# EquivSVA: A Formally Verified Dataset of Behavioral Assertions Across Equivalent RTL Implementations

Source: [arXiv](https://arxiv.org/abs/2609.26751v1)  
Feed7 permalink: https://feed7.dev/p/2609-26751v1-0om13uz  
Published: 2026-09-22T17:33:09.000Z  
Trust: Needs Review (needs_review)

## Why Included

EquivSVA tests whether generated hardware assertions describe interface behavior rather than quirks of one RTL implementation, exposing robustness gaps hidden by single-implementation evals.

## Source Summary

EquivSVA organizes **120 behavior families** into four equivalent RTL implementations each, totaling **480 implementations**, 914 gold properties, and 360 mutants. Every family passes a 17-job formal-validation suite.

## Practical Implication

Builders evaluating assertion agents should split by behavior family and test outputs across structurally different implementations. In the Qwen2.5-Coder-7B-Instruct case study, only **93 of 293** interface-only properties were formally sound.

## Agent-Ready Context

EquivSVA organizes **120 behavior families** into four equivalent RTL implementations each, totaling **480 implementations**, 914 gold properties, and 360 mutants. Every family passes a 17-job formal-validation suite.

Builders evaluating assertion agents should split by behavior family and test outputs across structurally different implementations. In the Qwen2.5-Coder-7B-Instruct case study, only **93 of 293** interface-only properties were formally sound.

This is a specialized hardware-verification dataset, not a broad coding-agent benchmark. Sound-property counts varied across equivalent implementations in **14 of 24** test families, but the paper reports only one model demonstration.

## Connected Context

Feed7 judgment across 856 accumulated Signals:

This adds formally verified, representation-varied evaluation to the benchmark-integrity toolkit. Family-level splits reduce leakage between equivalent implementations, while cross-implementation testing reveals whether assertions capture behavior rather than syntax. The single-model result exposes substantial soundness and robustness gaps but does not establish how other assertion agents perform.

- [When LLM Decompilers Recompile More and Preserve Less](https://feed7.dev/p/2609-05370v1-03wuqdv) — Both require semantic equivalence checks beyond surface success: EquivSVA formally validates assertions across equivalent RTL, while the decompiler study differentially executes reconstructed code against the original.
- [Computer Use at the Edge of the Statistical Precipice — Pierluca D'Oro, Programma Labs](https://feed7.dev/p/computer-use-at-the-edge-of-the-statistical-precipice-pierluca-d-oro-pro-16136hs) — Equivalent RTL implementations provide controlled environment variation, implementing the broader recommendation to vary benchmark state so fixed structural patterns cannot stand in for adaptation.
- [When Will The Benchmaxxing Plague End? — Nick Heiner, Surge AI](https://feed7.dev/p/when-will-the-benchmaxxing-plague-end-nick-heiner-surge-ai-178gqcg) — The family-level split and formal validation directly address two benchmark risks highlighted there: leakage between related cases and weak verifiers that reward shortcuts.
- [Metrics Failure in LLM-Based Code Vulnerability Repair: An Empirical Study and a Change-Aware Screen](https://feed7.dev/p/2609-26749v1-1fj6jga) — Both show that convenient proxy metrics can misstate correctness and replace them with domain-grounded validation, formal soundness here and execution-grounded security checks for repair.

## Context Map

- Layer: benchmark
- Domains: coding
- Topics: agent-evals, benchmark-integrity, agent-reliability

## Uncertainty

- This is a specialized hardware-verification dataset, not a broad coding-agent benchmark. Sound-property counts varied across equivalent implementations in **14 of 24** test families, but the paper reports only one model demonstration.

## Agent Instruction

Use this item as source-backed context. Do not invent claims beyond the linked source. If this item conflicts with another source, call out the conflict.