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Agentic AI-based Coverage Closure for Formal Verification

Sivaram Pothireddypalli, Ashish Raman, Deepak Narayan Gadde, Aman Kumar · Mar 3, 2026 · Citations: 0

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How to use this page

Provisional trust

This page is a lightweight research summary built from the abstract and metadata while deeper extraction catches up.

Best use

Background context only

What to verify

Read the full paper before copying any benchmark, metric, or protocol choices.

Evidence quality

Provisional

Derived from abstract and metadata only.

Abstract

Coverage closure is a critical requirement in Integrated Chip (IC) development process and key metric for verification sign-off. However, traditional exhaustive approaches often fail to achieve full coverage within project timelines. This study presents an agentic AI-driven workflow that utilizes Large Language Model (LLM)-enabled Generative AI (GenAI) to automate coverage analysis for formal verification, identify coverage gaps, and generate the required formal properties. The framework accelerates verification efficiency by systematically addressing coverage holes. Benchmarking open-source and internal designs reveals a measurable increase in coverage metrics, with improvements correlated to the complexity of the design. Comparative analysis validates the effectiveness of this approach. These results highlight the potential of agentic AI-based techniques to improve formal verification productivity and support comprehensive coverage closure.

Abstract-only analysis — low confidence

All signals on this page are inferred from the abstract only and may be inaccurate. Do not use this page as a primary protocol reference.

  • This page is still relying on abstract and metadata signals, not a fuller protocol read.
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Should You Rely On This Paper?

Signal extraction is still processing. This page currently shows metadata-first guidance until structured protocol fields are ready.

Best use

Background context only

Use if you need

A provisional background reference while structured extraction finishes.

Main weakness

This page is still relying on abstract and metadata signals, not a fuller protocol read.

Trust level

Provisional

Usefulness score

Unavailable

Eval-fit score is unavailable until extraction completes.

Human Feedback Signal

Not explicit in abstract metadata

Evaluation Signal

Weak / implicit signal

Usefulness for eval research

Provisional (processing)

Extraction confidence 0%

If you are doing eval pipeline work, start here:

Human Eval Hub LLM-as-Judge Hub Pairwise Preference Hub Tool-Use Eval Hub

What We Could Verify

These are the protocol signals we could actually recover from the available paper metadata. Use them to decide whether this paper is worth deeper reading.

Human Feedback Types

provisional (inferred)

None explicit

No explicit feedback protocol extracted.

"Coverage closure is a critical requirement in Integrated Chip (IC) development process and key metric for verification sign-off."

Evaluation Modes

provisional (inferred)

Automatic metrics

Includes extracted eval setup.

"Coverage closure is a critical requirement in Integrated Chip (IC) development process and key metric for verification sign-off."

Quality Controls

provisional (inferred)

Not reported

No explicit QC controls found.

"Coverage closure is a critical requirement in Integrated Chip (IC) development process and key metric for verification sign-off."

Benchmarks / Datasets

provisional (inferred)

Not extracted

No benchmark anchors detected.

"Coverage closure is a critical requirement in Integrated Chip (IC) development process and key metric for verification sign-off."

Reported Metrics

provisional (inferred)

Not extracted

No metric anchors detected.

"Coverage closure is a critical requirement in Integrated Chip (IC) development process and key metric for verification sign-off."

Rater Population

provisional (inferred)

Unknown

Rater source not explicitly reported.

"Coverage closure is a critical requirement in Integrated Chip (IC) development process and key metric for verification sign-off."

Human Feedback Details

This page is using abstract-level cues only right now. Treat the signals below as provisional.

  • Potential human-data signal: No explicit human-data keywords detected.
  • Potential benchmark anchors: No benchmark names detected in abstract.
  • Abstract highlights: 3 key sentence(s) extracted below.

Evaluation Details

Evaluation fields are inferred from the abstract only.

  • Potential evaluation modes: Automatic metrics
  • Potential metric signals: No metric keywords detected.
  • Confidence: Provisional (metadata-only fallback).

Research Brief

Metadata summary

Coverage closure is a critical requirement in Integrated Chip (IC) development process and key metric for verification sign-off.

Based on abstract + metadata only. Check the source paper before making high-confidence protocol decisions.

Key Takeaways

  • Coverage closure is a critical requirement in Integrated Chip (IC) development process and key metric for verification sign-off.
  • However, traditional exhaustive approaches often fail to achieve full coverage within project timelines.
  • This study presents an agentic AI-driven workflow that utilizes Large Language Model (LLM)-enabled Generative AI (GenAI) to automate coverage analysis for formal verification, identify coverage gaps, and generate the required formal properties.

Researcher Actions

  • Compare this paper against nearby papers in the same arXiv category before using it for protocol decisions.
  • Validate inferred eval signals (Automatic metrics) against the full paper.
  • Use related-paper links to find stronger protocol-specific references.

Caveats

  • Generated from abstract + metadata only; no PDF parsing.
  • Signals below are heuristic and may miss details reported outside the abstract.

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