.com/paper/2411.16238/) (2024)
- [From Concept to Practice: an Automated LLM-aided UVM Machine for RTL Verification](https://mlsanity.com/paper/2504.19959/) (2025)
- [Spec2Assertion: Automatic Pre-RTL Assertion Generation using Large Language Models with Progressive Regularization](https://mlsanity.com/paper/2505.07995/) (2025)

## Built on by (most cited first, 1 of 1)

- [BEHAVE: Functional Behavior Modeling Enables Self-Improving Agents for Hardware Design and Verification](https://mlsanity.com/paper/2609.34785/) (2026)

## Abstract (arXiv)

Functional verification dominates integrated circuit (IC) front-end engineering effort, and a single missed bug that escapes to silicon can trigger a costly respin. Recent large language models (LLMs) offer new opportunities to automate this process, yet existing LLM-based approaches generate each component through independent single-turn calls with no shared context, leaving interface mismatches undetected and reported coverage disconnected from specification requirements. To address these challenges, we present GoGoTB, an agentic framework that achieves end-to-end verification closure through three subsystems: an agentic execution control layer, an evolvable knowledge system, and specification-grounded coverage closure. The execution control layer separates deterministic enforcement from LLM reasoning at every tool and stage boundary. The knowledge system dispatches methodology and design-specific expertise on demand. The coverage framework anchors every bin to a named specification behavior so that each residual gap has a diagnosable root cause and a targeted remedy. Tested on 8 register transfer level (RTL) designs without any human intervention, GoGoTB achieves 100% environment generation success and averages 98.4% line, 97.2% branch, 97.0% toggle, and 83.2% functional coverage. No prior work successfully generates a complete verification environment or achieves meaningful coverage on the same benchmarks.