| # AI-NativeBench: Processed Results & Analysis Artifacts |
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| This directory contains **processed experiment outputs**, **aggregated statistics**, and **analysis/visualization scripts** for the paper **"AI-NativeBench: An Open-Source White-Box Agentic Benchmark Suite for AI-Native Systems"**. |
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| Compared with the raw dataset (see `README-RAW.md`), this `RESULTS/` folder additionally includes: |
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| - **Derived artifacts** such as `execution_path.md` (extracted trace trees), per-run metadata, and task outputs. |
| - **Aggregated tables** (CSV) and **paper-ready figures** (PDF) for RQ1–RQ3. |
| - **Scripts** to reproduce the aggregation and plots. |
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|
| ## Repository layout |
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| - **Model-level run outputs** (per-model folders): |
| - `GPT-5/` |
| - `GPT-4o-mini/` |
| - `DeepSeek-V3-1/` |
| - `DeepSeek-R1/` |
| - `Gemini-2.5-flash/` |
| - `Gemini-2.5-flash-nothinking/` |
| - `Qwen3-235b/` |
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| - **RQ-level analysis artifacts**: |
| - `RQ1/`: trajectory/success/retry evaluations and radar charts |
| - `RQ2/`: agent-time vs. LLM/tool share breakdowns + plots |
| - `RQ3/`: performance summaries (time/tokens), comparisons, and violin plots |
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|
| - **Trace processing utilities**: |
| - `extract_trace_tree.py`: generates `execution_path.md` from `langfuse_trace.json` |
| - `batch_extract_trees.sh`: batch-generate `execution_path.md` for all runs |
| - `README-extract_trace_tree.md`: detailed usage and output format |
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| ## Model folders (per-run outputs) |
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| Each model folder contains multiple **application/architecture** subfolders (e.g., `BookWriter-MCP`, `SQLAssistant-A2A`). |
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| ### Architecture naming conventions |
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| - **No suffix**: pure framework baseline (e.g., `EmailResponder/`, `GameBuilder/`, `MarkdownValidator/`) |
| - **`*-A2A`**: A2A protocol variant |
| - **`*-H_A2A`**: hard-coded / heterogeneous A2A baseline (in some plots/scripts, this may be treated as an A2A-mix / H-A2A variant) |
| - **`*-MCP`**: tool calling via MCP |
| |
| ### Typical per-run structure |
| |
| Under each application folder, `test_results/` stores multiple runs (commonly `run_*/` or `session_*/` directories). A typical run contains: |
| |
| - **`langfuse_trace.json`**: raw distributed tracing data for the run |
| - **`execution_path.md`**: extracted execution-path tree (generated by `extract_trace_tree.py`) |
| - **`metadata.json`**: run metadata |
| - **Task outputs**: generated artifacts such as books/chapters, reports, validation results, logs, etc. |
| |
| Example: |
| |
| ```text |
| RESULTS/ |
| ├── GPT-5/ |
| │ └── BookWriter-MCP/ |
| │ └── test_results/ |
| │ └── run_YYYYMMDD_HHMMSS/ |
| │ ├── langfuse_trace.json |
| │ ├── execution_path.md |
| │ ├── metadata.json |
| │ └── ... (task outputs) |
| ``` |
| |
| ## RQ1: trajectory/success/retry evaluation |
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| Location: `RQ1/` |
| |
| - **What it contains**: |
| - per-task evaluation scripts and reference trajectories (YAML) |
| - aggregated CSV outputs (e.g., trajectory metrics, success rates, retry summaries) |
| - radar charts under `RQ1/RadarCharts/` |
| |
| - **Entry points**: |
| - per-task scripts (run from a specific `RQ1/<Task-Variant>/` folder): |
| - `evaluate_trajectory.py` |
| - `evaluate_success.py` |
| - `analyze_retry_patterns.py` |
| - plot generator (run from `RQ1/`): |
| - `generate_radar_charts.py` |
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| See `RQ1/README.md` for details. |
| |
| ## RQ2: agent-time / LLM-share breakdown |
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| Location: `RQ2/` |
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| - **What it contains**: |
| - per-task aggregation outputs (CSV) and mapping files (`agent_map.md`, `FRAMEWORK_map.yaml`) |
| - consolidated summary tables (e.g., `llm_share_summary.csv`) |
| - paper-ready figures (PDF), including heatmaps and bar charts |
| |
| - **Typical reproduction flow** (from `RQ2/`): |
| - `python aggregate_llm_share.py` |
| - `python plot_llm_share_heatmap.py` |
| - `python plot_model_overhead_bars_a2a.py` |
| - `python plot_model_overhead_bars_mix.py` |
| - `python plot_framework_overhead_ratio_mix_vs_a2a.py` |
| - `python plot_agent_time_share_bars_unified_y_by_series.py` |
| - `python plot_total_classified_ecdf.py` |
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| See `RQ2/README.md` for details. |
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| ## RQ3: performance analysis (time/tokens) |
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| Location: `RQ3/` |
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| - **What it contains**: |
| - parsers/aggregators that scan `execution_path.md` across all runs |
| - performance reports (CSV) under `RQ3/performance_reports/` |
| - comparison reports under `RQ3/agent_time_reports/` |
| - violin plots (PDF) under `RQ3/Violin/` |
| |
| - **Typical commands** (from repository root): |
| - `python RQ3/analyze_performance.py` |
| - `python RQ3/analyze_agent_time_comparisons.py` |
| - `python RQ3/generate_summary.py` |
| - `python RQ3/plot_total_tokens_violin.py` |
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| See `RQ3/README.md` for details. |
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| ## Trace extraction: generating `execution_path.md` |
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| `execution_path.md` is a key intermediate artifact consumed by RQ analyses. |
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| - **Single file**: |
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| ```bash |
| python3 extract_trace_tree.py <path/to/langfuse_trace.json> |
| ``` |
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| - **Batch** (scan the entire `RESULTS/` tree and generate `execution_path.md` next to each trace): |
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| ```bash |
| ./batch_extract_trees.sh |
| ``` |
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| See `README-extract_trace_tree.md` for the output format and filtering rules. |
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| ## Requirements |
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| - **Trace extraction** (`extract_trace_tree.py`): |
| - Python 3 |
| - standard library only |
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| - **RQ analyses and plotting** (`RQ1/`, `RQ2/`, `RQ3/`): |
| - Python 3 |
| - common scientific stack (e.g., `numpy`, `pandas`, `matplotlib`) |
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| ## Notes |
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| - Some scripts assume the default directory layout under `RESULTS/`. If you rename/move folders, update the corresponding path configuration inside the scripts. |
| - Trace files (`langfuse_trace.json`) can be large; many PDFs/CSVs in `RQ1/`, `RQ2/`, and `RQ3/` are **generated analysis artifacts**. |
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