| const D = {"title": "FDN_generalist", "run_id": "2026-09-22_02-46-01", "stage": "done", "current_gen": 34, "completed": true, "generations": "open-ended", "games_per_gen": 72, "decks": ["FDN_generalist"], "sections": [{"title": "Actual strength", "blurb": "Win rates with 95% Wilson intervals. Eval games put each new checkpoint against opponents that never change, so a rising line means real improvement, not both decks drifting together.", "charts": [{"id": "str_eval_offline", "title": "vs. search without a network", "unit": "win rate", "fmt": "pct"}, {"id": "str_selfplay", "title": "Self-play matchup (both nets current)", "unit": "win rate", "fmt": "pct"}, {"id": "str_onplay", "title": "Player on the play wins", "unit": "rate", "fmt": "pct"}]}, {"title": "Learning", "blurb": "Does the network absorb what search finds? Agreement is last gen's net predicting this gen's MCTS choices on fresh games.", "charts": [{"id": "learn_prior_agree", "title": "Priority agreement: prev net vs new search", "unit": "top-1 accuracy", "fmt": "pct"}, {"id": "learn_prior_target_agree", "title": "Target agreement: prev net vs new search", "unit": "top-1 accuracy", "fmt": "pct"}, {"id": "learn_prior_value_corr", "title": "Value prediction vs outcome (prev net)", "unit": "correlation", "fmt": "num"}, {"id": "learn_value_loss", "title": "Value loss", "unit": "MSE", "fmt": "num"}, {"id": "learn_policy_loss", "title": "Priority policy loss (train)", "unit": "weighted KL", "fmt": "num"}, {"id": "learn_entropy_target", "title": "Entropy of MCTS priority targets", "unit": "nats", "fmt": "num"}, {"id": "learn_entropy_pred", "title": "Entropy of network priority policy", "unit": "nats", "fmt": "num"}, {"id": "learn_grad", "title": "Mean gradient norm", "unit": "L2", "fmt": "num"}, {"id": "data_states", "title": "Training states collected", "unit": "states", "fmt": "int"}, {"id": "data_onehot", "title": "Decisions where search picked one move only", "unit": "fraction", "fmt": "pct"}, {"id": "data_legal", "title": "Legal actions per decision", "unit": "actions", "fmt": "num"}]}, {"title": "Behavior health", "blurb": "Heuristics parsed from game logs. Early agents fail by doing nothing, so spells up and idle turns / passes-with-plays down is the healthy direction.", "charts": [{"id": "beh_spells_cast_per_game", "title": "Spells cast per game", "unit": "spells", "fmt": "num"}, {"id": "beh_lands_played_per_game", "title": "Lands played per game", "unit": "lands", "fmt": "num"}, {"id": "beh_attackers_declared_per_game", "title": "Attackers declared per game", "unit": "attackers", "fmt": "num"}, {"id": "beh_pass_with_play_rate", "title": "Own main phase: passed with a castable card", "unit": "rate", "fmt": "pct"}, {"id": "beh_missed_land_drops_per_game", "title": "Missed land drops per game", "unit": "turns", "fmt": "num"}, {"id": "beh_idle_turns_per_game", "title": "Idle turns (turn 5+, castable, did nothing)", "unit": "turns", "fmt": "num"}, {"id": "beh_bad_target_rate", "title": "Bad targets (removal on own / pump on theirs)", "unit": "rate", "fmt": "pct"}]}, {"title": "Environment health", "blurb": "Is the simulator producing games 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