openmoji-g1-metric-coordinates-v10-abec130-d7044dc2-9b9b1699

completed passed

Hypothesis

v9, under leak-free corruption and sound optimisation, reached 1.506 precision lift while a zero-parameter local-continuity statistic reaches 1.893 on the identical inputs. That statistic works by subtracting decoded view-unit values, and the encoder has no such operand: coordinates enter as an nn.Embedding over 418 unordered categories, and the trained table carries no order at all - Spearman -0.136 between embedding distance and bin distance, adjacent bins at 0.989 of the all-pairs mean, while the output head, which gets a direct gradient from the corpus marginal, reaches +0.143 and 0.882. Decoding each token to its view-unit value and projecting Fourier features of it should let the model compute the difference it currently cannot, and it should then beat the statistic rather than trail it.

Why run it

This is the one remaining measured candidate after data volume, capacity, the corruption level, noise conditioning, objective balance and the density leak have all been addressed or eliminated. If a metric input lets the model pass a trivial geometric heuristic, Gate G has a working denoiser to build on for the first time. If it does not, the failure is not about how the operands are presented, and the honest reading is that this model class does not learn the relation.

Scope limits

Detection-only, so it reconstructs nothing. Single seed, probability 0.35, marginal-respecting corruption. Beating the continuity statistic on detection is not the same as producing a usable icon; no run in this project has yet produced one.

Measured behaviour

Held-out loss

Table view

optimizer step	held-out loss
0	0.9760
60	0.6606
120	0.6508
180	0.6472
240	0.6470
300	0.6455
360	0.6455
420	0.6445
480	0.6447
540	0.6401
600	0.6321
660	0.6244
720	0.6130
780	0.6025
840	0.5889
900	0.5731
960	0.5637
1020	0.5520
1080	0.5445
1140	0.5345
1200	0.5269
1260	0.5223
1320	0.5164
1380	0.5134
1440	0.5071
1500	0.5047
1560	0.5031
1620	0.5013
1680	0.4969
1740	0.4936
1800	0.4967
1860	0.4910
1920	0.4881
1980	0.4861
2040	0.4856
2100	0.4829
2160	0.4806
2220	0.4798
2280	0.4797
2340	0.4790
2400	0.4771
2460	0.4832
2520	0.4733
2580	0.4760
2640	0.4755
2700	0.4758
2760	0.4729
2820	0.4750
2880	0.4747
2940	0.4707
3000	0.4677
3060	0.4679
3120	0.4669
3180	0.4646
3240	0.4644
3300	0.4647
3360	0.4643
3420	0.4641
3480	0.4648
3540	0.4666
3600	0.4599
3660	0.4625
3720	0.4567
3780	0.4580
3840	0.4519
3900	0.4516
3960	0.4491
4020	0.4462
4080	0.4475
4140	0.4462
4200	0.4423
4260	0.4423
4320	0.4410
4380	0.4398
4440	0.4409
4500	0.4428
4560	0.4383
4620	0.4369
4680	0.4393
4740	0.4345
4800	0.4345
4860	0.4290
4920	0.4322
4980	0.4327
5040	0.4313
5100	0.4364
5160	0.4308
5220	0.4274
5280	0.4308
5340	0.4241
5400	0.4293
5460	0.4307
5520	0.4295
5580	0.4261
5640	0.4336
5700	0.4302
5760	0.4269
5820	0.4324

Held-out token accuracy

aggregatechanged fieldsretained fields

Table view

optimizer step	aggregate	changed fields	retained fields
0	0.3341	0.0013	0.5121
60	0.6252	0.0001	0.9595
120	0.6418	0.0001	0.9850
180	0.6466	0.0001	0.9924
240	0.6471	0.0000	0.9932
300	0.6464	0.0000	0.9921
360	0.6483	0.0000	0.9951
420	0.6461	0.0001	0.9916
480	0.6468	0.0001	0.9927
540	0.6449	0.0001	0.9897
600	0.6270	0.0002	0.9622
660	0.6230	0.0002	0.9561
720	0.5956	0.0007	0.9137
780	0.5879	0.0013	0.9016
840	0.5791	0.0012	0.8882
900	0.5947	0.0013	0.9121
960	0.5934	0.0013	0.9101
1020	0.5961	0.0011	0.9143
1080	0.6000	0.0011	0.9202
1140	0.5965	0.0015	0.9148
1200	0.5970	0.0009	0.9158
1260	0.5896	0.0015	0.9041
1320	0.5924	0.0014	0.9085
1380	0.5846	0.0014	0.8966
1440	0.5893	0.0016	0.9036
1500	0.5916	0.0014	0.9072
1560	0.5872	0.0016	0.9003
1620	0.5875	0.0017	0.9007
1680	0.5817	0.0016	0.8920
1740	0.5878	0.0015	0.9014
1800	0.5816	0.0014	0.8919
1860	0.5801	0.0018	0.8894
1920	0.5871	0.0019	0.9001
1980	0.5854	0.0019	0.8974
2040	0.5773	0.0019	0.8850
2100	0.5790	0.0022	0.8875
2160	0.5874	0.0016	0.9007
2220	0.5848	0.0020	0.8965
2280	0.5820	0.0019	0.8922
2340	0.5823	0.0018	0.8928
2400	0.5873	0.0017	0.9005
2460	0.5766	0.0021	0.8838
2520	0.5852	0.0021	0.8971
2580	0.5806	0.0019	0.8901
2640	0.5766	0.0018	0.8840
2700	0.5824	0.0022	0.8928
2760	0.5840	0.0017	0.8954
2820	0.5765	0.0018	0.8839
2880	0.5726	0.0022	0.8776
2940	0.5801	0.0019	0.8893
3000	0.5797	0.0018	0.8887
3060	0.5763	0.0021	0.8833
3120	0.5820	0.0024	0.8920
3180	0.5812	0.0022	0.8909
3240	0.5846	0.0024	0.8960
3300	0.5779	0.0021	0.8858
3360	0.5797	0.0019	0.8887
3420	0.5779	0.0023	0.8858
3480	0.5734	0.0022	0.8789
3540	0.5725	0.0020	0.8776
3600	0.5804	0.0020	0.8897
3660	0.5742	0.0022	0.8802
3720	0.5781	0.0021	0.8861
3780	0.5791	0.0022	0.8877
3840	0.5816	0.0020	0.8916
3900	0.5795	0.0016	0.8886
3960	0.5836	0.0017	0.8948
4020	0.5847	0.0017	0.8964
4080	0.5802	0.0018	0.8896
4140	0.5840	0.0021	0.8952
4200	0.5828	0.0017	0.8935
4260	0.5795	0.0016	0.8886
4320	0.5820	0.0015	0.8924
4380	0.5796	0.0014	0.8889
4440	0.5793	0.0015	0.8883
4500	0.5753	0.0019	0.8820
4560	0.5821	0.0019	0.8924
4620	0.5854	0.0022	0.8973
4680	0.5818	0.0016	0.8922
4740	0.5840	0.0016	0.8954
4800	0.5761	0.0019	0.8831
4860	0.5852	0.0017	0.8973
4920	0.5842	0.0014	0.8958
4980	0.5844	0.0018	0.8959
5040	0.5820	0.0015	0.8924
5100	0.5771	0.0019	0.8847
5160	0.5844	0.0015	0.8962
5220	0.5860	0.0018	0.8985
5280	0.5837	0.0021	0.8948
5340	0.5887	0.0022	0.9023
5400	0.5823	0.0016	0.8929
5460	0.5847	0.0017	0.8965
5520	0.5822	0.0016	0.8927
5580	0.5845	0.0017	0.8961
5640	0.5783	0.0021	0.8864
5700	0.5793	0.0018	0.8882
5760	0.5866	0.0022	0.8991
5820	0.5792	0.0020	0.8879

Training token accuracy (per batch)

Table view

optimizer step	train accuracy
1	0.3187
2	0.3995
3	0.5041
4	0.5126
5	0.5179
6	0.5552
7	0.5244
8	0.5423
9	0.5452
10	0.5140
11	0.4951
12	0.4757
13	0.4620
14	0.4597
15	0.4720
16	0.5191
17	0.5302
18	0.5588
19	0.5440
20	0.5643
21	0.5425
22	0.5671
23	0.5760
24	0.5872
25	0.5611
26	0.5834
27	0.5397
28	0.5774
29	0.5576
30	0.5590
31	0.5691
32	0.5623
33	0.5538
34	0.5732
35	0.5769
36	0.5916
37	0.5733
38	0.5843
39	0.5935
40	0.6161
41	0.6072
42	0.6205
43	0.5829
44	0.6231
45	0.6290
46	0.6134
47	0.6127
48	0.6174
49	0.6024
50	0.6212
51	0.6085
52	0.6187
53	0.6105
54	0.6101
55	0.6071
56	0.5994
57	0.6226
58	0.6225
59	0.6268
60	0.6132
61	0.6211
62	0.6123
63	0.6255
64	0.6321
65	0.6408
66	0.6402
67	0.6177
68	0.6436
69	0.6204
70	0.6357
71	0.6434
72	0.6353
73	0.6432
74	0.6223
75	0.6424
76	0.6314
77	0.6288
78	0.6284
79	0.6009
80	0.6408
81	0.6288
82	0.6331
83	0.6123
84	0.6331
85	0.6336
86	0.6194
87	0.6253
88	0.6243
89	0.6411
90	0.6406
91	0.6387
92	0.6351
93	0.6064
94	0.6491
95	0.6473
96	0.6362
97	0.6349
98	0.6477
99	0.6462
100	0.6359
101	0.6480
102	0.6417
103	0.6455
104	0.6400
105	0.6593
106	0.6407
107	0.6404
108	0.6321
109	0.6596
110	0.6466
111	0.6485
112	0.6416
113	0.6411
114	0.6477
115	0.6430
116	0.6468
117	0.6385
118	0.6410
119	0.6405
120	0.6600
121	0.6420
122	0.6573
123	0.6366
124	0.6458
125	0.6556
126	0.6447
127	0.6406
128	0.6498
129	0.6400
130	0.6440
131	0.6334
132	0.6373
133	0.6355
134	0.6418
135	0.6204
136	0.6337
137	0.6354
138	0.6478
139	0.6389
140	0.6301
141	0.6432
142	0.6464
143	0.6415
144	0.6342
145	0.6418
146	0.6441
147	0.6499
148	0.6380
149	0.6431
150	0.6349
151	0.6454
152	0.6378
153	0.6386
154	0.6343
155	0.6601
156	0.6413
157	0.6376
158	0.6463
159	0.6379
160	0.6471
161	0.6415
162	0.6398
163	0.6473
164	0.6436
165	0.6407
166	0.6448
167	0.6490
168	0.6417
169	0.6552
170	0.6506
171	0.6509
172	0.6536
173	0.6445
174	0.6407
175	0.6461
176	0.6346
177	0.6404
178	0.6384
179	0.6562
180	0.6426
181	0.6406
182	0.6464
183	0.6453
184	0.6523
185	0.6615
186	0.6528
187	0.6430
188	0.6365
189	0.6345
190	0.6452
191	0.6443
192	0.6374
193	0.6514
194	0.6395
195	0.6538
196	0.6546
197	0.6415
198	0.6380
199	0.6426
200	0.6536
201	0.6449
202	0.6409
203	0.6580
204	0.6506
205	0.6439
206	0.6480
207	0.6525
208	0.6621
209	0.6538
210	0.6540
211	0.6357
212	0.6526
213	0.6470
214	0.6384
215	0.6490
216	0.6325
217	0.6443
218	0.6380
219	0.6532
220	0.6391
221	0.6461
222	0.6419
223	0.6513
224	0.6554
225	0.6602
226	0.6409
227	0.6363
228	0.6391
229	0.6510
230	0.6448
231	0.6316
232	0.6425
233	0.6499
234	0.6473
235	0.6331
236	0.6513
237	0.6362
238	0.6508
239	0.6555
240	0.6520
241	0.6378
242	0.6433
243	0.6596
244	0.6367
245	0.6497
246	0.6469
247	0.6371
248	0.6540
249	0.6448
250	0.6477
251	0.6551
252	0.6510
253	0.6510
254	0.6572
255	0.6507
256	0.6510
257	0.6548
258	0.6435
259	0.6617
260	0.6403
261	0.6323
262	0.6459
263	0.6525
264	0.6456
265	0.6568
266	0.6421
267	0.6543
268	0.6424
269	0.6477
270	0.6483
271	0.6338
272	0.6531
273	0.6409
274	0.6304
275	0.6254
276	0.6402
277	0.6482
278	0.6385
279	0.6538
280	0.6372
281	0.6495
282	0.6582
283	0.6406
284	0.6542
285	0.6502
286	0.6534
287	0.6416
288	0.6413
289	0.6560
290	0.6565
291	0.6350
292	0.6486
293	0.6461
294	0.6546
295	0.6463
296	0.6526
297	0.6520
298	0.6571
299	0.6466
300	0.6497
301	0.6538
302	0.6520
303	0.6395
304	0.6454
305	0.6314
306	0.6394
307	0.6389
308	0.6496
309	0.6491
310	0.6404
311	0.6353
312	0.6609
313	0.6508
314	0.6527
315	0.6389
316	0.6428
317	0.6505
318	0.6458
319	0.6432
320	0.6572
321	0.6415
322	0.6419
323	0.6339
324	0.6425
325	0.6371
326	0.6473
327	0.6470
328	0.6449
329	0.6543
330	0.6389
331	0.6389
332	0.6420
333	0.6473
334	0.6449
335	0.6384
336	0.6569
337	0.6367
338	0.6527
339	0.6455
340	0.6408
341	0.6446
342	0.6448
343	0.6460
344	0.6480
345	0.6522
346	0.6462
347	0.6511
348	0.6327
349	0.6508
350	0.6555
351	0.6528
352	0.6489
353	0.6383
354	0.6516
355	0.6466
356	0.6454
357	0.6517
358	0.6377
359	0.6322
360	0.6451
361	0.6507
362	0.6449
363	0.6559
364	0.6412
365	0.6434
366	0.6522
367	0.6510
368	0.6388
369	0.6355
370	0.6371
371	0.6587
372	0.6412
373	0.6536
374	0.6489
375	0.6489
376	0.6453
377	0.6468
378	0.6387
379	0.6520
380	0.6477
381	0.6402
382	0.6485
383	0.6452
384	0.6416
385	0.6372
386	0.6380
387	0.6413
388	0.6522
389	0.6355
390	0.6418
391	0.6469
392	0.6574
393	0.6313
394	0.6585
395	0.6365
396	0.6552
397	0.6582
398	0.6440
399	0.6523
400	0.6424
401	0.6489
402	0.6508
403	0.6470
404	0.6495
405	0.6464
406	0.6458
407	0.6331
408	0.6532
409	0.6495
410	0.6467
411	0.6375
412	0.6480
413	0.6460
414	0.6409
415	0.6445
416	0.6485
417	0.6471
418	0.6445
419	0.6417
420	0.6491
421	0.6519
422	0.6527
423	0.6422
424	0.6556
425	0.6473
426	0.6465
427	0.6462
428	0.6409
429	0.6405
430	0.6494
431	0.6482
432	0.6627
433	0.6489
434	0.6509
435	0.6506
436	0.6351
437	0.6510
438	0.6508
439	0.6396
440	0.6567
441	0.6473
442	0.6384
443	0.6390
444	0.6435
445	0.6489
446	0.6398
447	0.6507
448	0.6449
449	0.6432
450	0.6261
451	0.6352
452	0.6492
453	0.6513
454	0.6437
455	0.6522
456	0.6574
457	0.6545
458	0.6384
459	0.6367
460	0.6485
461	0.6445
462	0.6444
463	0.6452
464	0.6496
465	0.6454
466	0.6407
467	0.6396
468	0.6479
469	0.6439
470	0.6517
471	0.6555
472	0.6470
473	0.6628
474	0.6315
475	0.6478
476	0.6470
477	0.6440
478	0.6406
479	0.6426
480	0.6553
481	0.6392
482	0.6571
483	0.6558
484	0.6572
485	0.6405
486	0.6572
487	0.6536
488	0.6410
489	0.6542
490	0.6473
491	0.6343
492	0.6397
493	0.6443
494	0.6428
495	0.6456
496	0.6460
497	0.6448
498	0.6365
499	0.6459
500	0.6615
501	0.6528
502	0.6537
503	0.6422
504	0.6501
505	0.6369
506	0.6435
507	0.6411
508	0.6546
509	0.6475
510	0.6394
511	0.6436
512	0.6591
513	0.6526
514	0.6426
515	0.6411
516	0.6516
517	0.6454
518	0.6425
519	0.6443
520	0.6521
521	0.6393
522	0.6559
523	0.6518
524	0.6430
525	0.6475
526	0.6611
527	0.6311
528	0.6577
529	0.6511
530	0.6484
531	0.6401
532	0.6640
533	0.6548
534	0.6569
535	0.6462
536	0.6372
537	0.6432
538	0.6401
539	0.6421
540	0.6375
541	0.6465
542	0.6454
543	0.6351
544	0.6497
545	0.6468
546	0.6319
547	0.6476
548	0.6280
549	0.6554
550	0.6354
551	0.6359
552	0.6408
553	0.6374
554	0.6447
555	0.6351
556	0.6346
557	0.6432
558	0.6275
559	0.6552
560	0.6398
561	0.6456
562	0.6387
563	0.6351
564	0.6429
565	0.6450
566	0.6346
567	0.6367
568	0.6388
569	0.6441
570	0.6319
571	0.6364
572	0.6526
573	0.6468
574	0.6419
575	0.6439
576	0.6425
577	0.6285
578	0.6361
579	0.6455
580	0.6281
581	0.6426
582	0.6344
583	0.6428
584	0.6364
585	0.6300
586	0.6373
587	0.6395
588	0.6495
589	0.6314
590	0.6548
591	0.6450
592	0.6348
593	0.6461
594	0.6333
595	0.6274
596	0.6372
597	0.6236
598	0.6239
599	0.6253
600	0.6419
601	0.6337
602	0.6423
603	0.6261
604	0.6327
605	0.6443
606	0.6360
607	0.6470
608	0.6341
609	0.6211
610	0.6172
611	0.6092
612	0.6240
613	0.6252
614	0.6348
615	0.6281
616	0.6264
617	0.6234
618	0.6231
619	0.6311
620	0.6339
621	0.6194
622	0.6257
623	0.6126
624	0.6269
625	0.6284
626	0.6176
627	0.6156
628	0.6197
629	0.6286
630	0.6495
631	0.6426
632	0.6362
633	0.6404
634	0.6344
635	0.6343
636	0.6262
637	0.6307
638	0.6246
639	0.6225
640	0.6137
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1579	0.6018
1580	0.5855
1581	0.5974
1582	0.6021
1583	0.5977
1584	0.5933
1585	0.6011
1586	0.5911
1587	0.5792
1588	0.6025
1589	0.5932
1590	0.5875
1591	0.5908
1592	0.5911
1593	0.5928
1594	0.5896
1595	0.5971
1596	0.5818
1597	0.5790
1598	0.5932
1599	0.5896
1600	0.6059
1601	0.5896
1602	0.5837
1603	0.5952
1604	0.6023
1605	0.5924
1606	0.5890
1607	0.6018
1608	0.5915
1609	0.5873
1610	0.5974
1611	0.5839
1612	0.6086
1613	0.5909
1614	0.5766
1615	0.5855
1616	0.6112
1617	0.5983
1618	0.5854
1619	0.5990
1620	0.6058
1621	0.5795
1622	0.5794
1623	0.5895
1624	0.5900
1625	0.5914
1626	0.5964
1627	0.5855
1628	0.5759
1629	0.5984
1630	0.5853
1631	0.5936
1632	0.5939
1633	0.5884
1634	0.5973
1635	0.5996
1636	0.6045
1637	0.5858
1638	0.5811
1639	0.5705
1640	0.5894
1641	0.5794
1642	0.5878
1643	0.5841
1644	0.5871
1645	0.5693
1646	0.6073
1647	0.5994
1648	0.5869
1649	0.5897
1650	0.5971
1651	0.5981
1652	0.5828
1653	0.5931
1654	0.5907
1655	0.6004
1656	0.6059
1657	0.5890
1658	0.5904
1659	0.6044
1660	0.6046
1661	0.5919
1662	0.5863
1663	0.5831
1664	0.5836
1665	0.5849
1666	0.5935
1667	0.5795
1668	0.5762
1669	0.5715
1670	0.5934
1671	0.5874
1672	0.5817
1673	0.6054
1674	0.5969
1675	0.5929
1676	0.5836
1677	0.5844
1678	0.5957
1679	0.5876
1680	0.5795
1681	0.5805
1682	0.5899
1683	0.5848
1684	0.5679
1685	0.5856
1686	0.5906
1687	0.5705
1688	0.5894
1689	0.5895
1690	0.6041
1691	0.5912
1692	0.5952
1693	0.5851
1694	0.5942
1695	0.5994
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1697	0.5883
1698	0.5872
1699	0.5895
1700	0.5831
1701	0.6037
1702	0.5794
1703	0.5968
1704	0.5870
1705	0.5938
1706	0.5886
1707	0.5930
1708	0.5920
1709	0.5852
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1711	0.5963
1712	0.5887
1713	0.5835
1714	0.5822
1715	0.5841
1716	0.5845
1717	0.5959
1718	0.5983
1719	0.5670
1720	0.5902
1721	0.5838
1722	0.5842
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1724	0.5867
1725	0.5935
1726	0.5883
1727	0.6003
1728	0.5906
1729	0.5936
1730	0.5924
1731	0.5917
1732	0.5984
1733	0.5995
1734	0.5963
1735	0.6014
1736	0.6025
1737	0.5864
1738	0.5986
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1740	0.5875
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1742	0.6008
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1745	0.5896
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1749	0.5897
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1756	0.5830
1757	0.5946
1758	0.5951
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1760	0.5989
1761	0.6033
1762	0.5974
1763	0.5920
1764	0.6043
1765	0.5949
1766	0.5906
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1768	0.5950
1769	0.5621
1770	0.5799
1771	0.5842
1772	0.5783
1773	0.6034
1774	0.5769
1775	0.5992
1776	0.6077
1777	0.5911
1778	0.5977
1779	0.5885
1780	0.6031
1781	0.6012
1782	0.5846
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1784	0.5955
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1786	0.6027
1787	0.6014
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1789	0.5913
1790	0.5816
1791	0.5938
1792	0.5944
1793	0.6063
1794	0.5940
1795	0.6104
1796	0.5993
1797	0.5989
1798	0.5959
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1800	0.5860
1801	0.5922
1802	0.5974
1803	0.6046
1804	0.5948
1805	0.5986
1806	0.5667
1807	0.5895
1808	0.6070
1809	0.5928
1810	0.6000
1811	0.6063
1812	0.6056
1813	0.5775
1814	0.5888
1815	0.5851
1816	0.5948
1817	0.5806
1818	0.5972
1819	0.5784
1820	0.5935
1821	0.6011
1822	0.5844
1823	0.5920
1824	0.5931
1825	0.5879
1826	0.5886
1827	0.5729
1828	0.5853
1829	0.5857
1830	0.5689
1831	0.5724
1832	0.5812
1833	0.5861
1834	0.5938
1835	0.5883
1836	0.5988
1837	0.5802
1838	0.5930
1839	0.5902
1840	0.6029
1841	0.6047
1842	0.5944
1843	0.6023
1844	0.5811
1845	0.5884
1846	0.5880
1847	0.5882
1848	0.5848
1849	0.5668
1850	0.5969
1851	0.5926
1852	0.5896
1853	0.5924
1854	0.5847
1855	0.5744
1856	0.5943
1857	0.5903
1858	0.5820
1859	0.6036
1860	0.5867
1861	0.5846
1862	0.5805
1863	0.6006
1864	0.5760
1865	0.5880
1866	0.5871
1867	0.5847
1868	0.5992
1869	0.6013
1870	0.5950
1871	0.5844
1872	0.5750
1873	0.5833
1874	0.5684
1875	0.5896
1876	0.6017
1877	0.5830
1878	0.5751
1879	0.5796
1880	0.5902
1881	0.5892
1882	0.5709
1883	0.5913
1884	0.5952
1885	0.5878
1886	0.5722
1887	0.5833
1888	0.6031
1889	0.5814
1890	0.5774
1891	0.5928
1892	0.6016
1893	0.5914
1894	0.5957
1895	0.5900
1896	0.5775
1897	0.5615
1898	0.5809
1899	0.5885
1900	0.5803
1901	0.5726
1902	0.5902
1903	0.5926
1904	0.5918
1905	0.5865
1906	0.5983
1907	0.5861
1908	0.5918
1909	0.6032
1910	0.5847
1911	0.5863
1912	0.5849
1913	0.5958
1914	0.5919
1915	0.5952
1916	0.5851
1917	0.5981
1918	0.5863
1919	0.5774
1920	0.5993
1921	0.5937
1922	0.6024
1923	0.5838
1924	0.6010
1925	0.5935
1926	0.6010
1927	0.5901
1928	0.5916
1929	0.5953
1930	0.5781
1931	0.5929
1932	0.5825
1933	0.5857
1934	0.5887
1935	0.5929
1936	0.5750
1937	0.5776
1938	0.6027
1939	0.5904
1940	0.6127
1941	0.6076
1942	0.5997
1943	0.5697
1944	0.6061
1945	0.6048
1946	0.5950
1947	0.5994
1948	0.6013
1949	0.6002
1950	0.5774
1951	0.6042
1952	0.5930
1953	0.5965
1954	0.5975
1955	0.5811
1956	0.5855
1957	0.5875
1958	0.5903
1959	0.5784
1960	0.5936
1961	0.5818
1962	0.5782
1963	0.5888
1964	0.5949
1965	0.5936
1966	0.5985
1967	0.5852
1968	0.6075
1969	0.5900
1970	0.5781
1971	0.5802
1972	0.5906
1973	0.5854
1974	0.5608
1975	0.5833
1976	0.5693
1977	0.5773
1978	0.5898
1979	0.5912
1980	0.6037
1981	0.5923
1982	0.6105
1983	0.5897
1984	0.5936
1985	0.5816
1986	0.5920
1987	0.5846
1988	0.5857
1989	0.5971
1990	0.5914
1991	0.5930
1992	0.5925
1993	0.5804
1994	0.5831
1995	0.5728
1996	0.5753
1997	0.5857
1998	0.5699
1999	0.5822
2000	0.5794
2001	0.5822
2002	0.5895
2003	0.5896
2004	0.5817
2005	0.5869
2006	0.5830
2007	0.5887
2008	0.5947
2009	0.6016
2010	0.6006
2011	0.6020
2012	0.5850
2013	0.5949
2014	0.5813
2015	0.5766
2016	0.5812
2017	0.5750
2018	0.5918
2019	0.5957
2020	0.5912
2021	0.5974
2022	0.5886
2023	0.5991
2024	0.5935
2025	0.6064
2026	0.5930
2027	0.6019
2028	0.5725
2029	0.6131
2030	0.5978
2031	0.6222
2032	0.5908
2033	0.5870
2034	0.5851
2035	0.5955
2036	0.5999
2037	0.5854
2038	0.5934
2039	0.5893
2040	0.5991
2041	0.5959
2042	0.5810
2043	0.5943
2044	0.5912
2045	0.5919
2046	0.6024
2047	0.5951
2048	0.5903
2049	0.6088
2050	0.6057
2051	0.5791
2052	0.6064
2053	0.5978
2054	0.5832
2055	0.6012
2056	0.5830
2057	0.5783
2058	0.5862
2059	0.5803
2060	0.6091
2061	0.5872
2062	0.6020
2063	0.5833
2064	0.5767
2065	0.5833
2066	0.5813
2067	0.5765
2068	0.5938
2069	0.5851
2070	0.6154
2071	0.5824
2072	0.5830
2073	0.5877
2074	0.5919
2075	0.6000
2076	0.5905
2077	0.6086
2078	0.6036
2079	0.6040
2080	0.5900
2081	0.5975
2082	0.6070
2083	0.5897
2084	0.5897
2085	0.5853
2086	0.5917
2087	0.5923
2088	0.5806
2089	0.5885
2090	0.5828
2091	0.5889
2092	0.6069
2093	0.5829
2094	0.5907
2095	0.5850
2096	0.5978
2097	0.5778
2098	0.5780
2099	0.5885
2100	0.5877
2101	0.5748
2102	0.5889
2103	0.5792
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2105	0.5567
2106	0.5969
2107	0.5804
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2110	0.5851
2111	0.5834
2112	0.5855
2113	0.5856
2114	0.5914
2115	0.5998
2116	0.6038
2117	0.5715
2118	0.5894
2119	0.5891
2120	0.5842
2121	0.5820
2122	0.6004
2123	0.5670
2124	0.5787
2125	0.5878
2126	0.5865
2127	0.5721
2128	0.6006
2129	0.5854
2130	0.5936
2131	0.5851
2132	0.5806
2133	0.5804
2134	0.5858
2135	0.5763
2136	0.5982
2137	0.5858
2138	0.5985
2139	0.5898
2140	0.5912
2141	0.5882
2142	0.5798
2143	0.5996
2144	0.5869
2145	0.5981
2146	0.5945
2147	0.6121
2148	0.5804
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2150	0.5830
2151	0.5778
2152	0.5869
2153	0.5919
2154	0.5972
2155	0.5915
2156	0.5988
2157	0.6097
2158	0.5768
2159	0.6119
2160	0.6074
2161	0.5895
2162	0.5846
2163	0.5897
2164	0.5979
2165	0.5898
2166	0.5966
2167	0.5775
2168	0.5908
2169	0.5994
2170	0.5967
2171	0.5865
2172	0.5918
2173	0.5970
2174	0.5750
2175	0.5849
2176	0.5882
2177	0.6009
2178	0.5971
2179	0.5956
2180	0.5738
2181	0.5840
2182	0.5895
2183	0.5814
2184	0.5773
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2190	0.5696
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2200	0.5920
2201	0.5962
2202	0.5945
2203	0.5990
2204	0.5978
2205	0.5941
2206	0.6009
2207	0.5900
2208	0.5840
2209	0.5928
2210	0.5867
2211	0.5986
2212	0.5836
2213	0.5815
2214	0.5975
2215	0.5883
2216	0.6059
2217	0.5909
2218	0.6008
2219	0.6125
2220	0.6045
2221	0.5796
2222	0.5945
2223	0.5912
2224	0.5802
2225	0.5876
2226	0.5694
2227	0.5867
2228	0.5902
2229	0.5941
2230	0.5970
2231	0.5796
2232	0.5828
2233	0.6028
2234	0.5887
2235	0.5888
2236	0.5865
2237	0.5990
2238	0.5849
2239	0.5849
2240	0.5766
2241	0.5908
2242	0.5759
2243	0.5837
2244	0.5861
2245	0.5849
2246	0.5942
2247	0.6019
2248	0.5904
2249	0.5852
2250	0.5873
2251	0.5819
2252	0.5956
2253	0.5864
2254	0.5962
2255	0.6011
2256	0.5798
2257	0.5832
2258	0.5959
2259	0.6100
2260	0.5981
2261	0.6060
2262	0.5880
2263	0.5962
2264	0.5781
2265	0.5914
2266	0.5776
2267	0.5791
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2270	0.5770
2271	0.5597
2272	0.5828
2273	0.5840
2274	0.6010
2275	0.5952
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2279	0.6029
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2281	0.6040
2282	0.5859
2283	0.5906
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2286	0.5901
2287	0.5868
2288	0.6000
2289	0.5893
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2291	0.5930
2292	0.5927
2293	0.5928
2294	0.5846
2295	0.5955
2296	0.5756
2297	0.5796
2298	0.5900
2299	0.5947
2300	0.5957
2301	0.5953
2302	0.5911
2303	0.5945
2304	0.5815
2305	0.5836
2306	0.5832
2307	0.5983
2308	0.5940
2309	0.5866
2310	0.5725
2311	0.5665
2312	0.5840
2313	0.5862
2314	0.6011
2315	0.6100
2316	0.6054
2317	0.5956
2318	0.5807
2319	0.5864
2320	0.5732
2321	0.5827
2322	0.5746
2323	0.5882
2324	0.5760
2325	0.5995
2326	0.5999
2327	0.6026
2328	0.6071
2329	0.5975
2330	0.5838
2331	0.5840
2332	0.6067
2333	0.5816
2334	0.5889
2335	0.5814
2336	0.5960
2337	0.5766
2338	0.5837
2339	0.5843
2340	0.5781
2341	0.6066
2342	0.5759
2343	0.5977
2344	0.6118
2345	0.6060
2346	0.5997
2347	0.5910
2348	0.5826
2349	0.5855
2350	0.5935
2351	0.5820
2352	0.5818
2353	0.5740
2354	0.5953
2355	0.5935
2356	0.5908
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2358	0.5903
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2360	0.6034
2361	0.5959
2362	0.5837
2363	0.5888
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2366	0.6095
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2370	0.5956
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2372	0.5810
2373	0.5922
2374	0.5899
2375	0.5906
2376	0.5888
2377	0.5938
2378	0.5988
2379	0.6092
2380	0.6087
2381	0.5869
2382	0.5853
2383	0.6019
2384	0.6000
2385	0.5972
2386	0.5887
2387	0.5832
2388	0.6087
2389	0.5830
2390	0.6113
2391	0.5753
2392	0.5699
2393	0.5631
2394	0.5963
2395	0.5968
2396	0.5869
2397	0.5891
2398	0.5954
2399	0.5865
2400	0.6186
2401	0.5940
2402	0.5696
2403	0.5981
2404	0.5927
2405	0.6017
2406	0.6002
2407	0.5772
2408	0.5872
2409	0.5973
2410	0.5899
2411	0.5996
2412	0.5825
2413	0.5735
2414	0.5985
2415	0.5968
2416	0.5808
2417	0.5893
2418	0.5842
2419	0.5845
2420	0.5849
2421	0.5881
2422	0.5943
2423	0.5810
2424	0.6071
2425	0.5864
2426	0.5822
2427	0.5913
2428	0.5776
2429	0.6001
2430	0.5937
2431	0.5951
2432	0.5846
2433	0.5773
2434	0.5904
2435	0.5895
2436	0.5972
2437	0.6016
2438	0.5934
2439	0.5777
2440	0.5827
2441	0.5877
2442	0.5928
2443	0.5909
2444	0.5867
2445	0.5673
2446	0.5938
2447	0.6063
2448	0.6008
2449	0.6029
2450	0.5920
2451	0.6064
2452	0.5842
2453	0.5882
2454	0.5976
2455	0.5891
2456	0.5813
2457	0.6004
2458	0.5656
2459	0.5834
2460	0.5955
2461	0.5934
2462	0.5804
2463	0.5864
2464	0.5948
2465	0.5944
2466	0.5890
2467	0.5979
2468	0.5929
2469	0.5886
2470	0.5807
2471	0.6028
2472	0.5844
2473	0.5900
2474	0.5966
2475	0.5898
2476	0.5728
2477	0.5757
2478	0.5946
2479	0.5583
2480	0.5869
2481	0.5912
2482	0.6006
2483	0.5726
2484	0.5986
2485	0.5879
2486	0.5875
2487	0.5997
2488	0.5997
2489	0.5850
2490	0.6019
2491	0.5931
2492	0.5942
2493	0.5890
2494	0.6026
2495	0.6018
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5485	0.5811
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5488	0.6158
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5490	0.6129
5491	0.6155
5492	0.6052
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5494	0.6053
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5496	0.6181
5497	0.6094
5498	0.6233
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5501	0.6137
5502	0.6124
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5504	0.5874
5505	0.6093
5506	0.6062
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5510	0.6022
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5616	0.6014
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5650	0.5945
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5670	0.6088
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5733	0.6103
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5748	0.5994
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5815	0.6057
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5820	0.6009

Measured result

Verbatim from the run's summary.json.

bucket

bucket-p32-t128

bucket_icons

3359

checkpoint_bytes

4058542

checkpoint_round_trip

true

checkpoint_sha256

bd511202fbe97a72…

config_sha256

d7044dc2e280b142…

corruption

factorized_marginal_respecting_geometry

cuda_version

13.4

deterministic_algorithms

true

device

cuda

eval_every

final_train

loss

0.3899182379245758

step

5820

train_token_accuracy

0.6009021842355176

group_vocabulary_size

locked_path_exact

true

metrics_sha256

fc117db62ce0cfc6…

model_parameters

525152

schema_version

scope

dominant-bucket fixed-topology geometry pilot; not unconditional generation

selected_train_rows

color/svg/1F3F4-E0064-E0065-E0062-E0065-E007F.svg, color/svg/1F468-1F3FC-200D-1F9BC.svg, color/svg/1F561.svg, color/svg/1F469-1F3FE-200D-1F9BC.svg, color/svg/1F469-1F3FD-200D-2764-FE0F-200D-1F48B-200D-1F469-1F3FE.svg, color/svg/1F6B5-1F3FB-200D-2642-FE0F.svg … and 2675 more

selected_validation_rows

color/svg/1F994.svg, color/svg/E30A.svg, color/svg/1F6BE.svg, color/svg/1F3CA-1F3FB-200D-2642-FE0F.svg, color/svg/1F3CB-1F3FC-200D-2642-FE0F.svg, color/svg/1F93D-1F3FC.svg … and 122 more

selection

completed_steps

5820

evals_without_improvement

min_delta

0.0

objective

held_out_loss

patience_evals

selected_held_out_loss

0.42414212226867676

selected_step

5340

stopped_early

true

steps

6300

study_version

openmoji-g1-metric-coordinates-v10

subgroup_vocabulary_size

118

torch_version

2.14.0a0+4fdf77b940.nv26.08

validation

accuracy

0.5886572685462907

changed_accuracy

0.002151925973746503

changed_total

13941

loss

0.42414212226867676

retained_accuracy

0.9023286147235969

retained_total

26067

validation_final_step

accuracy

0.5792341531693661

changed_accuracy

0.0020084642421634028

changed_total

13941

loss

0.4324174225330353

retained_accuracy

0.887942609429547

retained_total

26067

validation_trace_sha256

4a764469e0b45465…

validation_untrained

accuracy

0.3340831833633273

changed_accuracy

0.0012911555842479018

changed_total

13941

loss

0.9759629964828491

retained_accuracy

0.5120650631066099

retained_total

26067

Written result

For the entire Gate G sequence no trained model had beaten a zero-parameter heuristic on a task without a shortcut in it. This one does.

The diagnostic panel measured why the earlier ones could not. The continuity statistic that kept winning works by subtracting decoded view-unit values, and the encoder had no such operand: coordinates entered as an nn.Embedding over 418 unordered categories, and the trained table carried no order whatsoever — Spearman −0.136 between embedding distance and bin distance, adjacent bins at 0.989 of the all-pairs mean. The output head, which gets a direct gradient from the corpus marginal, did learn order: +0.143 and 0.882. The input side never had a reason to.

This run decodes each token to its view-unit value using the role-correct affine map — the segment kind selects it, since slot 0 is an endpoint for a LINE and a control handle for a CUBIC — and projects Fourier features of it, replacing the categorical tables rather than augmenting them.

Result

detector	parameters	precision	lift	recall
marginal density (0 params, no context)	0	—	0.771	—
v9, categorical input	579,872	0.5248	1.506	0.1291
local-continuity statistic	0	—	1.893	—
v10, metric input	525,152	0.9691	2.781	0.2384

criterion	threshold	observed	outcome
beats_the_free_geometric_detector	> 1.893	2.781	pass
improves_on_categorical_input	> 1.506	2.781	pass
structural_safety	locked-path exact, round-trips	both true	pass
reproducibility	identical rerun	identical	pass

Every criterion passes. At the base flag rate it reaches 0.7182 precision and recall for a 2.061 lift, against v9's 1.232.

The model got smaller. 579,872 → 525,152 parameters, because the categorical tables are not allocated at all under metric encoding. A 54,720-parameter reduction nearly doubles the detector. That rules out capacity as the explanation, which the earlier capacity arm had already eliminated from the other direction.

The run used 5,340 of 6,300 steps before early stopping, against v9's 2,340 — with a usable input representation there is more to learn and it keeps learning longer.

The arc this completes

Four defects, each verified before it was acted on, none of them a tuning knob:

#	defect	fix	detector lift
—	starting point (v7)	—	1.365
1	loss averaged over groups, not fields — four fields carried 31% of it	pool over fields
2	no attention padding mask — 29.4% of the sequence attended as content	mask padding	2.806 (v8)
3	corruption drew uniformly, so a token-marginal detector scored 2.84 with no context	draw from the corpus marginal	1.506 (v9, honest task)
4	coordinates encoded as unordered categories, so no difference was computable	decode to view units	2.781 (v10)

Defect 3 is the one that matters for reading the others: it revealed that v8's 2.806 was mostly the leak, and it is why v9's 1.506 — not v8's number — is the baseline this run improves on. Without closing the leak first, v10's 2.781 would have been unreadable too.

Scope, honestly

This is detection, not reconstruction. The model identifies which fields were corrupted; it does not yet put back what was there. Held-out accuracy is meaningless for a detection-only model and the identity baseline is untouched by this run. No render in this project has yet produced a recognizable icon.

What it establishes is that the representation and the training setup can support learning a real geometric relation — which nothing before it had shown. The natural next run applies all four fixes to the full denoising objective and re-tests the identity baseline, which is the bar every earlier model failed.

Single seed, probability 0.35, marginal-respecting corruption. Slot binding was switched off in the same change, disclosed in the run record: it existed to break the permutation symmetry of six summed embeddings, and concatenated per-slot features already distinguish them, so leaving it on would have carried a mechanism that no longer does anything.

State transitions

planned2026-09-21T01:20:00Z
completed2026-09-21T01:40:00Zmetric_coordinate_encoding_beats_every_free_detector_on_a_leak_free_task_with_fewer_parameters

Run record

Verbatim from runs/openmoji-g1-metric-coordinates-v10-abec130-d7044dc2-9b9b1699/run.yaml, the record committed before launch.

schema_version

run_id

openmoji-g1-metric-coordinates-v10-abec130-d7044dc2-9b9b1699

state

completed

parent_run

openmoji-g1-marginal-corruption-v9-74dc5fd-0ef00ff4-9b9b1699

hypothesis

v9, under leak-free corruption and sound optimisation, reached 1.506 precision lift while a zero-parameter local-continuity statistic reaches 1.893 on the identical inputs. That statistic works by subtracting decoded view-unit values, and the encoder has no such operand: coordinates enter as an nn.Embedding over 418 unordered categories, and the trained table carries no order at all - Spearman -0.136 between embedding distance and bin distance, adjacent bins at 0.989 of the all-pairs mean, while the output head, which gets a direct gradient from the corpus marginal, reaches +0.143 and 0.882. Decoding each token to its view-unit value and projecting Fourier features of it should let the model compute the difference it currently cannot, and it should then beat the statistic rather than trail it.

expected_information_gain

scope_limits

changed_factors

primary

model.metric_coordinates 0 -> 8, which replaces the categorical coordinate and endpoint tables with a projection of Fourier features of the decoded view-unit value, using the role-correct affine map selected by segment kind.

secondary

model.slot_binding true -> false, disclosed rather than hidden. Slot binding existed to break the permutation symmetry of six summed embeddings; concatenating per-slot features already distinguishes the slots, so leaving it on would be carrying a mechanism that no longer does anything. It is not expected to matter either way.

parameters

579,872 -> 525,152, a strict REDUCTION, because the categorical tables are not allocated at all. This cannot be confounded with the capacity arm.

held_fixed

v9 exactly otherwise: marginal corruption, pooled loss, padding mask, detection only, edit-mask heads, the full 2,681-icon split, the 128-icon validation draw and its corruption seeds, seed 3101, probability 0.35, batch 16, lr 0.001, eval_every 60, the 6,300-step cap, held-out-loss selection with patience 8.

code

git_commit

abec130f668d108cdddc4f9a3fdad3e410c4fe71

execution_mode

native-local

config

path

configs/learning/openmoji-g1-metric-coordinates-v10.yaml

sha256

d7044dc2e280b142…

dataset

hybrid_sha256

9b9b1699677a6f97…

bucket

bucket-p32-t128

train_samples

2681

validation_samples

128

baselines_under_marginal_corruption

note

all at the matched 8.573% flag rate, on the same 128 held-out icons

local_continuity_statistic

1.893

marginal_density_detector

0.771

v9_trained_categorical

1.506

predeclared_criteria

evaluated_at

the checkpoint selected by held-out loss

primary

beats_the_free_geometric_detector

statement

Precision lift exceeds 1.893, the local-continuity statistic under this same corruption. No trained model in this project has ever beaten a free detector on a leak-free task, so this is the first real bar.

improves_on_categorical_input

statement

Precision lift exceeds v9's 1.506.

structural_safety

statement

Locked-path exactness holds and the canonical checkpoint round-trips.

reproducibility

statement

A second complete invocation returns an identical JSON result.

reported_not_gated

recall and precision at the model's own threshold and at the base flag rate, the selected step and whether early stopping or the cap ended the run

falsification_meaning

If a metric input does not move the detector, then how the operands are presented is not the constraint, and the measured input-side defect - real as it is - was not load-bearing. The remaining structural candidate is the architecture lens's field-level tokenisation, giving every coordinate its own sequence position rather than packing six into one slot at about 16 dimensions each. If that also fails, the honest reading is that this model class does not learn the relation and PROJECT_PLAN.md section 12's branch 4 - the cached autoregressive model on the same codec, which is Gate I - becomes the live branch.

outputs

local_metadata

runs/openmoji-g1-metric-coordinates-v10-abec130-d7044dc2-9b9b1699

report_root

reports/learning/openmoji-g1-metric-coordinates-v10

durable_artifacts

/home/dev/.cache/openmoji-g1-metric-coordinates-v10-abec130-d7044dc2-9b9b1699

planned_at

2026-09-21 01:20:00+00:00

completed_at

2026-09-21 01:40:00+00:00

result

device

cuda

model_parameters

525152

parameters_removed_vs_v9

54720

selected_step

5340

completed_steps

5820

ended_by

early_stopping

identical_rerun

true

detector_at_matched_flag_rate

precision

0.9691

lift_over_base_rate

2.781

recall

0.2384

detector_at_base_flag_rate

precision

0.7182

lift_over_base_rate

2.061

recall

0.7182

predeclared_outcome

overall

passed

beats_the_free_geometric_detector

passed

true

observed

2.781

threshold

1.893

improves_on_categorical_input

passed

true

observed

2.781

threshold

1.506

structural_safety

passed

true

reproducibility

passed

true

conclusion

The first trained model in this project to beat a zero-parameter heuristic on a task with no shortcut in it, and it does so with 54,720 FEWER parameters than the arm it improves on. Encoding coordinates as magnitudes rather than identities takes the detector from 1.506 to 2.781 against a free continuity statistic at 1.893. Capacity is excluded from both directions: the capacity arm was already eliminated, and this change is a strict reduction.

completes_the_arc

Four verified defects, none a tuning knob: the loss averaged over groups so four fields carried 31% of it; no attention padding mask, so 29.4% of the sequence was attended as content; corruption drew uniformly so a token-marginal detector scored 2.84 with no context at all; and coordinates were encoded as unordered categories so no difference was computable. Closing the third is what made the others readable - it revealed that v8's 2.806 was mostly the leak, which is why v9's 1.506 rather than v8's number is the baseline this run improves on.

scope

Detection, not reconstruction. The model identifies which fields were corrupted; it does not put back what was there, held-out accuracy is meaningless for a detection-only model, and the identity baseline is untouched by this run. What it establishes is that the representation and training setup can support learning a real geometric relation, which nothing before it had shown.