{ "analysis": "A3 basin-approach dynamics", "date": "2026-08-08", "plan": "history/plans/2026-08-08_analysis_phase.md A3", "method": { "data": "VAL scalars val/cos_to_target and val/erank from hub tensorboard event files (multi-file runs merged by step, later file wins on duplicates)", "model": "y(t) = y_inf - (y_inf - y0) * exp(-(t - t_first)/tau), scipy curve_fit; fit_full = all points (first val at step 1000); fit_late_ge2000 = steps >= 2000 (past warmup)", "model_free": "t50/t90/t95 = first step crossing y0 + frac*(final5mean - y0), linear interp; robust to the fit instability below", "note": "single exponential systematically under-models every curve: observed t90/t50 = 5.27-7.66 across all 8 trunks vs 3.32 for a pure exponential. The approach is two-timescale (fast half-depth lock by ~2-3.4k steps, heavy consolidation tail). Late-phase fits reach R2 >= .95 for 11/16 curves; a2_dense-s0 is unfittable (R2 .5452 full / .4374 late) despite a smooth monotone curve - model misfit, not noise. Cross-run comparisons therefore use the model-free crossings." }, "runs": { "alephlm0-a1_anchored-s0": { "arm": "a1_anchored", "seed": "s0", "source_repo": "AbstractPhil/alephlm-0", "event_files": [ "events.out.tfevents.1785831061.4233bd44767e.74871.0" ], "gauges": { "val/cos_to_target": { "n_points": 62, "step_range": [ 1000.0, 62000.0 ], "y_at_step1000": 0.6886, "final_value": 0.8393, "fit_full": { "y_inf": 0.8353, "y0": 0.7164, "tau_steps": 5998.0, "r2": 0.9404, "t_offset": 1000.0, "n_points": 62 }, "fit_late_ge2000": { "y_inf": 0.8378, "y0": 0.7562, "tau_steps": 9037.0, "r2": 0.9672, "t_offset": 2000.0, "n_points": 61 }, "crossings_model_free": { "t50_steps": 3235.0, "t90_steps": 18940.0, "t95_steps": 27690.0, "t90_over_t50": 5.853 }, "fit_quality": "late_ok" }, "val/erank": { "n_points": 62, "step_range": [ 1000.0, 62000.0 ], "y_at_step1000": 52.78, "final_value": 98.59, "fit_full": { "y_inf": 97.29, "y0": 61.8, "tau_steps": 6249.0, "r2": 0.9305, "t_offset": 1000.0, "n_points": 62 }, "fit_late_ge2000": { "y_inf": 98.18, "y0": 73.88, "tau_steps": 9795.0, "r2": 0.959, "t_offset": 2000.0, "n_points": 61 }, "crossings_model_free": { "t50_steps": 3245.0, "t90_steps": 19620.0, "t95_steps": 31030.0, "t90_over_t50": 6.045 }, "fit_quality": "late_ok" } } }, "alephlm0-a1_anchored-s1": { "arm": "a1_anchored", "seed": "s1", "source_repo": "AbstractPhil/alephlm-0", "event_files": [ "events.out.tfevents.1785903809.4233bd44767e.94925.0", "events.out.tfevents.1785984808.d2d7b1e1e4e4.1889.0" ], "gauges": { "val/cos_to_target": { "n_points": 62, "step_range": [ 1000.0, 62000.0 ], "y_at_step1000": 0.6843, "final_value": 0.8391, "fit_full": { "y_inf": 0.8351, "y0": 0.7121, "tau_steps": 5498.0, "r2": 0.9418, "t_offset": 1000.0, "n_points": 62 }, "fit_late_ge2000": { "y_inf": 0.8373, "y0": 0.7551, "tau_steps": 8341.0, "r2": 0.9704, "t_offset": 2000.0, "n_points": 61 }, "crossings_model_free": { "t50_steps": 2678.0, "t90_steps": 17870.0, "t95_steps": 25240.0, "t90_over_t50": 6.673 }, 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"events.out.tfevents.1785642653.0396b32d8fce.18515.0" ], "gauges": { "val/cos_to_target": { "n_points": 62, "step_range": [ 1000.0, 62000.0 ], "y_at_step1000": 0.6626, "final_value": 0.8421, "fit_full": { "y_inf": 0.8371, "y0": 0.6957, "tau_steps": 3759.0, "r2": 0.9023, "t_offset": 1000.0, "n_points": 62 }, "fit_late_ge2000": { "y_inf": 0.8405, "y0": 0.7655, "tau_steps": 7982.0, "r2": 0.9561, "t_offset": 2000.0, "n_points": 61 }, "crossings_model_free": { "t50_steps": 1986.0, "t90_steps": 13890.0, "t95_steps": 20930.0, "t90_over_t50": 6.996 }, "fit_quality": "late_ok" }, "val/erank": { "n_points": 62, "step_range": [ 1000.0, 62000.0 ], "y_at_step1000": 50.99, "final_value": 103.1, "fit_full": { "y_inf": 101.6, "y0": 60.67, "tau_steps": 4285.0, "r2": 0.9149, "t_offset": 1000.0, "n_points": 62 }, "fit_late_ge2000": { "y_inf": 102.6, "y0": 79.3, "tau_steps": 8482.0, "r2": 0.9653, "t_offset": 2000.0, "n_points": 61 }, "crossings_model_free": { "t50_steps": 2234.0, "t90_steps": 17120.0, "t95_steps": 24550.0, "t90_over_t50": 7.666 }, "fit_quality": "late_ok" } } } }, "verdict": { "same_attractor": "yes - endpoint band cos .8390-.8420, erank 98.56-99.21 (E1 six) with captionbert trunks landing the high-erank corner 102.9/103.2", "same_path_shape": "yes - all 8 trunks share the two-timescale approach (t90/t50 5.27-7.66, all far above the 3.32 of a single exponential); no trunk takes a qualitatively different route", "anchored_vs_dense": "REVERSED vs small-bed expectation: dense reaches 90% basin depth FASTER. t90(cos) a2_dense 14785/14770 (seeds s0/s1) vs a1_anchored 18936/17870 and a3_random 17717/18811. Same ordering on erank (a2 18186/16990 vs a1 19616/18580, a3 19224/19763). The small-bed anchored sample-efficiency advantage (.9055 vs .7790 @2ep) is a CAPABILITY-gauge fact; on the TRAINING gauge at full scale, routing costs ~3-4k steps of basin depth. Consistent with A2: routed function accrues LATE - the router is still organizing while dense is already settling", "learned_vs_frozen_anchors": "indistinguishable paths: a1 seed-mean t90(cos) 18403 vs a3 18264 (0.8% apart); anchor learnability does not change basin approach", "seed_consistency": "high, arm-dominated: t90(cos) seed spread a2 0.1%, a1 5.8%, a3 6.0%; every arm's two seeds are closer to each other than to any other arm", "captionbert_trunks": "fastest approach of all 8 (t90 cos 11952/13894) and higher erank asymptote (final 102.9/103.2) - the 5-teacher consensus bed both accelerates the approach and lands a wider basin corner", "tau_table_late_fit_cos": { "a1_anchored-s0": 9037, "a1_anchored-s1": 8341, "a2_dense-s0": "unreliable (R2 .44)", "a2_dense-s1": 8243, "a3_random-s0": 7833, "a3_random-s1": 7860, "captionbert-v2": 6332, "captionbert-v2-B": 7982 }, "tau_table_late_fit_erank": { "a1_anchored-s0": 9795, "a1_anchored-s1": 8896, "a2_dense-s0": "unreliable (R2 .54)", "a2_dense-s1": 10030, "a3_random-s0": 9206, "a3_random-s1": 8970, "captionbert-v2": 7132, "captionbert-v2-B": 8482 } } }