/gaia-architecture-comparison

name: gaia-architecture-comparison
description: Side-by-side comparison of ruflo vs HAL vs other GAIA harnesses — capability gaps, design decisions, and improvement roadmap
argument-hint: "[--focus=tools|routing|memory|cost]"
allowed-tools: Bash Read mcp__plugin_ruflo-core_ruflo__memory_search mcp__plugin_ruflo-core_ruflo__memory_store
disable-model-invocation: true

GAIA Architecture Comparison Skill

Compare ruflo's GAIA benchmark harness against the Princeton HAL reference implementation and other open-source harnesses to understand capability gaps and prioritize improvements.

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)

HAL uses a similar loop but with:

• 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

| Dimension | ruflo | HAL reference | Gap | |-----------|-------|--------------|-----| | Question count | 53 (partial L1) | 300 (full L1) | Use `--limit 165` for full L1 | | Web search | DuckDuckGo / Google CSE | BrowserBase live | Add Playwright or Browserless | | Code execution | python_exec stub | Real Jupyter kernel | Implement real sandbox | | Image OCR | image_describe (Gemini) | GPT-4V / Gemini | Functionally equivalent | | File handling | file_read | Full PDF/XLSX/ZIP parser | Expand file_read | | Self-consistency | voting.ts (Track A) | Not in reference | ruflo advantage | | Hardness routing | predictor.ts (Track Q) | Not in reference | ruflo advantage | | Memory | AgentDB HNSW | None | ruflo advantage | | Pass-rate L1 | ~20.8% (iter 23) | 74.6% (HAL Sonnet 4.5) | ~54 pp gap |

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)

4. **Structured file parsing** — PDF, XLSX, and ZIP attachments require dedicated parsers. `file_read` currently handles plain text and images only.

5. **Turn budget** — 12 turns may be insufficient for complex multi-step questions. HAL uses up to 20 turns for L3.

6. **System prompt tuning** — HAL's system prompt is more elaborate and explicitly instructs the model to use tools before answering.

ruflo advantages

7. **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.

8. **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.

9. **AgentDB memory** — storing patterns across runs enables the agent to recall successful strategies for similar question types.

Improvement roadmap

| Priority | Change | Expected Lift | Effort | |----------|--------|--------------|--------| | P0 | Real python_exec sandbox (E2B) | +15-25 pp | High | | P0 | Full 165-Q L1 evaluation | Accurate baseline | Low | | P1 | Playwright-based web_browse | +5-10 pp | Medium | | P1 | PDF/XLSX file parser | +3-8 pp | Medium | | P2 | Increase max-turns to 20 for L2/L3 | +2-5 pp | Low | | P2 | System prompt tuning (iter 30 research) | +2-5 pp | Low | | P3 | Google Grounding via Gemini (iter 32) | +3-7 pp | Medium | | P3 | Multi-provider routing (Gemini Flash for cheap Q's) | Cost reduction | Medium |

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."