{"version": "techtree_browser_model_v2_description_native", "row_count": 4057, "feature_dim": 773, "context_length": 16, "obs_dim": 5, "act_dim": 768, "frontier_year": 2025, "seconds_per_year": 31557600.0, "layout": {"main_block": {"offset_floats": 0, "rows": 4057, "dim": 773, "note": "row i = the model-input feature row for chronological event i: first the normalized observation numerics (indices 0..obs_dim-1), then the normalized action numerics (text embedding only; the semantic axis labels are not model-input features for this checkpoint). All values are stored post-normalization ((raw - mean) / std with train-split stats), exactly as the model consumes them. Feature masks are all zero for this corpus and are not stored; pass zeros."}, "softs_block": {"offset_floats": 3136061, "rows": 4057, "dim": 45, "note": "raw (un-normalized) frozen semantic readout targets per event, axes concatenated in axis order; they are not model-input features for this description-native checkpoint"}, "persistence_block": {"offset_floats": 3318626, "rows": 4057, "dim": 45, "note": "trailing-25-event mean of the soft labels at each event (the persistence input of the frozen readout); normalize each axis segment to sum 1 before use"}}, "year_stats": {"year_signed_log": {"index": 0, "mean": 1.3615633743599687, "std": 7.23747861207541, "formula": "sign(year) * log1p(abs(year)), then (x - mean) / std"}, "is_bce": {"index": 1, "mean": 0.38913282107574093, "std": 0.48768740599104393, "formula": "1 if year < 0 else 0, then (x - mean) / std"}, "log_delta_t_s": {"index": 2, "mean": 6.985293348751983, "std": 9.411442679429038, "formula": "log1p(max(0, year_t - year_{t-1}) * seconds_per_year), then (x - mean) / std"}}, "time_encoding": {"kind": "relative_fourier", "bands": 4, "note": "The relative-fourier time encoding is computed INSIDE the ONNX graph from observation feature index 2 (the normalized log-delta-t): phases = t * 2^k for k in 0..bands-1, features = [sin(phases), cos(phases)], projected by a learned MLP and added to the predictor stream. Callers only supply the normalized log-delta-t column."}, "text_embedding": {"start": 5, "count": 768, "provider": "gemma", "model": "embeddinggemma-300m-mrl768", "input": "the event description, without category replacement or semantic-label substitution", "mean": [-0.10065624862909317, 0.0020555793307721615, 0.009488134644925594, 0.012314721010625362, 0.006155896466225386, 0.009760336950421333, -0.013973643071949482, 0.018739154562354088, 0.026601843535900116, -0.020603636279702187, -0.019521020352840424, -0.028074387460947037, -0.00039765090332366526, -0.005076271947473288, 0.016559971496462822, 0.023223863914608955, 0.019497202709317207, -0.02440236322581768, -0.011471528559923172, -0.008543851785361767, -0.006805419456213713, -0.019415540620684624, -0.0012358762323856354, -0.013730322010815144, 0.0144895538687706, 0.011477199383080006, 0.008079078048467636, 0.00727416668087244, 0.008992204442620277, 0.0052692643366754055, 0.006527951918542385, -0.014424995519220829, 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{"context_length": 16, "window": "the 16 chronologically ordered events ending at the anchor (the last event with year <= the queried year)", "time_gaps": "after any edit, re-sort the window chronologically (real events by corpus index; synthetic events keyed just after the last real event with year <= theirs), keep row 0's stored log-delta-t, and recompute rows 1..15 from the window's own year sequence with the log_delta_t_s formula", "remove": "removed corpus indices leave the visible history entirely; earlier real events refill the window from the left", "duplicate": "appended copies displace the oldest non-duplicated window entries", "inject": "each synthetic event displaces the chronologically oldest remaining entry and slots into the window by year order", "synthetic_row": "clone the nearest-by-year real event's feature row (and its era/source categorical ids), then overwrite (a) the year-derived observation dims from the given year and (b) the description-embedding action dims with the visitor's raw embedding vector normalized by the frozen training statistics; semantic labels remain readout-only metadata", "readout": "shares = simplex_projection(persistence(anchor) + latent @ W + b) per axis, where persistence is the anchor's normalized trailing-25-event label share; the anchor (and therefore the persistence input) is fixed by the queried year and is NOT affected by window edits"}}