Claude Architect Lab

Multi-Pass Review Architecture

Core

Design multi-instance and multi-pass review architectures · Difficulty 4/5

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multi-passreviewverificationattention-dilution
Prerequisites
Self-Critique Limitations & Independent Review

Multi-pass review splits large reviews into focused passes to avoid attention dilution and contradictory findings.

The Attention Dilution Problem

Reviewing a large codebase (14+ files) in one pass causes the model to miss issues in the middle of long contexts. Findings may also contradict each other when the model loses track of cross-file dependencies.

Multi-Pass Architecture

Pass 1 -- Per-File Local Analysis:

  • Each file gets focused, independent analysis
  • Catches local issues: bugs, style violations, security problems
  • Each pass has the model's full attention on one file

    • Pass 2 -- Cross-File Integration:

  • Separate pass focused on interactions between files
  • Catches: data flow issues, API contract mismatches, dependency problems
  • Uses summaries from Pass 1 as context

    • Benefits

  • No attention dilution: Each file gets full attention
  • No contradictions: Cross-file issues resolved in integration pass
  • Parallelizable: Per-file passes can run concurrently
  • Scalable: Works for any number of files

    • Combined with Independent Review

    For maximum quality:

  • Generator instance produces code
  • Per-file review passes catch local issues
  • Cross-file integration pass catches interactions
  • All review passes use independent instances (no generator context)

    • Key Takeaways

    • ✓Split large reviews into per-file local passes plus cross-file integration passes
    • ✓Per-file passes avoid attention dilution; integration passes catch cross-file issues
    • ✓Per-file passes are parallelizable for efficiency
    • ✓Combine multi-pass with independent instances for maximum review quality

    Related Concepts

    Self-Critique Limitations & Independent Review

    Self-review in the same context suffers from confirmation bias -- the model retains generation reasoning

    Batch Processing Strategy & API Selection

    Batch API saves 50% but has up to 24-hour processing with no latency SLA

    Test Yourself1 of 1

    Your team uses Claude Code to generate code suggestions, but subtle issues — performance optimizations that break edge cases, cleanups that change behavior unexpectedly — only surface when a different team member reviews the PR. Claude's reasoning during generation shows it considered these cases but concluded its approach was correct. Which approach directly addresses the root cause?