# DeepAssert: An LLM-Aided Verification Framework with Fine-Grained Assertion Generation for Modules with Extracted Module Specifications

arXiv: https://arxiv.org/abs/2509.14668 · HTML: https://mlsanity.com/paper/2509.14668/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Sep 2025.

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

- [QiMeng-CodeV-SVA: Training Specialized LLMs for Hardware Assertion Generation via RTL-Grounded Bidirectional Data Synthesis](https://mlsanity.com/paper/2603.14239/) (2026)

## Abstract (arXiv)

Assertion-Based Verification (ABV) is a crucial method for ensuring that logic designs conform to their architectural specifications. However, existing assertion generation methods primarily rely on information either from the design specification, or register-transfer level (RTL) code. The former methods are typically limited to generating assertions for the top-level design. As the top-level design is composed of different modules without module-level specifications, they are unable to generate deep assertions that target the internal functionality of modules. The latter methods often rely on a golden RTL model, which is difficult to obtain. To address the above limitations, this paper presents a novel large language model (LLM)-aided verification framework named DeepAssert. DeepAssert is capable of analyzing the invocation relationships between modules and extracting independent specifications for each module with its I/O port information. These extracted specifications are subsequently used to guide LLMs to automatically generate fine-grained deep assertions for these modules. Our evaluation demonstrates that DeepAssert significantly outperforms existing methods such as AssertLLM and Spec2Assertion in generating high-quality deep assertions for modules. Furthermore, when integrated with these methods, DeepAssert can enhance the overall quality of the assertions generated. This allows for a more comprehensive and effective verification process.