Reconstruct scientific figures as editable TikZ code

Do not train, fine-tune, calibrate or select on the validation or test split of any public dataset or benchmark; training splits and other public corpora are fine. Do not use SciTikZ-Bench or any mirror of it for anything; SciTikZ-230K is fine.

Server interface

POST /v1/chat/completions with model=submission, temperature=0, max_tokens=16384 and one user message holding a PNG image_url and the text prompt from validate.py. Return the LaTeX document in choices[0].message.content inside a closed ```latex fence; only the first fence is scored, and responses over 64,000 characters fail that figure.

Evaluation

All 611 test figures must finish generating within 7,200 seconds at 16 concurrent requests, or the run fails. Score: compilation rate × geometric mean of SigLIP, CLIP, exp(-LPIPS/0.5), SSIM, 1-DreamSim, exp(-TED/0.4) and CrystalBLEU. Output is compiled with pdfLaTeX (30 seconds, no shell escape or external files) and only the first page is scored. A request that fails after the shared retries, times out or answers in an undocumented shape is scored as an empty answer: it does not compile, and its code metrics are the empty program's. Metric details, render size limits and local setup are in /task/public/SCHEMA.md.

Artifacts

Record your starting open-weights checkpoint as base_model and its immutable revision as base_revision in architecture.json. Keep the download scripts or manifests showing where your training data came from.

Include architecture.json with base_model, base_revision, training_evidence_files and inference_evidence_files. The file lists use paths relative to output/ and include training source, serve.sh and model-loading code. Keep initialization/training logs with the submission as readable text or JSON. Name the source of every training, calibration and selection corpus (in architecture.json or a data manifest) and keep its download commands and logs with the submission. I will check for training-data provenance. Ship compiled dependencies as unmodified PyPI or PyTorch wheels, installed into /tmp at startup. Prewarmed JIT kernel caches (Triton, TorchInductor) may be included.

Submission

Put serve.sh, code, weights and dependencies in /workspace/output/ as regular files. Relative symlinks that resolve inside output/ are copied as the regular files they point to. However, absolute, escaping, dangling or cyclic symlinks fail the submission (for example, use python -m venv --copies, or vendor wheels). The /workspace/output folder is copied to /submission when being evaluated. serve.sh runs as an unprivileged user that can write to /submission, /tmp and the preinstalled Python environment (/opt/venv) and nowhere else; install vendored wheels there or under /submission. You should package dependencies for offline installation, since the evaluation server will not have internet access. The command bash /submission/serve.sh receives HOST=0.0.0.0, PORT=8000 and MODEL_NAME=submission. GET /health must return 200 when ready, within 300 seconds. Startup and inference run offline on one H100 SXM 80GB, with 8 CPU cores and 80 GiB RAM. The serving image is Ubuntu 24.04 with the CUDA 12.8 runtime libraries (no CUDA compiler), Python 3.12 in /opt/venv first on PATH (pip, numpy, pillow, safetensors, httpx preinstalled), gcc and Python headers. PyTorch is not preinstalled: vendor cp312 manylinux wheels (and their dependencies). Every request must answer HTTP 200 with the documented JSON. A request that fails (non-200, connection error, invalid JSON) is retried twice on the same payload. If it still fails, or answers in an undocumented shape, you get no credit for that item. Up to one evaluation request in twenty may fail that way, but more than that gives you no credit for the entire run.

Development

The development machine is CPU-only. Use spin --help to rent compute and access model APIs, and spin budget to check how much allowance you have left. The API key your coding harness was started with is for the harness alone. Do not use the API key in your own code, scripts, curl or Spin machines, copy it anywhere, or start extra harness sessions as workers; AI model calls you make yourself must go through spin and spend your allowance. I will check your session transcript for this.

To check your solution locally, run python /task/validate.py --server-url URL. See /task/public for examples and supporting files.

You can check your solution on the same infrastructure I will evaluate it on using frontier selfcheck, which copies /workspace/output/, starts it as the evaluation does, sends public examples through the same response checks and measures the speed requirements on public inputs. Its report gives the stage reached, the digest of the files it checked, the GPU, the startup time and each requirement's measured value and margin, and the end of the server log. You can check your solution four times.

A check usually takes 10-40 minutes (frontier selfcheck status shows this task's limit) and runs while you keep working. See frontier selfcheck --help for more details.

When your solution is final, hand it in with frontier submit: it shows what /workspace/output/ holds and asks you to confirm with frontier submit --confirm, which submits the folder as it is and ends your session. Without a submission, /workspace/output/ is graded as it is when your session ends. See frontier submit --help.

Your session ends the moment you finish a turn without a tool call: background commands, scheduled wake-ups and reminders will not bring you back, and anything not yet in /workspace/output/ is lost. Wait for long jobs with foreground commands under the tool time limit (for example sleep 600; tail -n 3 train.log, repeated) and stop only when the final files are in place.