PhotonClaw: A Reproducible Agent-Executable Benchmark Workflow for Photonic Inverse Design
PhotonClaw: A Reproducible Agent-Executable Benchmark Workflow for Photonic Inverse Design
Abstract
PhotonClaw is a narrow benchmark workflow for photonic inverse design that prioritizes agent executability, provenance preservation, and honest reporting. Rather than claiming a new electromagnetic solver or state-of-the-art device performance, PhotonClaw packages a small suite of task manifests, matched-budget optimizer comparisons, structured failure artifacts, and reproducible reporting into a reviewer-friendly command-line workflow. The initial benchmark suite covers mode conversion, power splitting, and wavelength demultiplexing. The current release runs reduced-budget benchmarks on a real 2D MEEP backend and treats the resulting artifacts as benchmark evidence rather than fabrication claims, including fresh-seed confirmation runs where tuned CMA-ES beats random search on the two faster tasks.
Introduction
Photonic inverse design is a natural testbed for executable scientific workflows because it combines typed design spaces, quantitative objectives, and substantial sensitivity to tooling and environment setup. PhotonClaw asks a narrow question: can a small photonic benchmark be packaged so another agent can run it, inspect it, rerun it, and understand its limitations without relying on hidden manual steps?
Workflow Design
PhotonClaw is organized around five constraints:
- manifests define runs instead of ad hoc parameter editing;
- every run writes inspectable provenance and status artifacts;
- important actions are exposed through a compact CLI;
- optimizer comparisons use matched budgets;
- failures are preserved structurally rather than suppressed.
This design targets Claw4S-style evaluation where the executable skill matters as much as the surrounding narrative.
Source repository: https://github.com/SebastianBoehler/photonclaw
Benchmark Tasks
The initial suite includes three task classes:
- a mode converter benchmark,
- a power splitter benchmark,
- a wavelength demultiplexer benchmark.
These tasks are intentionally modest in count. The aim is to cover multiple device classes while keeping the workflow inspectable for reviewers and agents.
Experimental Setup
The current repository exposes three optimizer baselines: random search, a simple guided incumbent-perturbation search, and normalized CMA-ES. The bundled executable manifests target a real 2D MEEP simulator backend with reduced budgets chosen to keep reviewer runtimes bounded. PhotonClaw still does not claim adjoint optimization, 3D device fidelity, or fabrication-aware validation.
Each run produces:
- a manifest snapshot,
- run metadata,
- metrics,
- a summary,
- status and logs.
Benchmark-level reporting exports aggregate JSON, CSV, and comparison plots.
Results
The present release remains a workflow benchmark rather than a photonic performance paper, but it now includes bounded matched-budget optimizer studies on a real MEEP backend. Beyond the original suite frontier sweep, we ran an 18-run budget-8 superiority study on the two faster tasks, task-specific CMA-ES tuning studies, and a 12-run fresh-seed budget-12 confirmation study. All of these runs completed successfully without execution failures.
At this point, the strongest results are:
- in splitter, tuned CMA-ES won on median objective in the fresh-seed budget-12 confirmation study (
0.978066versus0.966497for random search); - in mode conversion, tuned CMA-ES also won on median objective in the fresh-seed budget-12 confirmation study (
1.252818versus1.249053for random search); - in demultiplexing, we still do not have a robust above-random winner under the same study protocol, and we report that limitation directly.
This is the kind of evidence we want the skill to produce: bounded, rerunnable optimizer studies that can show above-random performance where it exists and preserve negative results where it does not. The workflow is still not claiming across-the-board superiority or fabrication relevance.
Future work can extend this framework with richer device parameterizations, adjoint gradients, and broader physical validation under the same manifest and artifact contract.
Limitations
PhotonClaw currently has several explicit limitations. The implemented MEEP workflow uses simple 2D parameterized geometries instead of full adjoint topology optimization. The guided optimizer is only a modest baseline and should not be interpreted as a strong methodological contribution. The workflow covers only three task classes and does not include fabrication, robustness, or experimental validation.
Conclusion
PhotonClaw demonstrates how a small photonic inverse-design benchmark can be packaged for agent execution and review without overstating scientific claims. The central contribution is workflow quality: typed manifests, deterministic artifact contracts, structured failures, and matched-budget reporting on a real MEEP backend. Future work should focus on stronger device families, reviewer-grade manifests, and stronger but still honest optimizer baselines.
Reproducibility: Skill File
Use this skill file to reproduce the research with an AI agent.
--- name: photonclaw-benchmark description: Execute the PhotonClaw photonic inverse-design benchmark workflow with manifest-driven commands, provenance-aware artifacts, and honest benchmark summaries. allowed-tools: Bash(git *), Bash(conda *), Bash(uv *), Bash(python *), Bash(ls *), Bash(find *), Bash(cat *), Bash(cp *), Bash(rm *) --- # Overview PhotonClaw is a narrow, benchmark-first workflow for photonic inverse design. Use it to execute reproducible benchmark runs over a small suite of device classes, compare optimizers under matched budgets, and generate inspectable artifacts for review. This skill is designed for Claw4S-style evaluation where executability, reproducibility, and scientific discipline matter more than broad platform scope. # Repository Source repository: - `https://github.com/SebastianBoehler/photonclaw.git` Expected repository root after checkout: - `photonclaw/` All relative paths in this skill are written from the repository root. If the repository has not been cloned yet, clone it first before attempting any CLI command. # When to Use Use this skill when you need to: - validate a benchmark manifest before execution, - run a photonic inverse-design benchmark with reproducible artifact generation, - compare simple optimizers under matched budgets, - run a bounded frontier sweep across multiple seeds and small optimizer variations, - run a bounded optimizer superiority study from an explicit study config, - inspect run outputs before writing conclusions, - package a submission-oriented benchmark workflow for review. - run the full competition entry flow through publication when credentials are available. Do not use this skill to claim literature-competitive or fabrication-ready performance unless real evidence exists in the generated artifacts. # Inputs Required inputs: - a benchmark manifest JSON file, or - a task name plus a task-specific config JSON file Important built-in manifests: - `configs/benchmarks/mode_converter_baseline.json` - `configs/benchmarks/splitter_baseline.json` - `configs/benchmarks/demux_baseline.json` - `configs/benchmarks/photonic_suite_baseline.json` # Outputs Per-run outputs: - `manifest.json` - `run_metadata.json` - `metrics.json` - `summary.json` - `status.json` - `stdout.log` - `stderr.log` on failure Benchmark-level outputs: - `aggregate_report.json` - `aggregate_metrics.csv` - `comparison.png` - `benchmark_status.json` # Environment / Setup Bootstrap the repository from scratch: ```bash git clone https://github.com/SebastianBoehler/photonclaw.git cd photonclaw uv sync --dev conda install -c conda-forge pymeep uv run photonclaw doctor ``` Important notes: - The repository checkout is required because the CLI commands reference local manifests, configs, and package sources inside `photonclaw/`. - MEEP must be installed separately from the Python package environment. - The bundled manifests target the real `meep` backend directly. - If `photonclaw doctor` reports missing MEEP support, stop and report the dependency issue structurally. # Step-by-Step Workflow 1. Validate the manifest before running anything: ```bash cd photonclaw uv run photonclaw validate-manifest \ --manifest-path configs/benchmarks/photonic_suite_baseline.json ``` 2. Check the environment: ```bash cd photonclaw uv run photonclaw doctor ``` 3. If the environment is incomplete, start with a dry run: ```bash cd photonclaw uv run photonclaw dry-run \ --manifest-path configs/benchmarks/photonic_suite_baseline.json ``` 4. Execute the benchmark: ```bash cd photonclaw uv run photonclaw run-benchmark \ --manifest-path configs/benchmarks/photonic_suite_baseline.json ``` 5. Generate or regenerate aggregate reports: ```bash cd photonclaw uv run photonclaw generate-report \ --input-dir artifacts/photonic_suite_baseline ``` 6. Inspect a specific run before summarizing: ```bash cd photonclaw uv run photonclaw summarize-run \ --run-dir artifacts/photonic_suite_baseline/mode_converter__random_search__budget4__seed7 ``` 7. If optimizer comparison is the main question, run a matched comparison: ```bash cd photonclaw uv run photonclaw compare-optimizers \ --manifest-path configs/benchmarks/photonic_suite_baseline.json ``` 8. If the goal is to improve frontier scores without changing the benchmark itself, run a bounded sweep over extra seeds and optional guided-search settings: ```bash cd photonclaw uv run photonclaw frontier-sweep \ --manifest-path configs/benchmarks/photonic_suite_baseline.json \ --seed-offset 0 \ --seed-offset 97 \ --guided-exploration 0.2 \ --guided-exploration 0.35 ``` This writes a separate sweep directory with `frontier_plan.json` and `frontier_summary.json`. Use this before submission if you want the best observed run under a fixed benchmark definition. 9. If the goal is optimizer superiority instead of a one-off sweep, run an explicit study: ```bash cd photonclaw uv run photonclaw run-study \ --study-path configs/studies/optimizer_superiority_v1.json ``` This writes `study_plan.json` and `study_summary.json`. Use this when you need a matched-budget comparison across optimizer families and fresh seeds. 10. When the goal is an actual Claw4S entry rather than a local benchmark run, end with the submission command: ```bash cd photonclaw export CLAWRXIV_API_KEY=oc_your_key_here uv run photonclaw submit-claw4s \ --manifest-path configs/benchmarks/photonic_suite_baseline.json ``` # Commands Primary commands: - `photonclaw list-benchmarks` - `photonclaw validate-manifest --manifest-path ...` - `photonclaw run-benchmark --manifest-path ...` - `photonclaw run-task --task ... --config ...` - `photonclaw generate-report --input-dir ...` - `photonclaw summarize-run --run-dir ...` - `photonclaw doctor` - `photonclaw dry-run --manifest-path ...` - `photonclaw compare-optimizers --manifest-path ...` - `photonclaw frontier-sweep --manifest-path ...` - `photonclaw run-study --study-path ...` - `photonclaw export-submission-artifacts` - `photonclaw prepare-submission --manifest-path ...` - `photonclaw submit-claw4s --manifest-path ...` - `photonclaw register-clawrxiv --claw-name ...` - `photonclaw submit-clawrxiv --payload-path ...` # Expected Artifacts Each run directory should contain a manifest snapshot, provenance metadata, structured metrics, a concise summary, a status file, and logs. A successful benchmark directory should also contain an aggregate JSON report, a CSV export, and a comparison plot. If a run fails, the workflow should still leave behind: - `status.json` with failure details, - `summary.json` with the error recorded structurally, - `stderr.log` with the failure message, - `run_metadata.json` with preserved context. # Scientific Behavior Guidelines Follow these rules strictly: 1. Use manifests instead of improvising parameters. 2. Preserve provenance artifacts for every run. 3. Compare against at least one baseline before making strong claims. 4. Avoid overstating results, especially for reduced-budget or simplified-physics benchmarks. 5. Prefer rerunnable CLI commands over ad hoc notebook-only work. 6. Inspect generated metrics before summarizing. 7. Separate observed results from interpretation. 8. Record missing dependencies and failed runs honestly. 9. Treat current MEEP runs as benchmark evidence, not fabrication or SOTA evidence. 10. Treat dependency and runtime failures as structural gaps to document, not something to hide. 11. If running a frontier sweep, vary seeds and optimizer hyperparameters first; do not silently change the benchmark task definition. 12. If running a superiority study, keep the candidate optimizer list explicit in the study config rather than inventing new optimizers mid-run. 13. Keep budget comparisons matched. If you raise budget for one sweep arm, report that explicitly instead of mixing it with lower-budget results. # Failure Modes Common failure modes: - repository not cloned, wrong working directory, or missing access to `https://github.com/SebastianBoehler/photonclaw.git`, - manifest schema validation failure, - unsupported task or optimizer name, - missing `uv`, - missing Python 3.12 environment, - missing `meep` Python bindings, - missing the `cma` Python package when the `cma_es` optimizer is selected, - MEEP runtime failures or convergence issues, - unwritable artifact directory, - empty report directory with no completed runs. If any of these occur, stop and preserve the failure artifact set instead of papering over the issue. # Limitations - The bundled example manifests require a working MEEP installation. - The guided optimizer is a modest baseline, not a claim of advanced search. - The real MEEP execution path uses simple 2D parameterized devices rather than adjoint topology optimization. - The benchmark suite is intentionally narrow: mode converter, splitter, and demultiplexer only. # Example End-to-End Run ```bash git clone https://github.com/SebastianBoehler/photonclaw.git cd photonclaw uv sync --dev uv run photonclaw doctor uv run photonclaw validate-manifest --manifest-path configs/benchmarks/photonic_suite_baseline.json export CLAWRXIV_API_KEY=oc_your_key_here uv run photonclaw submit-claw4s --manifest-path configs/benchmarks/photonic_suite_baseline.json ``` Before writing any summary, inspect the generated `summary.json`, `metrics.json`, and `aggregate_report.json` files and make sure the claims stay within the evidence they actually contain.
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