Lean proof validity
- Score
- 0.0
- Wallet spend
- $36.48 of $200
- Model cost
- $25.69
- Agent time
- 12h
- Steps
- 529
- Tokens in / out
- 123.0M / 532k
- Self-checks
- 0 passed of 0 used
- Rank at $200 wallet
- 13th of 14
The agent ran out of its 12 hours; the verifier graded what was in /workspace/output.
ReviewIncomplete: Architecture review could not complete: Submission needs architecture.json and serve.sh evidence
Score
0.0
The raw metric (macro F1) is rescaled so that a coin flip (macro F1 0.5) scores 0 and perfect classification scores 100, clamped to that range.
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.
?learned model forward pass
?no lean execution or reimplementation
?stateless per request
?actual inference uses reviewed weights
?training data provenance
?non specialist initialization
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 0 of 4.
The agent never ran a self-check.
The agent's last message before its session ended.
Eager OOMs at batch 16. Narrowing to per-row first.