{ "beta": 1.5683131734208395, "blocks": 4096, "occurrence_mass": 1.0, "conditional_entropy_nats": 3.5714104546868244, "pair_marginal_l1": 6.792850538862395e-09, "reference": { "vocab": 4096, "original_source": { "identity": { "fit_report_sha256": "7dafa8fe382295e77ce4ed32683ebe56ce83f0b9d25beed2a4410ee7d73c258e", "token_sha256": "f4ec26137d7487429f68a6e977d8386479f205001a6ec7c424bb90f21e6869c9", "suffix_sha256": "b3487d310803cd504260137665a1db1361c0d5c0b24e747747a2673dc2e266f2", "reconstruction": "merged fourgram prefix counts plus final triples; circular reverse histories v2" }, "entropy": 3.5714023585974424, "reference_entropy": 3.571402358583158, "observed_pairs": 5910900, "occurrences": 1999999998, "history_boundary_triples": 2, "source_token_count": 2000000000, "entropy_difference_from_archive": 1.4284573524037114e-11, "note": "Target counts reconstructed from the available 2B natural stream prefix. Matching supported-pair count and near-matching entropy are consistency checks, not proof of byte-identical original source counts. Reverse histories add two wraparound triples to make empirical pair weights exactly stationary." }, "reference_sha256": "535c57b1e3ef35730e26c68987d44cf48df8a5f383387061337dbc7f841ef765", "observed_trigrams": 132810622, "observed_contexts": 5910900, "occurrences": 2000000002, "added_supported_triples": [ [ 480, 2879, 294 ], [ 2879, 294, 2 ], [ 294, 2, 1 ], [ 2, 1, 1465 ] ], "support_matches_original_empirical": true, "original_occurrences": 1999999998, "pair_frequency_tvd_upper_bound": 1.999999998e-09, "conditional_entropy_nats": 3.5714023607206284, "bigram_conditional_entropy_nats": 4.87638457764605, "additional_information_nats": 1.3049822169254215, "deterministic_contexts": 1457930, "deterministic_occurrence_mass": 0.0028618434971381564, "pair_flow_count_error": 0, "note": "Removed two artificial wrap edges, then added one count along an existing-support return path. Exact stationary reference; probabilities differ minutely from the finite-stream empirical source." }, "identity": { "reference": "/notebooks/synthetic_trigrams/outputs/research/natural_language_scaling/trigram_reference_inputs/original", "reference_sha256": "535c57b1e3ef35730e26c68987d44cf48df8a5f383387061337dbc7f841ef765", "rank": 128, "alpha": 0.0, "seed": 20261103, "tolerance": 1e-08, "implementation": "sparse-middle-blocks-v2", "feature_bank_rank": 128, "context_features": "independent", "context_frequency_exponent": 0.0, "frequency_pivot_count": 18063.0, "frequency_multiplier_bounds": [ 0.5, 2.0 ] }, "target_entropy_nats": 3.5714023607206284, "additional_information_nats": 1.3049741229592255, "probabilities_directory": "outputs/research/natural_language_scaling/trigram_flat_components_128_source/fits/beta_1.5683131734", "note": "Exact empirical support; stationary pair marginals and total conditional information matched. Per-context information is not fitted. Coefficients follow power decay before masking and balancing; no exact final spectrum claim." }