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Rotifer Protocol
Rotifer Protocol

Posted on • Originally published at rotifer.dev

Compete in the Arena: Optimizing Fitness

#tutorial #advanced #arena #fitness

The Arena is where genes prove their worth. Every gene submitted to the Arena receives a fitness score (F(g)) and a safety score (V(g)) — two metrics that determine its rank, survival, and your developer reputation.

In this tutorial, you'll submit a gene, understand its scores, iteratively improve it, and watch it climb the rankings.

Prerequisites

  • Rotifer CLI installed (npm i -g @rotifer/playground)
  • A gene ready to submit (see Your First Gene in 5 Minutes)
  • (Optional) Cloud login for global Arena (rotifer login)

Step 1: Submit to the Arena

Let's submit a gene. If you don't have one, create a quick JSON formatter:

mkdir -p genes/json-fmt && cat > genes/json-fmt/index.ts << 'EOF'
export async function express(input: { code: string; indent?: number }) {
  const indent = input.indent ?? 2;
  try {
    const parsed = JSON.parse(input.code);
    return { formatted: JSON.stringify(parsed, null, indent), valid: true };
  } catch {
    return { formatted: input.code, valid: false };
  }
}
EOF

rotifer wrap json-fmt --domain code.format
rotifer compile json-fmt
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Now submit:

rotifer arena submit json-fmt
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Testing 'json-fmt' before submission...
✓ All tests passed

Submitting to Arena...
✓ Gene 'json-fmt' submitted

Rank:     #2 in code.format
F(g):     0.7834
V(g):     0.9100
Fidelity: Native
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Step 2: Understand the Scores

F(g) — Fitness Score

The fitness function combines multiple factors into a single 0.0–1.0 score:

Factor Weight What It Measures
Correctness 0.30 Does the output match the expected schema?
Performance 0.25 Execution time and resource efficiency
Reliability 0.20 Consistency across multiple runs
Fidelity bonus 0.15 Native > Hybrid > Wrapped
Diversity 0.10 Frequency-dependent selection (rare domains get a bonus)

V(g) — Safety Score

The safety validation is a hard gate with a separate 0.0–1.0 score:

Check Description
L0 constraints No filesystem, no network (unless Hybrid), no eval
Schema compliance Input/output match declared phenotype
Resource limits Fuel metering within bounds
IR integrity WASM custom sections intact

Admission Gate

Both scores must clear thresholds to enter the Arena:

  • F(g) >= 0.3 (default τ)
  • V(g) >= 0.7 (default V_min)

If either fails, the gene is rejected with diagnostic feedback.

Step 3: Diagnose a Low Score

Your gene got F(g) = 0.7834. Let's see why it didn't score higher. Check the detailed breakdown:

rotifer arena submit json-fmt --verbose
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Fitness Breakdown:
  Correctness:  0.92  ✓ All schema validations passed
  Performance:  0.58  ⚠ Avg execution: 12ms (target: <5ms)
  Reliability:  0.95  ✓ 19/20 runs consistent
  Fidelity:     0.70  Native WASM
  Diversity:    0.45  code.format has 3 competitors

  F(g) = 0.7834
  V(g) = 0.9100
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The bottleneck is Performance (0.58). The gene takes 12ms on average — the Arena wants under 5ms.

Step 4: Optimize for Performance

The JSON formatter uses JSON.parse + JSON.stringify which is fine, but we can optimize the hot path:

export async function express(input: { code: string; indent?: number }) {
  const indent = input.indent ?? 2;
  const code = input.code.trim();

  // Fast path: already formatted or empty
  if (!code || code === '{}' || code === '[]') {
    return { formatted: code, valid: code.length > 0 };
  }

  try {
    const parsed = JSON.parse(code);
    return { formatted: JSON.stringify(parsed, null, indent), valid: true };
  } catch {
    return { formatted: code, valid: false };
  }
}
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Recompile and resubmit:

rotifer compile json-fmt
rotifer arena submit json-fmt
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Fitness Breakdown:
  Correctness:  0.92
  Performance:  0.75  ↑ Avg execution: 6ms
  Reliability:  0.95
  Fidelity:     0.70
  Diversity:    0.45

  F(g) = 0.8234  ↑ (+0.04)
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Better — performance improved from 0.58 to 0.75.

Step 5: Improve Correctness

The correctness score is 0.92 — not perfect. This usually means edge cases aren't handled. Let's add robustness:

export async function express(input: { code: string; indent?: number }) {
  const indent = input.indent ?? 2;
  const code = (input.code ?? '').trim();

  if (!code) {
    return { formatted: '', valid: false };
  }

  try {
    const parsed = JSON.parse(code);
    return { formatted: JSON.stringify(parsed, null, indent), valid: true };
  } catch {
    // Try to salvage: strip trailing commas (common error)
    try {
      const cleaned = code.replace(/,\s*([\]}])/g, '$1');
      const parsed = JSON.parse(cleaned);
      return { formatted: JSON.stringify(parsed, null, indent), valid: true };
    } catch {
      return { formatted: code, valid: false };
    }
  }
}
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rotifer compile json-fmt
rotifer arena submit json-fmt
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  Correctness:  0.98  ↑ Edge cases handled
  Performance:  0.74
  Reliability:  0.97  ↑
  F(g) = 0.8567  ↑ (+0.033)
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Step 6: Watch the Rankings

Monitor your gene's position in real-time:

rotifer arena watch code.format
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Watching code.format rankings...

[14:32:01] json-fmt ↑ #2 → #1 (F: 0.82 → 0.86)
[14:32:04] No changes
[14:32:07] No changes

Press Ctrl+C to stop
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Step 7: Go Global with Cloud Arena

Local Arena is great for testing. To compete globally:

rotifer login
rotifer arena submit json-fmt --cloud
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✓ Submitted to Cloud Arena
Rank: #8 globally in code.format
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View global rankings:

rotifer arena list --cloud -d code.format
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Step 8: Check Your Reputation

Every Arena submission affects your developer reputation:

rotifer reputation --mine
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Developer Reputation:
  Arena Score:    0.82  (based on gene rankings)
  Usage Score:    0.45  (based on install counts)
  Stability:     0.91  (based on gene consistency)

  Overall:       0.73
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Reputation is a composite of your genes' Arena performance, how often others use them, and their reliability over time.

The Optimization Loop

The key takeaway is the iterative improvement cycle:

  1. Submit → get F(g) breakdown
  2. Diagnose → find the weakest factor
  3. Improve → targeted code changes
  4. Resubmit → verify improvement
  5. Watch → monitor competitive position

This is biological evolution in action — the Arena creates selection pressure, and your gene adapts.

Tips for High Fitness

  • Handle edge cases: null inputs, empty strings, malformed data — the Arena tests all of them
  • Minimize execution time: avoid unnecessary allocations, use fast paths for common cases
  • Be deterministic: same input should always produce same output (reliability score)
  • Choose the right fidelity: Native WASM gets a 0.70 base, Wrapped gets 0.45
  • Target niche domains: diversity factor rewards genes in less competitive domains

Deep Dive: See the Gene Standard specification for the complete fitness model, and the Arena CLI Reference for all command options.

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