Add degradation analysis + backing artifacts

- reports/degradation-analysis.md: interpretation of the one-parameter-at-a-time
  ablations (ctx/short/small/data vs v1), grounded in val loss + sample text.
  Key findings: held-out loss tracks quality for generalizing models; different
  degradations give qualitatively different failure text; data-starvation
  overfits (train ppl 1.1 / val ppl 322) with samples that hide the damage.
- reports/compare.md: side-by-side samples across all configs
- reports/loss-{small,short,ctx,data}.csv: variant training curves

reports/loss-small.csv

@@ -0,0 +1,40 @@
+iter,train_loss,val_loss,lr
+500,3.9623,3.9608,0.000600
+1000,3.4403,3.4320,0.000598
+1500,3.1455,3.1442,0.000594
+2000,2.9553,2.9633,0.000589
+2500,2.8516,2.8464,0.000582
+3000,2.7778,2.7718,0.000574
+3500,2.7201,2.7195,0.000564
+4000,2.6785,2.6591,0.000552
+4500,2.6386,2.6377,0.000540
+5000,2.6045,2.5929,0.000525
+5500,2.5917,2.5906,0.000510
+6000,2.5724,2.5575,0.000494
+6500,2.5456,2.5374,0.000476
+7000,2.5357,2.5335,0.000458
+7500,2.5279,2.5149,0.000438
+8000,2.5107,2.4987,0.000418
+8500,2.5012,2.4953,0.000398
+9000,2.5002,2.4832,0.000377
+9500,2.4812,2.4738,0.000356
+10000,2.4779,2.4697,0.000334
+10500,2.4721,2.4516,0.000313
+11000,2.4595,2.4584,0.000292
+11500,2.4524,2.4410,0.000271
+12000,2.4597,2.4465,0.000250
+12500,2.4451,2.4416,0.000230
+13000,2.4431,2.4348,0.000210
+13500,2.4292,2.4232,0.000191
+14000,2.4176,2.4196,0.000173
+14500,2.4216,2.4238,0.000156
+15000,2.4285,2.4162,0.000141
+15500,2.4089,2.4081,0.000126
+16000,2.4085,2.4228,0.000113
+16500,2.4114,2.4084,0.000101
+17000,2.4089,2.4052,0.000090
+17500,2.4007,2.3989,0.000081
+18000,2.4007,2.3943,0.000073
+18500,2.4035,2.3914,0.000068
+19000,2.4030,2.4014,0.000063
+19500,2.3995,2.3953,0.000061