Trial · svg-generation-4hvwuc

SVG generation

Qwen3.8 MaxxhighHarness Claude Code 2.1.295

Wallet

Score: 0.0
Wallet spend: $167.80 of $200
Model cost: $17.17
Agent time: 7h 10m
Steps: 360
Tokens in / out: 37.0M / 344k
Self-checks: 0 passed of 1 used
Rank at $200 wallet: 6th of 14

Loading trajectory…

pass3 of 3 checks passed · reviewed by claude-opus-5-5 · read 360 trajectory steps

After grading, a reviewer model reads the submission's files and the agent's trajectory and checks each of the task's rules. A failed check makes the score 0. Step numbers link into the trajectory.

✓finetuned from gpt oss 20bsrc/run_pipeline.sh downloads openai/gpt-oss-20b at the pinned revision 6cee5e81. It then dequantizes MXFP4 to bf16 at /

src/run_pipeline.sh downloads openai/gpt-oss-20b at the pinned revision 6cee5e81. It then dequantizes MXFP4 to bf16 at /ram/base_bf16, and the dequant check showed this was bit-exact. Next it runs src/train.py, a real DeepSpeed ZeRO-3 full-parameter SFT loop with backward and engine.step, for 929 optimizer steps. In logs/train_log_rank0.jsonl the loss falls from about 3.3 to about 0.6. src/save_final.py reshards the trained weights and asserts that they differ from the base. src/finish.sh and src/dl_final.py carry that checkpoint through R2 into the shipped model directory. The shipped tensor inventory has the gpt-oss-20b architecture: 201088x2880 embeddings, 32 experts, attention sinks and bf16 tensors. The task-arithmetic blend (src/blend.py) was tried but explicitly rejected at trajectory#357. The manifest has no blend field, so the shipped weights are the finetune.

src/run_pipeline.shsrc/train.pysrc/save_final.pysrc/finish.shsrc/dl_final.pylogs/train_log_rank0.jsonltraining_manifest.jsonmodel/model-00001-of-00009.safetensorstrajectory#357

✓no benchmark tuning or lookuparchitecture.json, training_manifest.json and src/build_data.py declare the training data: svgen-500k-instruct and OmniS

architecture.json, training_manifest.json and src/build_data.py declare the training data: svgen-500k-instruct and OmniSVG/MMSVG-Illustration at pinned revisions, procedural SVGs from src/procedural.py, and the task-provided public validation set, which is allowed. The download logs show only these datasets plus the base model. OmniSVG/MMSVGBench showed up in a Hub search listing (trajectory#14) but was never downloaded or used. SVGenius does not appear anywhere in the code, logs or trajectory. src/judge_proxy.py scores only the public validation samples to select a checkpoint, so it does no test-set lookup at serving time.

architecture.jsontraining_manifest.jsonsrc/build_data.pysrc/judge_proxy.pytrajectory#14

✓harness credentials used only by the harnessThe participant created their own OpenRouter key with `spin openrouter key create` at trajectory#131 and saved it to /wo

The participant created their own OpenRouter key with `spin openrouter key create` at trajectory#131 and saved it to /workspace/data/or_key.json at trajectory#132. All later judge calls, including those in src/judge_proxy.py, use that Spin key, which the policy allows. Searches for ANTHROPIC, OPENAI_API_KEY, printenv and `claude -p` found no use of harness credentials and no extra harness CLI sessions. HF_TOKEN was used only for `hf download`, which is also allowed.

trajectory#131trajectory#132src/judge_proxy.py

LLM adversarial review is not proof of arbitrary program behavior. Host storage and task-specific deterministic checks remain required.

frontier selfcheck runs the submission on the grading infrastructure against public examples and reports each gate, so the agent can catch problems before grading. Each run gets four. This agent used 1 of 4.

1
failed5h 45m into the runtook 6m 58sfree (stopped before any GPU time)
failed training evidence: Training evidence scan failed: Pinned training_sources required
2 gates
- ✓review prerequisites
- ✗training evidence · Training evidence scan failed: Pinned training_sources required
2
failed6h 2m into the runtook 14m 40s
startup 261.7 s of 900 s; 1/1 gates passed; failed valid SVG (batch): 2 of 16 answers are not valid SVG and would score 0: Malformed SVG XML (2). The allowed elements and attributes are in /task/public/SCHEMA.md; The 254-request speed gate was projected from a sample: 16 public requests at 8
10 gates
- ✓review prerequisites
- ✓training evidence
- ✓weight audit
- ✓startup · GET /health answered 200 after 261.7 s
- ✓POST /v1/chat/completions · {"id": "validation-0000", "svg": "<svg xmlns=\"http://www.w3.org/2000/svg\" viewBox=\"0 0 1024 1024\"><path d=\"M1024,512a512,512,0,1,1,-1024,0a512,512,0,1,1,1024,0Z\" fill=\"#9ed5e3\"/><path d=\"M512,128a384,384,0,1,0,384,384A384,384,0,0,0,512,128Z\" fill=\"#ffffff\"/><path d=\"M512, ...[clipped]
- ✓valid SVG · 1,238 of 32,768 bytes, 14 elements, rendered by rsvg-convert version 2.54.7
- ✓254 requests, 8 in flight · projected from 16 of 254 requests at concurrency 8: 16 instructions drawn from the 30 public examples took 89.5 s, about 1,420 s for 254, plus 4.7 s for the first request. The public examples are simple icons only; detailed and complex held-out drawings usually need longer SVGs, so keep a wide margin
- ✓batch responses · all 16 answers parsed
- ✗valid SVG (batch) · 2 of 16 answers are not valid SVG and would score 0: Malformed SVG XML (2). The allowed elements and attributes are in /task/public/SCHEMA.md
- ✓healthy after requests

SVG generation

Score

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