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Challenge

LlamaIndex Infrastructure Noise & Benchmark Variance Analyzer

Infrastructure noise such as noisy neighbors and network jitter obscures true performance improvements in agentic benchmarks. Build a LlamaIndex RAG and workflow framework that indexes benchmark execution logs, computes statistical variance confidence intervals, and isolates true algorithmic progress from hardware noise.

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Challenge brief

What you are building

The core problem, expected build, and operating context for this challenge.

Construct a LlamaIndex RAG and statistical workflow engine to quantify hardware infrastructure noise and filter benchmark variance.

Delivery guide

How work is evaluated

Evaluation

Evaluates noise reduction and statistical significance detection across benchmark test runs.

Datasets

Challenge datasets

Public datasets, plus the private datasets you have access to.

Evaluation rubric

How submissions are scored

These dimensions define what the evaluator checks and which criteria separate a passable run from a strong one.

Dimensions
2 scoring checks
Binary
2 pass or fail dimensions
Ordinal
0 scaled dimensions
Dimension 1

significance_test_accuracy

Ensures correct determination of statistical significance.

Binary check

This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.

Dimension 2

noise_identification_f1

F1-score for identifying specific root causes of infrastructure noise • target: 0.94 • range: 0.85-1

Binary check

This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.

Learning goals

What you should walk away with

  • Index benchmark execution logs using LlamaIndex vector indices and document parsers

  • Build a statistical analysis workflow to separate variance from model score improvements

  • Calculate 95% confidence intervals and p-values for agentic evaluation runs

  • Identify hardware noise factors such as CPU throttling and background OS tasks

Resources and assets

Reference links and supporting material

Dataset notes

Execution benchmark run logs containing pass rates, host CPU steal times, and disk IOPS counters.

Challenge setup

Evaluates noise reduction and statistical significance detection across benchmark test runs.

Challenge input

JSON containing baseline_scores, treatment_scores, host_metrics_logs

LlamaIndex

Framework for indexing document logs and statistical analysis tools

Evaluated output

JSON containing is_statistically_significant, p_value, noise_variance_ratio, identified_noise_source

Evaluation checks
  • Ensures correct determination of statistical significance.
  • F1-score for identifying specific root causes of infrastructure noise • target: 0.94 • range: 0.85-1
Configured evaluation evidence
  • Benchmark: SWE-bench Infrastructure Variance Suite
  • Noise Identification F1 target: 0.94
  • 1 public reference case
Configured runtime
  • Python execution harness
  • Python sandbox (unavailable on Versalist)

Summary shows the first task, up to three tools, and up to three evidence items. Open the technical recipe to view all configured items.

View technical recipe

Configured tools

Action Space
  • Llama Index · Required
  • LlamaIndex · Optional
  • Llama 3.2 · Optional

Evaluation contract

  • significance_test_accuracy · Weight 1
  • noise_identification_f1 · Weight 1

Metrics

  • noise_identification_f1 · Target 0.94

Benchmarks

  • SWE-bench Infrastructure Variance Suite
  • general

Reference cases

  • 1 public reference case

Workflow tasks

  1. Task 1

    Input: JSON containing baseline_scores, treatment_scores, host_metrics_logs

    Output: JSON containing is_statistically_significant, p_value, noise_variance_ratio, identified_noise_source

    Execution harness: Python

Evaluation tools

  • LlamaIndex · RAG Framework

Recipe freeze marker

No recipe freeze time is recorded.

Frequently Asked Questions about LlamaIndex Infrastructure Noise & Benchmark Variance Analyzer