[Perf] Linux/arm64: 1 Regression on 6/16/2026 6:17:36 PM +00:00

### Run Information

Name | Value
-- | --
Architecture | arm64
OS | azurelinux 3.0
Queue | CobaltAzureLinux
Baseline | [98d5787a726f050c592e7c126c6ab62b1954288c](https://github.com/dotnet/runtime/commit/98d5787a726f050c592e7c126c6ab62b1954288c)
Compare | [4b211ba77ff3bfa4b7d2ba0c873e6bd337b6c6f0](https://github.com/dotnet/runtime/commit/4b211ba77ff3bfa4b7d2ba0c873e6bd337b6c6f0)
Diff | [Diff](https://github.com/dotnet/runtime/compare/98d5787a726f050c592e7c126c6ab62b1954288c...4b211ba77ff3bfa4b7d2ba0c873e6bd337b6c6f0)
Configs | CompilationMode:tiered, R2RType:r2r_interpreter, RunKind:micro

### Regressions in System.Memory.Span<Int32>

Benchmark | Baseline | Test | Test/Base | Test Quality | Edge Detector | Baseline IR | Compare IR | IR Ratio
-- | -- | -- | -- | -- | -- | -- | -- | --
|

• [SequenceEqual - Duration of single invocation]()
• 📝 - [Benchmark Source]()
• [ADX - Test Multi Config Graph]()

![graph]()
[Test Report]()

### Repro
General Docs link: https://github.com/dotnet/performance/blob/main/docs/benchmarking-workflow-dotnet-runtime.md

```cmd
git clone https://github.com/dotnet/performance.git
py .\performance\scripts\benchmarks_ci.py -f net8.0 --filter 'System.Memory.Span*'
```

### System.Memory.Span<Int32>.SequenceEqual(Size: 4)

#### ETL Files

#### Histogram

#### JIT Disasms

### Docs

[Profiling workflow for dotnet/runtime repository](https://github.com/dotnet/performance/blob/master/docs/profiling-workflow-dotnet-runtime.md)
[Benchmarking workflow for dotnet/runtime repository](https://github.com/dotnet/performance/blob/master/docs/benchmarking-workflow-dotnet-runtime.md)

Start with the System.Memory Span.cs benchmark source at the cited lines and read the benchmarking and profiling workflow documents. Run the supplied benchmarks_ci.py command for System.Memory.Span, then compare the baseline and test commits and inspect the linked report. Done means identifying and addressing the SequenceEqual regression, with the benchmark confirming the improvement.

Written by the indexing model from the issue text.