GAIA Architecture Comparison Skill

When to use

• Planning the next iteration of GAIA work
• Evaluating which architectural change has the highest pass-rate ROI
• Onboarding a new contributor to the benchmark codebase

Architecture overview

ruflo harness (current)

gaia-bench run
  └─ gaia-loader.ts      — HF dataset download + cache
  └─ gaia-agent.ts       — multi-turn Anthropic Messages loop
       └─ gaia-tools/    — web_search, file_read, web_browse,
                           image_describe, python_exec
  └─ gaia-voting.ts      — Track A self-consistency (N attempts → majority vote)
  └─ gaia-hardness/      — Track Q difficulty predictor (ADR-136)
  └─ gaia-judge.ts       — two-stage LLM-as-judge scorer

HAL reference (Princeton)

• OpenAI function calling as the tool interface
• BrowserBase / Playwright for real browser automation
• Code interpreter sandbox (Jupyter kernel)
• Larger token budget per turn (4096+)
• Full 300-question evaluation set

Key differences

--limit 165

Gap analysis

Primary gaps (high impact)

1. Real code execution — many L2/L3 questions require running Python to compute a numerical answer. The current

   python_exec

   tool is a stub. Implementing a real sandbox (E2B, Pyodide, or subprocess) is the single highest-ROI change.
2. Full question set — running 53/300 L1 questions underestimates true pass-rate because the first 53 skew easier. Run

   --limit 165

   (full L1) for a comparable HAL score.
3. Real browser —

   web_browse

   currently fetches raw HTML. Replacing it with Playwright/Browserless for JavaScript-rendered pages would unlock many web navigation questions.

Secondary gaps (medium impact)

1. Structured file parsing — PDF, XLSX, and ZIP attachments require dedicated parsers.

   file_read

   currently handles plain text and images only.
2. Turn budget — 12 turns may be insufficient for complex multi-step questions. HAL uses up to 20 turns for L3.
3. System prompt tuning — HAL's system prompt is more elaborate and explicitly instructs the model to use tools before answering.

ruflo advantages

1. Self-consistency voting (Track A) — running N attempts per question and taking the majority answer reduces variance on borderline questions. HAL does not implement this.
2. Hardness routing (Track Q) — routing each question to an appropriate model and turn budget based on predicted difficulty. This reduces cost on easy questions while providing more resources for hard ones.
3. AgentDB memory — storing patterns across runs enables the agent to recall successful strategies for similar question types.

Improvement roadmap

Loading context from past research

npx @claude-flow/cli@latest memory search \
  --namespace gaia-patterns \
  --query "architecture comparison HAL benchmark"

Storing comparison findings

npx @claude-flow/cli@latest memory store \
  --namespace gaia-patterns \
  --key "architecture-comparison-$(date +%Y%m%d)" \
  --value "HAL gap: 54pp. Primary: python_exec stub. Secondary: browser, file parsing."