SirStone
07f6f3af3f
The user's goal was "a TM gun that can learn fast and generalize better". Swept offline over the real DrussGT fixtures (no live battles) by coordinate descent, one lever at a time, with a SHUFFLED-FEEDBACK CONTROL - a TM trained on randomised targets. That control is what settles the question. Final confirmation, 4 seeds each (~74,600 first-100-tick bullets per config): config EARLY(first 100) OVERALL Shuf_w3 (RANDOM feedback) 23.9% 20.0% win3_s1.1 (best real TM found) 23.7% 20.2% Shuf_w10 (RANDOM feedback) 23.1% 20.0% win3_st100 (prior job's edit) 23.0% 20.1% win3_off (TM correction ~= 0) 22.7% 20.2% def_w10 (shipped default) 22.1% 20.3% Linear (deterministic reference) 34.0% 24.3% The best real config beats the default early (23.7% vs 22.1%, non-overlapping per-seed ranges, z=+7.34, p=2e-13) - but its own SHUFFLED control scores 23.9%, i.e. HIGHER, z=-0.91, p=0.37. Random targets do at least as well. So the early gain is not learning. Per-lever screens were flat: TM_N_CLAUSES 25/50/100/200 all 23.0% early, completely flat; TM_N_STATES 4/32/100 all ~22-23% (unstable across seeds); TM_S mildly monotonic (lower better early); TM_T flat; TM_WINDOW_SIZE 2/3/10 all within noise of each other and of the shuffled control. Two further findings: - The TM-off ablation (correction ~= 0) scores 22.7%/20.2%, essentially the same as TM-on. The TM's correction is near-zero-mean noise; the gun's one-shot internal linear baseline accounts for its accuracy. - The TM gun is 10.3 pp behind Linear early and 4.1 pp behind overall. That deficit is in the BASELINE MODEL (LinearGun iterates flight time; this gun does not), not in the TM hyper-parameters. Tuning knobs cannot close it. Conclusion: do not tune TM hyper-parameters further. Either the input representation or the prediction target is what needs to change - the shuffled control shows the TM is not extracting target information beyond its baseline. Defaults left UNCHANGED (window=10/states=32/S=1.5/T=25/clauses=50); an uncommitted prior edit (window=3/states=100) was reverted as unsupported. Hyper-parameters are now compile-time overridable (-d:TM_WINDOW_SIZE=3 etc.) so future sweeps need no gun edit. NOT MEASURED: real hit rate vs DrussGT (offline only by design). The repo's own docs/gun_rack_analysis.md 2 reports the virtual metric is a sign-unstable ranker of real hit rate, so the comparison against "Linear 10.7% real" is not direct - whether the TM is competitive live is INFERRED-unknown, not measured. Guards: test_gun_harness 39/39, test_vbullet_metric, test_power_selection, test_tsetlin_gun, test_tm_pattern_learning all green.