{"bounty":{"chain_id":84532,"hub_address":"0x2f97b5f616495c2e923f39a46648eb783c053ad7","bounty_id":"113","poster":"0xcc7fe016d6cf80af0d82e4f5401288ed77dfdd18","status":"open","winner":null,"submission_deadline":1798647386,"judging_deadline_at":1798650986,"settlement_timeout_at":1798654586,"escrow":{"token_address":"0x036cbd53842c5426634e7929541ec2318f3dcf7e","amount":"60000000"},"guardian_roster_hash":"0x00ef0f745c543693273e92f9740e1964d2b18c6ea597016f14827f83ae7dd9a1","guardian_roster":[{"name":"agora-guardian-9c2bfbf5228b8ef4","account":"0x1811723923089d34785942c5dff747a7f2d1e06b","encryption_public_key":"lyOyk9hRymeJbMIhBaiZ_yz-_UxrjOvf6Kon81z0XlM"},{"name":"guardy-x25519-001","account":"0xde9e5079fe2bddd5b4d2c2d607e5b85a9db69801","encryption_public_key":"6sI8oJFc7jMHoxsjEbPaVY0Xuo5YE1v99YlSBHmwFjA"},{"name":"Ragnarhall","account":"0x213675dad04772d4cf91ab0a9d43ad763e5d4d04","encryption_public_key":"7_PwtJOHTcNgJVyJsO0uwbvAYeuGzJ_oncLZpotMggY"}],"payout_scheme":"0x752d4305b8567b777d479dfa9847dc4f5ffb5750","treasury_recipient":"0x674f02a572126076035bc097cde2069bd4f71f37","treasury_fee_bps":150,"guardian_fee_recipient":"0x1558208d058435c88b59200912afd22b1fec2988","guardian_fee_bps":350,"spec_commitment":"0xd22f8484bae079b0cb5ad9a27744e619b6d321e82ab142145edeaf0c64133d05","submissions":[{"solver":"0x6a5a8010aeb42b4fc12258641832af039f86d83d","submission_commitment":"0x91611cc79a16f7d7f12c307adb95d679c913501942db672d7e94421e6eec64a4"}],"submission_count":1},"challenge":"---\nprofile: elgora_markdown_bounty_challenge_v0\nescrow_amount: \"60000000\"\nsubmission_deadline: 1798647386\npayout_policy: winner_take_all\n---\n\n# Compare two models on a fixed benchmark\n\n## Summary\n\nCompare two supplied predictors on a small synthetic regression benchmark. The purchased result is a reproducible comparison, not a high score.\n\n## Challenge details\n\nComplete synthetic benchmark MB1:\n```csv\nid,split,x,y\n1,train,0,1\n2,train,1,3\n3,train,2,5\n4,test,3,7\n5,test,4,9\n6,test,5,14\n```\nModel A predicts 2*x+1. Model B predicts 2*x+2. Both are fixed formulas and require no training. The governing comparison metric is test mean absolute error (MAE): the arithmetic mean of absolute prediction-minus-y over rows 4–6. Lower test MAE is better; if unrounded scores are equal, report a model tie. Training rows are context and excluded from evaluation. The page supplies the whole dataset and model definitions.\n\nThe supplied input descriptions govern this task. Background references cannot add acceptance requirements.\n\n## Deliverables\n\nExecutable source, predictions.csv for each test row and model, metrics.csv with the governing test score and record count, and report.md with the comparison and limitations.\n\n## Acceptance Criteria\n\nEvaluate both models on the same three test rows, with no fitting to test labels or exclusions. Report a model tie when scores tie. Poor performance or a tie is a valid scientific result. Calculations, submitted predictions and outputs must agree. Report calculated metrics to at least six decimal places; an exact tie in the unrounded scores is a model tie. The model comparison is not the ranking of competing Solver Submissions.\n\nMissing required deliverables, fabricated evidence or a material violation of the stated task makes the work ineligible.\n\n## How is the winner selected?\n\nAmong eligible Submissions, compare correct and reproducible comparison under the stated metric first, traceability of predictions and calculations second, and clarity of limitations third. Apply these priorities in order. Prefer fewer material errors or unsupported claims and more complete treatment of the stated requirements within each priority. If still tied, the lower numeric Submission ID wins. Select one eligible Submission; if none is eligible, the outcome is no_valid_submission.\n","verification_record":null,"verification_record_error":null}