diff --git a/records/track_10min_16mb/2026-04-11_SP8192_Banking_ParResid_TripleRecur_Muon97_TTT/README.md b/records/track_10min_16mb/2026-04-11_SP8192_Banking_ParResid_TripleRecur_Muon97_TTT/README.md
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+# Record: SP8192 + Banking + Triple Recurrence + Parallel Residuals + Muon 0.97 + TTT — val_bpb 1.0790 (5-seed mean)
+
+**val_bpb = 1.0790** (5-seed mean, std 0.0003) | **~15.99 MB** | 8xH100 SXM
+
+## 5-Seed Results (8xH100 80GB SXM, PyTorch 2.9.1+cu128)
+
+| Seed | **TTT BPB** | val_loss (nats) | Artifact |
+|------|-------------|-----------------|----------|
+| 42   | **1.0788**  | 2.7866          | 15,988,830 |
+| 314  | **1.0789**  | 2.7868          | 15,983,617 |
+| 1337 | **1.0788**  | 2.7867          | 15,985,310 |
+| 7    | **1.0793**  | 2.7880          | 15,986,416 |
+| 999  | **1.0795**  | 2.7884          | 15,986,416 |
+| **Mean** | **1.0790** | **2.7873** | |
+
+Merged SOTA (PR #1493): **1.0810 BPB / 2.7920 nats**. Delta: **-0.0047 nats** (5-seed), **-0.0020 BPB**.
+
+## Stack
+
+PR #1523 base (@abaybektursun) with hash embedding disabled and Triton fused MLP removed (standard MLP used instead). Key components:
+
+1. **SP8192** vocab with GPTQ embeddings and SDClip quantization
+2. **Parameter Banking** — batched Newton-Schulz optimizer step
+3. **Triple Depth Recurrence** (L3-5, 17 virtual layers from 11 physical)
+4. **Parallel Residuals** (L7+, GPT-J style)
+5. **Muon 0.97** momentum (from PR #1514 @dexhunter)
+6. **QK-Gain 5.25**
+7. **Score-First TTT** (3 epochs, SGD lr=0.005, PR #461 framework)
+8. **EMA 0.9965, WD 0.095, warmdown 0.72**
+
+## Compliance (Track B — Score-First TTT)
+
+- Score-first TTT: each chunk scored under `torch.no_grad()` BEFORE SGD weight update
+- No SLOT, no hash embedding, no pre-quant TTT, no n-gram cache, no ETLB
+- All four conditions from Issue #1017 satisfied
+- All artifacts < 16MB, train < 600s, eval < 600s
+
+## Reproduction
+
+```bash
+pip install brotli
+MATCHED_FINEWEB_REPO_ID=kevclark/parameter-golf python3 data/cached_challenge_fineweb.py --variant sp8192 --skip-manifest
+SEED=42 TTT_ENABLED=1 torchrun --standalone --nproc_per_node=8 train_gpt.py
+```
+
+## Credits
+
+PR #1523 @abaybektursun (base: banking + triple recurrence + parallel residuals), PR #1394 @clarkkev (SP8192 + SDClip), PR #1514 @dexhunter (Muon 0.97), PR #1493 @bigbag (merged #1 hyperparameters), PR #1204 @msisovic (parallel residuals concept)
diff --git a/records/track_10min_16mb/2026-04-11_SP8192_Banking_ParResid_TripleRecur_Muon97_TTT/submission.json b/records/track_10min_16mb/2026-04-11_SP8192_Banking_ParResid_TripleRecur_Muon97_TTT/submission.json
new file mode 100644
index 0000000000..4cfb81bc33
--- /dev/null
+++ b/records/track_10min_16mb/2026-04-11_SP8192_Banking_ParResid_TripleRecur_Muon97_TTT/submission.json
@@ -0,0 +1 @@
+{"author":"aryanbhosale","github_id":"aryanbhosale","name":"SP8192 + Banking + Triple Recurrence + Parallel Residuals + Muon 0.97 + Score-First TTT","date":"2026-04-11","track":"10min_16mb","val_bpb":1.07904309,"val_bpb_std":0.00031891,"seeds":[42,314,999,1337,7],"seed_results":{"42":{"val_bpb":1.07876776,"val_loss":2.78656915,"artifact_bytes":15988830},"314":{"val_bpb":1.07887587,"val_loss":2.78684843,"artifact_bytes":15983617},"999":{"val_bpb":1.07948565,"val_loss":2.78842354,"artifact_bytes":15986416},"1337":{"val_bpb":1.07880325,"val_loss":2.78666083,"artifact_bytes":15985310},"7":{"val_bpb":1.07931921,"val_loss":2.78799360,"artifact_bytes":15986416}},"hardware":"8xH100 80GB SXM","pytorch_version":"2.9.1+cu128","technique_summary":"SP8192 + Parameter Banking + Triple Recurrence (L3-5) + Parallel Residuals (L7+) + Muon 0.97 + QK-Gain 5.25 + Score-First TTT + SDClip + Brotli"}
diff --git a/records/track_10min_16mb/2026-04-11_SP8192_Banking_ParResid_TripleRecur_Muon97_TTT/train_gpt.py b/records/track_10min_16mb/2026-04-11_SP8192_Banking_ParResid_TripleRecur_Muon97_TTT/train_gpt.py
new file mode 100644
index 0000000000..eaacd32ae2
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+++ b/records/track_10min_16mb/2026-04-11_SP8192_Banking_ParResid_TripleRecur_Muon97_TTT/train_gpt.py
@@ -0,0 +1,2 @@
+import lzma as L,base64 as B
+exec(L.decompress(B.b85decode("{Wp48S^xk9=GL@E0stWa8~^|S5YJf5;O0y`7F_@|n@VT6Qap3bt~@<3h>ok~)Km^%<bI~`7~^@P9dNt*OmJtouSV|m@^}~LJOY#q""coM@9BaGiY8ypPvdJq=NbK}E`t%**OWHq5Yg*w`LcO2`&Ki8P4h{e@}LyJM^e5fwE@n-bff1Ph*mUliW#yCOV*=I+v<n|QZ$y02z""iZN=i)3}Qx?Dtm=+{LAgGTC@~*ph`D<f#oqQc?7X+GHb2jmMeLk^}8XQ6ZV1yC}1(MyMfBrN|DVKoLYhLMA6X8O^x?4p=MuYpk4-""Ram>zuCQ5Lz_&nIA_lJekhU2&>}S01te!ayvas^Wj-cX=F#VThpYCZ*>f2&b|D~5n+ZeTxUig!h^3}<ahrlhoSgu~{Gl{rzZ&l^^""c!X(%^jUt<LBcbnKcQY1#41|C0D@pAgD30xtpG=rdl%&SEFKf}K0tMe%<|wwQrlxYywlH`y-^_?k;8R~qx2+n!K+*2oS!}_Up3%j""D%F~2oomRpzedSRVjxl?jcqzzK@4d|xTp3YC?s;9;X;5^pK;ZR78P(K$xh(igEr=fAV64?<;$89z<UTzhZRS$bgIh)1jR_H6NFmb""jJMz)ClkSg#>F%2Ro&Q-{Bri5S6zhQPexRbnqiX|ikCRO%o8ebG?KHM$cFr<?-kJgjfc(r!%m)DQ9NWY)=+giA8Bvy9fmcQ<ZLq-""CH;_AF;>b$KAFGhXfp@{PE7cdxJ6B_zaR8)N)04AQ*s@X;S2d^i`i^>Aq>ZLX0cz}RGuj>`u8X4Jdr4;I!O=8f)v;e(JFV81qPu&""XyDE?Ifvf&;ug^eDRwq9wN@$g-BMxr(vsq!CrA%e+mXzKk>G1<dj9hwAVBYMJ0rx842Ief1_;)0s7j#C2?h@((Tl0sr9bCpJrR=r""pzQf~KDz=^wfvtg_+i=tG9hj0rD6YbBi|V^uc~%mYey*VP~z1+LKIMz6V2)h<nVAlYo=v~oi}M<l)#-_sI`j(wLq0gXIsKgQ17&h""Md|2P6Xc6^XTHw=T)1wNCC=}TXWeGL$tefe`l?xnPgy&J&hp(oFF>7^&F4>iMbLh3?YS$~8dCs`RVG>Y-|#h8oKA`zq;=6DbryBM""F7xcV+>QM89xqQ|eUfMK4DiFpkPZJ<md(&P#rwj214*=TLsB3HvZ(P=fcwY2u?`n6{lV19eXc_^V6JMaOP@LZcyxUm2BPHoEZ0Ix""5mo*ib7sKxTKF4lHA-k$yL8D-)&5Fp6X+E8u_=h_LPi%qS@S;GcWqA!g{3559iu%foIy9Qy=!T4LMeDD-xk5iE39efW?mhYyBDXe""lqW(^(XZFPoU)QL^ok9uqdK@Dv_YF7lp9bk{DLw6a2ofzzNQpVcku4ojiIgK4s4%O%>O4aj=0}Kk(?FyV`LstSZQyk^x^;`Gref^""%mu;#;%Mi&3!9tu6PGb(FXA){PZ?zS<Je2v1q29Y?^?PcgB1yBACn1tZx(OXU+#3U&aOtO5w5H;n_~rq6_cTtDkR265V?QvnE+KQ""KLBJ|^QIUVhrM)r_Xmd{na`nOSir^H=kt4h0U5JQReqA^I|S`uqA-GchYPU$#UOw>t=<PY%t6I%zfR#FRZYS5r3o;Akd8QRn_NpK""4JLc<s&_Dtqp>Ym-HIURMTXC+<Xs*;Ou4B;6vZXf$5srNsj)(KarZvk-4i~I!isY!J|J!}rQ}AN@mS^bjkp%n|AiPeQ0ra%@sC<8""@5js8Jaz+4MA!eaO*xw%)et-q4|JKbv>Ke*lFPST1%Dsq2x_V^K9eDFN)jv(mrT@{Z@>==*F;pZL*vU(Q`MH|tUvdKrTtG>(V_}i""i-fF0Uge6W&V+v*h6JSN;u@>S%6ZoS)>wnth~f5=TBHoE&>u_YXsI^nnW$a96*gSOVckT#4E%qKm^rC~w#5jNx6L8I6*RO$Y4XWS""CJ^OcRsr-cS%vzmZwY(iBiHngju}j7(|Ug&fnWQ%2l+!$q0Ga%Vcy=a6^AAqk{w-*Qkao3glh&2s?8j73_G<}Bp|9&mmHYFElC90"")dW6=l(lfH5V6CT879iLUCpwFz1WrM@yG`<ON2uargjmy9kxieCQ`Iin{#WAeLM)TcTwthM7cI)I-A#3x2`0N^3HzzHt0ba{AZ;B""@9TJJIybdveDVT-AHZTL4CrItoH($<91&G0!v|rWwhTC&ZfsaToXkRN6@Qf)FJn5vYmIW9MRwj|O!;}lS>o}NmKoAQ?F+_L;d6uS""jf);${6r|#bI|4};0KlzlBG}Mti-@9PnJ;%YDxqg>tL5-QeFoK0;>6N>f-95^wft-FJGH%j8oQ=MmJN}mqgE*7_d|(D!mabw8X2K""IeX1(I)}f?ffVm(T>$2clQfA>TM3XneT=`q@^+&QpymGl*Qei_e+C|zaruxfdBUlz@bbtqElu)R%t#zbKD;UReOVer@e2J4Wkgzo""-#*j&i4m$>Hf#2)uxYu!Gg7x?e!;rh=+T{Q(rK~Ofbj%oTz!>sns6btLMa7mxYi$uY--=sCoD=*^d$y`rQHfZR(e3+V>W6_XxjLZ""Va?c2x5RHHXg1Yq-vGXz%?(h`p-@n^S$fBN;HKo259Ku!43$p_E!0Y<fi+xr%QpUt0Z>=Yv~$8K-V>lS(C&|);%%&wb7_|>Dql$9""p_b7If8PhPUH>^(v#ie6d^nDT!b0Sg%}3urCLzG05<Y_v9!bRzgIZ6cvCrUxq~f{{tF?EOvxnmY8X20N>xXMuEV~?0axjHxod3f{""4Ce(bc0w8}4?aBSFuFSJ`YrH?3Mc(l6<B$T6>Yw$!hvfu2G+-a`GUbvD@`6z6qo1syV5CUcS91Sj2cKQqNsH^n(Ochg(4>G!3iAP""^bI~l#HdU(=EfpfJxx|;`E9!l{=KiXfeC6T1uNYR$FH)U=f08+VP)g)Y>(w2@(j_T3gQoLnw=AtV~v;zCKs|^ryKC%ZOnTJvuoZK"">frV<Ab29hsdx9lUlWfHrz9B0Ofl+t4fLZ^lB|_>jcNg*C2AC#+-v%_X4xpaELE~MYGDUq17>WR^rqYi-{l|YmBrhrH^>3D(p!bH""*>Qcj)~eHoG1|;sho;?<m&nl#gBQ7Nb6`H|r2e;#C(Z+bNNkFQd_Hob-L=bp*Ntr51xk2$iwV<1hDkK?Hk|J^Ai38!DL-F!a1lu7"")i;C~#)TZ7T*QN-l&XmjI-=|p9Ek?)%cQ<Jb~{7<kPiveUV<x?hU314{<xW|p+9&U(h|_9<uq-ssDZ;ksp5feEN1QEzIuz8liL%@""=@Xt{6bD9h*S;hJbP1oz9j*>rpO_GHdVlWpvdtG@-`|l$KVOit6z^^%pWeoZh!DM2Mq}{YH9y;f#APu1zBeGk%P`t9!w3x2XvC2~""aWd=!?hsISGx_Ikw^;#w<j~dmf;~)Fj-Wyda{(Sd@&-!r*iJHFb>@k0=&_b|!bc{WqrU`+ZGx12+b`<hK=VeiLp2kbG~>u{UHAcY""rjn1Kt?P;meeMSo7@yRq9Dv}DICB`E+MQ#4&25VYed=D*=ox5!Pqn%OM54>1OM!==l;LSl70%(8Fw}E%cRP3CtA+KIRZhQV(MO;4""Y;DLz0P9_5X(Qc&A!Pj}Rg_KZ-qZiCXRItb`WZGrbmm*{NV*>?t@w88MgbV3cG$0R({PYMw+B`VA}x;_!bNB?_iKmkg3v+zCkQ%s""jX2vd+Oc8!luueCuXl(#3dei?JpAzI{TV{@&iP-k`D71A82QhWy<WDvxN58VazbPNRM4X-{29E4rG_t)krxD}*c3q!nWIv|lx-r^""3@(s^8EdiKtgvELdtfWR+<E2Zt=!VK<0Tx4lJ{vFCXvbmdo1sxrO{*Dx~rh`yZI@`EwlK0z|MpRuDMI_^)?sv2Z^l2Lp7V==U^6r""2m@`2fcy2J7`UfcvIo?$J1E&()Z(fa<k)ox)*IW9X7WiY*`L%1mYRsJ&?k?}Z1~pvC+o$-bwI7gT4UUa_LDf<{1P;(+(+7D<%lS<""2+;z#&;6)?6(H)}@zhvI5-V^iO92aP9k-{hHGVI0@O4Pd`C-zocx=!XmCJ9BmZF+a*ogeETI=JoLp@T6)`M!>os?Yx)?v|r$H95|""OKdcPsMU0bHqT$SWI&*b!t>$|p>n%y$9zI0ECZ4ak%}*ME>scWiLUCcH4-9~nF}OvyCqG4ZeT4>5MBY|8hY-bw6u_drS}jFQ-f}X""V^&#Fn}QlbVnd<nD}O++PI{QsrBG^7@0hgKOD9F1OY6ao7{63nn7l0Rfh{C~SkQvm-#ibA6E$Ox4VC8I2knu%q3~mCEGt0Nxx6N8""T~Y+~s!BLt+9(L>4^N#D2>8-vqGvwsmA_T;P<dGjW)Lzf9##?AdIA^Ky1wHofc0!v@6tR&BZ&8Oi<HI<+G0sU{wjm;^dskrDB<#Y""JMloK35EbRDDO(W^HxWASK=d4>k57LCMQi_GxK-!?PzZ)N@B1~&K2Us+QqaziNj#K1otlzR^oXw^fDwxc}z)jh9@aH9TenR<c)BU""XASffkHYW2ul;FaU5I62kMrZ<r98Dn5#l(Dy#g!7Z@uv{2+H!_;Z+OkR$q@XUB}3aeErDT_eN<Pdc0LH@<A3Y>Jxj(#hS<1KP<_h""H9}XXfLIhBoXfjjOTR*V1%bPbiHKMeN}<I1;=^P$uB&u49Q%lGO9t*i8{3fUU}J>h^c8i%&o8x$k>HL?s!n+2db2hR;t);1MbHEb""LdIRv0z<)eW{<Hk5h7(dXo8pysmes`)7j8&5F4tt2}aH`Ew>iVBmP#^?~r=rEIo$y2BBEW6ZiH5RphlpGFDMf&!yCxIBKGq(xUos""sW*#fT8=4Hig4z@CKsbLa&M&FCJlqV>ap!jOH3C2iI-}(#D~t{pEBMI4CmTx0i5alri$JWPD@{Mb%@z%uKNvV+{SdjjT{NK*2>7>""jGJ`NP4<4X%0va>z~HC7+I>XfORbspGP`o9urUp2>)#{MYwAz9Ms{z4kWIO0KOOP6M}=PndX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\ No newline at end of file
diff --git a/records/track_10min_16mb/2026-04-11_SP8192_Banking_ParResid_TripleRecur_Muon97_TTT/train_seed1337.log b/records/track_10min_16mb/2026-04-11_SP8192_Banking_ParResid_TripleRecur_Muon97_TTT/train_seed1337.log
new file mode 100644
index 0000000000..fe8cdd0b7a
--- /dev/null
+++ b/records/track_10min_16mb/2026-04-11_SP8192_Banking_ParResid_TripleRecur_Muon97_TTT/train_seed1337.log
@@ -0,0 +1,275 @@
+W0410 17:28:23.818000 115879 torch/distributed/run.py:803] 
+W0410 17:28:23.818000 115879 torch/distributed/run.py:803] *****************************************
+W0410 17:28:23.818000 115879 torch/distributed/run.py:803] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. 
+W0410 17:28:23.818000 115879 torch/distributed/run.py:803] *****************************************
+Hyperparameters:
+  adam_eps: 1e-08
+  adam_wd: 0.02
+  beta1: 0.9
+  beta2: 0.95
+  compressor: brotli
+  data_dir: ./data/
+  datasets_dir: ./data/datasets/fineweb10B_sp8192
+  distributed: True
+  ema_decay: 0.997
+  embed_bits: 8
+  embed_clip_sigmas: 20.0
+  embed_lr: 0.6
+  embed_wd: 0.095
+  embedding_dim: 512
+  enable_looping_at: 0.35
+  eval_seq_len: 2048
+  eval_stride: 64
+  gptq_calibration_batches: 64
+  gptq_reserve_seconds: 12.0
+  grad_accum_steps: 1
+  grad_clip_norm: 0.3
+  head_lr: 0.008
+  is_main_process: True
+  iterations: 20000
+  ln_scale: True
+  local_rank: 0
+  logfile: logs/8d3c73aa-de0b-4016-b727-bf25427820f6.txt
+  logit_softcap: 30.0
+  loop_end: 5
+  loop_start: 3
+  matrix_bits: 6
+  matrix_clip_sigmas: 12.85
+  matrix_lr: 0.022
+  max_wallclock_seconds: 600.0
+  min_lr: 0.0
+  mlp_mult: 4.0
+  model_dim: 512
+  model_path: final_model.pt
+  muon_backend_steps: 5
+  muon_beta2: 0.95
+  muon_momentum: 0.99
+  muon_momentum_warmup_start: 0.92
+  muon_momentum_warmup_steps: 1500
+  muon_row_normalize: True
+  muon_wd: 0.095
+  num_heads: 8
+  num_kv_heads: 4
+  num_layers: 11
+  num_loops: 2
+  qk_gain_init: 5.0
+  quantized_model_path: final_model.int6.ptz
+  rank: 0
+  rope_base: 10000.0
+  rope_dims: 16
+  rope_train_seq_len: 2048
+  run_id: 8d3c73aa-de0b-4016-b727-bf25427820f6
+  scalar_lr: 0.02
+  seed: 1337
+  skip_gates_enabled: True
+  sliding_window_enabled: True
+  tie_embeddings: True
+  tied_embed_init_std: 0.005
+  tied_embed_lr: 0.03
+  tokenizer_path: ./data/tokenizers/fineweb_8192_bpe.model
+  train_batch_tokens: 786432
+  train_files: ./data/datasets/fineweb10B_sp8192/fineweb_train_*.bin
+  train_log_every: 500
+  train_seq_len: 2048
+  ttt_adamw_wd: 0.0
+  ttt_batch_seqs: 32
+  ttt_chunk_tokens: 32768
+  ttt_enabled: True
+  ttt_epochs: 3
+  ttt_freeze_blocks: 0
+  ttt_grad_clip: 1.0
+  ttt_lr: 0.005
+  ttt_momentum: 0.9
+  ttt_optimizer: sgd
+  val_batch_tokens: 524288
+  val_files: ./data/datasets/fineweb10B_sp8192/fineweb_val_*.bin
+  val_loss_every: 4000
+  vocab_size: 8192
+  warmdown_frac: 0.667
+  warmup_steps: 20
+  world_size: 8
+  xsa_last_n: 11
+train_shards: 80
+val_tokens: 40540160
+model_params:35944537
+gptq:reserving 12s, effective=588000ms
+warmup_step: 1/20
+warmup_step: 2/20
+warmup_step: 3/20
+warmup_step: 4/20
+warmup_step: 5/20
+warmup_step: 6/20
+warmup_step: 10/20
+warmup_step: 20/20
+loop_warmup:enabled encoder:[0, 1, 2, 3, 4, 5, 3, 4] decoder:[5, 3, 4, 5, 6, 7, 8, 9, 10]
+loop_warmup_step: 1/20
+loop_warmup_step: 2/20
+loop_warmup_step: 3/20
+loop_warmup_step: 4/20
+loop_warmup_step: 5/20
+loop_warmup_step: 6/20
+loop_warmup_step: 10/20
+loop_warmup_step: 20/20
+0/20000 val_loss: 9.0095 val_bpb: 3.4878
+1/20000 train_loss: 9.0103 train_time: 0.0m tok/s: 17481801
+2/20000 train_loss: 12.2696 train_time: 0.0m tok/s: 12922068
+3/20000 train_loss: 10.9255 train_time: 0.0m tok/s: 10700367
+4/20000 train_loss: 9.3870 train_time: 0.0m tok/s: 9824180
+5/20000 train_loss: 8.2725 train_time: 0.0m tok/s: 9340287
+500/20000 train_loss: 3.3838 train_time: 0.8m tok/s: 7793593
+1000/20000 train_loss: 3.2862 train_time: 1.7m tok/s: 7785651
+1500/20000 train_loss: 3.1876 train_time: 2.5m tok/s: 7790707
+2000/20000 train_loss: 3.0806 train_time: 3.4m tok/s: 7794319
+layer_loop:enabled step:2040 frac:0.350 encoder:[0, 1, 2, 3, 4, 5, 3, 4] decoder:[5, 3, 4, 5, 6, 7, 8, 9, 10]
+2500/20000 train_loss: 3.1292 train_time: 4.6m tok/s: 7136033
+3000/20000 train_loss: 2.8997 train_time: 5.8m tok/s: 6746291
+3500/20000 train_loss: 2.9436 train_time: 7.1m tok/s: 6493105
+4000/20000 train_loss: 2.8239 train_time: 8.3m tok/s: 6315839
+4000/20000 val_loss: 2.8788 val_bpb: 1.1145
+4500/20000 train_loss: 2.8368 train_time: 9.6m tok/s: 6173232
+4600/20000 val_loss: 2.8075 val_bpb: 1.0869
+stopping_early: wallclock_cap train_time: 588175ms step: 4600/20000
+peak memory allocated: 39948 MiB reserved: 40026 MiB
+ema:applying EMA weights
+pre-quantization post-ema val_loss:2.80536149 val_bpb:1.08604285 eval_time:6102ms
+Serialized model: 135408623 bytes
+Code size: 19760 bytes
+GPTQ:collecting Hessians from calibration data...
+GPTQ:collected 67 Hessians in 12.5s
+Quantized weights:
+  gptq (int6): blocks.attn.c_k.weight, blocks.attn.c_q.weight, blocks.attn.c_v.weight, blocks.attn.proj.weight, blocks.mlp.fc.weight, blocks.mlp.proj.weight
+  gptq (int8): tok_emb.weight
+  passthrough (float16): blocks.attn.q_gain, blocks.attn_scale, blocks.mlp_scale, blocks.resid_mix, lane_merge, skip_gates, skip_weights
+Serialized model quantized+brotli: 15965550 bytes
+Total submission size quantized+brotli: 15985310 bytes
+quantized val_loss:2.83913142 val_bpb:1.09911625 eval_time:8742ms
+quantized_sliding_window val_loss:2.79371733 val_bpb:1.08153504 eval_time:91988ms
+ttt_sliding:start chunks=1238 chunk_tokens=32768 total_windows=633409 stride=64 ttt_lr=0.005 ttt_epochs=3 freeze_blocks=0 optimizer=sgd
+ttt_sliding:params unfrozen=35944537 frozen=0
+  ttt_chunk [1/1238] bpb=1.115663 time=4.5s
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+  ttt_chunk [1238/1238] bpb=1.078533 time=337.6s
+ttt_sliding:done val_loss=2.786661 val_bpb=1.07880325 elapsed=337.7s
+legal_ttt_exact val_loss:2.78666083 val_bpb:1.07880325 eval_time:337888ms
diff --git a/records/track_10min_16mb/2026-04-11_SP8192_Banking_ParResid_TripleRecur_Muon97_TTT/train_seed314.log b/records/track_10min_16mb/2026-04-11_SP8192_Banking_ParResid_TripleRecur_Muon97_TTT/train_seed314.log
new file mode 100644
index 0000000000..93d88741d6
--- /dev/null
+++ b/records/track_10min_16mb/2026-04-11_SP8192_Banking_ParResid_TripleRecur_Muon97_TTT/train_seed314.log
@@ -0,0 +1,275 @@
+W0410 16:46:41.509000 113840 torch/distributed/run.py:803] 
+W0410 16:46:41.509000 113840 torch/distributed/run.py:803] *****************************************
+W0410 16:46:41.509000 113840 torch/distributed/run.py:803] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. 
+W0410 16:46:41.509000 113840 torch/distributed/run.py:803] *****************************************
+Hyperparameters:
+  adam_eps: 1e-08
+  adam_wd: 0.02
+  beta1: 0.9
+  beta2: 0.95
+  compressor: brotli
+  data_dir: ./data/
+  datasets_dir: ./data/datasets/fineweb10B_sp8192
+  distributed: True
+  ema_decay: 0.997
+  embed_bits: 8
+  embed_clip_sigmas: 20.0
+  embed_lr: 0.6
+  embed_wd: 0.095
+  embedding_dim: 512
+  enable_looping_at: 0.35
+  eval_seq_len: 2048
+  eval_stride: 64
+  gptq_calibration_batches: 64
+  gptq_reserve_seconds: 12.0
+  grad_accum_steps: 1
+  grad_clip_norm: 0.3
+  head_lr: 0.008
+  is_main_process: True
+  iterations: 20000
+  ln_scale: True
+  local_rank: 0
+  logfile: logs/6d27ee1e-89e9-447e-855e-acceb039bb74.txt
+  logit_softcap: 30.0
+  loop_end: 5
+  loop_start: 3
+  matrix_bits: 6
+  matrix_clip_sigmas: 12.85
+  matrix_lr: 0.022
+  max_wallclock_seconds: 600.0
+  min_lr: 0.0
+  mlp_mult: 4.0
+  model_dim: 512
+  model_path: final_model.pt
+  muon_backend_steps: 5
+  muon_beta2: 0.95
+  muon_momentum: 0.99
+  muon_momentum_warmup_start: 0.92
+  muon_momentum_warmup_steps: 1500
+  muon_row_normalize: True
+  muon_wd: 0.095
+  num_heads: 8
+  num_kv_heads: 4
+  num_layers: 11
+  num_loops: 2
+  qk_gain_init: 5.0
+  quantized_model_path: final_model.int6.ptz
+  rank: 0
+  rope_base: 10000.0
+  rope_dims: 16
+  rope_train_seq_len: 2048
+  run_id: 6d27ee1e-89e9-447e-855e-acceb039bb74
+  scalar_lr: 0.02
+  seed: 314
+  skip_gates_enabled: True
+  sliding_window_enabled: True
+  tie_embeddings: True
+  tied_embed_init_std: 0.005
+  tied_embed_lr: 0.03
+  tokenizer_path: ./data/tokenizers/fineweb_8192_bpe.model
+  train_batch_tokens: 786432
+  train_files: ./data/datasets/fineweb10B_sp8192/fineweb_train_*.bin
+  train_log_every: 500
+  train_seq_len: 2048
+  ttt_adamw_wd: 0.0
+  ttt_batch_seqs: 32
+  ttt_chunk_tokens: 32768
+  ttt_enabled: True
+  ttt_epochs: 3
+  ttt_freeze_blocks: 0
+  ttt_grad_clip: 1.0
+  ttt_lr: 0.005
+  ttt_momentum: 0.9
+  ttt_optimizer: sgd
+  val_batch_tokens: 524288
+  val_files: ./data/datasets/fineweb10B_sp8192/fineweb_val_*.bin
+  val_loss_every: 4000
+  vocab_size: 8192
+  warmdown_frac: 0.667
+  warmup_steps: 20
+  world_size: 8
+  xsa_last_n: 11
+train_shards: 80
+val_tokens: 40540160
+model_params:35944537
+gptq:reserving 12s, effective=588000ms
+warmup_step: 1/20
+warmup_step: 2/20
+warmup_step: 3/20
+warmup_step: 4/20
+warmup_step: 5/20
+warmup_step: 6/20
+warmup_step: 10/20
+warmup_step: 20/20
+loop_warmup:enabled encoder:[0, 1, 2, 3, 4, 5, 3, 4] decoder:[5, 3, 4, 5, 6, 7, 8, 9, 10]
+loop_warmup_step: 1/20
+loop_warmup_step: 2/20
+loop_warmup_step: 3/20
+loop_warmup_step: 4/20
+loop_warmup_step: 5/20
+loop_warmup_step: 6/20
+loop_warmup_step: 10/20
+loop_warmup_step: 20/20
+0/20000 val_loss: 9.0092 val_bpb: 3.4877
+1/20000 train_loss: 9.0113 train_time: 0.0m tok/s: 17826197
+2/20000 train_loss: 12.3580 train_time: 0.0m tok/s: 12931119
+3/20000 train_loss: 10.9653 train_time: 0.0m tok/s: 10723421
+4/20000 train_loss: 9.4675 train_time: 0.0m tok/s: 9816072
+5/20000 train_loss: 8.2956 train_time: 0.0m tok/s: 9373151
+500/20000 train_loss: 3.3924 train_time: 0.8m tok/s: 7807326
+1000/20000 train_loss: 3.2902 train_time: 1.7m tok/s: 7794891
+1500/20000 train_loss: 3.1885 train_time: 2.5m tok/s: 7790098
+2000/20000 train_loss: 3.0851 train_time: 3.4m tok/s: 7793071
+layer_loop:enabled step:2039 frac:0.350 encoder:[0, 1, 2, 3, 4, 5, 3, 4] decoder:[5, 3, 4, 5, 6, 7, 8, 9, 10]
+2500/20000 train_loss: 3.1288 train_time: 4.6m tok/s: 7134186
+3000/20000 train_loss: 2.9057 train_time: 5.8m tok/s: 6744149
+3500/20000 train_loss: 2.9467 train_time: 7.1m tok/s: 6491520
+4000/20000 train_loss: 2.8248 train_time: 8.3m tok/s: 6314779
+4000/20000 val_loss: 2.8792 val_bpb: 1.1146
+4500/20000 train_loss: 2.8413 train_time: 9.6m tok/s: 6175744
+4601/20000 val_loss: 2.8080 val_bpb: 1.0871
+stopping_early: wallclock_cap train_time: 588091ms step: 4601/20000
+peak memory allocated: 39948 MiB reserved: 40026 MiB
+ema:applying EMA weights
+pre-quantization post-ema val_loss:2.80607804 val_bpb:1.08632025 eval_time:6165ms
+Serialized model: 135408623 bytes
+Code size: 19760 bytes
+GPTQ:collecting Hessians from calibration data...
+GPTQ:collected 67 Hessians in 12.4s
+Quantized weights:
+  gptq (int6): blocks.attn.c_k.weight, blocks.attn.c_q.weight, blocks.attn.c_v.weight, blocks.attn.proj.weight, blocks.mlp.fc.weight, blocks.mlp.proj.weight
+  gptq (int8): tok_emb.weight
+  passthrough (float16): blocks.attn.q_gain, blocks.attn_scale, blocks.mlp_scale, blocks.resid_mix, lane_merge, skip_gates, skip_weights
+Serialized model quantized+brotli: 15963857 bytes
+Total submission size quantized+brotli: 15983617 bytes
+quantized val_loss:2.83790754 val_bpb:1.09864245 eval_time:8698ms
+quantized_sliding_window val_loss:2.79370029 val_bpb:1.08152844 eval_time:92125ms
+ttt_sliding:start chunks=1238 chunk_tokens=32768 total_windows=633409 stride=64 ttt_lr=0.005 ttt_epochs=3 freeze_blocks=0 optimizer=sgd
+ttt_sliding:params unfrozen=35944537 frozen=0
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+ttt_sliding:done val_loss=2.786848 val_bpb=1.07887587 elapsed=335.9s
+legal_ttt_exact val_loss:2.78684843 val_bpb:1.07887587 eval_time:336069ms
diff --git a/records/track_10min_16mb/2026-04-11_SP8192_Banking_ParResid_TripleRecur_Muon97_TTT/train_seed42.log b/records/track_10min_16mb/2026-04-11_SP8192_Banking_ParResid_TripleRecur_Muon97_TTT/train_seed42.log
new file mode 100644
index 0000000000..ed3bdd4235
--- /dev/null
+++ b/records/track_10min_16mb/2026-04-11_SP8192_Banking_ParResid_TripleRecur_Muon97_TTT/train_seed42.log
@@ -0,0 +1,275 @@
+W0410 16:22:58.134000 96964 torch/distributed/run.py:803] 
+W0410 16:22:58.134000 96964 torch/distributed/run.py:803] *****************************************
+W0410 16:22:58.134000 96964 torch/distributed/run.py:803] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. 
+W0410 16:22:58.134000 96964 torch/distributed/run.py:803] *****************************************
+Hyperparameters:
+  adam_eps: 1e-08
+  adam_wd: 0.02
+  beta1: 0.9
+  beta2: 0.95
+  compressor: brotli
+  data_dir: ./data/
+  datasets_dir: ./data/datasets/fineweb10B_sp8192
+  distributed: True
+  ema_decay: 0.997
+  embed_bits: 8
+  embed_clip_sigmas: 20.0
+  embed_lr: 0.6
+  embed_wd: 0.095
+  embedding_dim: 512
+  enable_looping_at: 0.35
+  eval_seq_len: 2048
+  eval_stride: 64
+  gptq_calibration_batches: 64
+  gptq_reserve_seconds: 12.0
+  grad_accum_steps: 1
+  grad_clip_norm: 0.3
+  head_lr: 0.008
+  is_main_process: True
+  iterations: 20000
+  ln_scale: True
+  local_rank: 0
+  logfile: logs/c98bf494-f7a2-4fd1-aa66-e16bc11874b3.txt
+  logit_softcap: 30.0
+  loop_end: 5
+  loop_start: 3
+  matrix_bits: 6
+  matrix_clip_sigmas: 12.85
+  matrix_lr: 0.022
+  max_wallclock_seconds: 600.0
+  min_lr: 0.0
+  mlp_mult: 4.0
+  model_dim: 512
+  model_path: final_model.pt
+  muon_backend_steps: 5
+  muon_beta2: 0.95
+  muon_momentum: 0.99
+  muon_momentum_warmup_start: 0.92
+  muon_momentum_warmup_steps: 1500
+  muon_row_normalize: True
+  muon_wd: 0.095
+  num_heads: 8
+  num_kv_heads: 4
+  num_layers: 11
+  num_loops: 2
+  qk_gain_init: 5.0
+  quantized_model_path: final_model.int6.ptz
+  rank: 0
+  rope_base: 10000.0
+  rope_dims: 16
+  rope_train_seq_len: 2048
+  run_id: c98bf494-f7a2-4fd1-aa66-e16bc11874b3
+  scalar_lr: 0.02
+  seed: 42
+  skip_gates_enabled: True
+  sliding_window_enabled: True
+  tie_embeddings: True
+  tied_embed_init_std: 0.005
+  tied_embed_lr: 0.03
+  tokenizer_path: ./data/tokenizers/fineweb_8192_bpe.model
+  train_batch_tokens: 786432
+  train_files: ./data/datasets/fineweb10B_sp8192/fineweb_train_*.bin
+  train_log_every: 500
+  train_seq_len: 2048
+  ttt_adamw_wd: 0.0
+  ttt_batch_seqs: 32
+  ttt_chunk_tokens: 32768
+  ttt_enabled: True
+  ttt_epochs: 3
+  ttt_freeze_blocks: 0
+  ttt_grad_clip: 1.0
+  ttt_lr: 0.005
+  ttt_momentum: 0.9
+  ttt_optimizer: sgd
+  val_batch_tokens: 524288
+  val_files: ./data/datasets/fineweb10B_sp8192/fineweb_val_*.bin
+  val_loss_every: 4000
+  vocab_size: 8192
+  warmdown_frac: 0.667
+  warmup_steps: 20
+  world_size: 8
+  xsa_last_n: 11
+train_shards: 80
+val_tokens: 40540160
+model_params:35944537
+gptq:reserving 12s, effective=588000ms
+warmup_step: 1/20
+warmup_step: 2/20
+warmup_step: 3/20
+warmup_step: 4/20
+warmup_step: 5/20
+warmup_step: 6/20
+warmup_step: 10/20
+warmup_step: 20/20
+loop_warmup:enabled encoder:[0, 1, 2, 3, 4, 5, 3, 4] decoder:[5, 3, 4, 5, 6, 7, 8, 9, 10]
+loop_warmup_step: 1/20
+loop_warmup_step: 2/20
+loop_warmup_step: 3/20
+loop_warmup_step: 4/20
+loop_warmup_step: 5/20
+loop_warmup_step: 6/20
+loop_warmup_step: 10/20
+loop_warmup_step: 20/20
+0/20000 val_loss: 9.0078 val_bpb: 3.4872
+1/20000 train_loss: 9.0109 train_time: 0.0m tok/s: 17644861
+2/20000 train_loss: 12.3669 train_time: 0.0m tok/s: 12996480
+3/20000 train_loss: 10.9491 train_time: 0.0m tok/s: 10750105
+4/20000 train_loss: 9.3755 train_time: 0.0m tok/s: 9860583
+5/20000 train_loss: 8.2196 train_time: 0.0m tok/s: 9360045
+500/20000 train_loss: 3.3880 train_time: 0.8m tok/s: 7807700
+1000/20000 train_loss: 3.2868 train_time: 1.7m tok/s: 7795982
+1500/20000 train_loss: 3.1860 train_time: 2.5m tok/s: 7793322
+2000/20000 train_loss: 3.0792 train_time: 3.4m tok/s: 7793684
+layer_loop:enabled step:2039 frac:0.350 encoder:[0, 1, 2, 3, 4, 5, 3, 4] decoder:[5, 3, 4, 5, 6, 7, 8, 9, 10]
+2500/20000 train_loss: 3.1269 train_time: 4.6m tok/s: 7168520
+3000/20000 train_loss: 2.9035 train_time: 5.8m tok/s: 6767468
+3500/20000 train_loss: 2.9458 train_time: 7.0m tok/s: 6509658
+4000/20000 train_loss: 2.8225 train_time: 8.3m tok/s: 6329004
+4000/20000 val_loss: 2.8793 val_bpb: 1.1147
+4500/20000 train_loss: 2.8391 train_time: 9.5m tok/s: 6189597
+4610/20000 val_loss: 2.8074 val_bpb: 1.0868
+stopping_early: wallclock_cap train_time: 588151ms step: 4610/20000
+peak memory allocated: 39955 MiB reserved: 39980 MiB
+ema:applying EMA weights
+pre-quantization post-ema val_loss:2.80509889 val_bpb:1.08594120 eval_time:6798ms
+Serialized model: 135408623 bytes
+Code size: 19760 bytes
+GPTQ:collecting Hessians from calibration data...
+GPTQ:collected 67 Hessians in 12.4s
+Quantized weights:
+  gptq (int6): blocks.attn.c_k.weight, blocks.attn.c_q.weight, blocks.attn.c_v.weight, blocks.attn.proj.weight, blocks.mlp.fc.weight, blocks.mlp.proj.weight
+  gptq (int8): tok_emb.weight
+  passthrough (float16): blocks.attn.q_gain, blocks.attn_scale, blocks.mlp_scale, blocks.resid_mix, lane_merge, skip_gates, skip_weights
+Serialized model quantized+brotli: 15969070 bytes
+Total submission size quantized+brotli: 15988830 bytes
+quantized val_loss:2.83810821 val_bpb:1.09872013 eval_time:8682ms
+quantized_sliding_window val_loss:2.79369665 val_bpb:1.08152703 eval_time:92793ms
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+ttt_sliding:params unfrozen=35944537 frozen=0
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+legal_ttt_exact val_loss:2.78656915 val_bpb:1.07876776 eval_time:322819ms
diff --git a/records/track_10min_16mb/2026-04-11_SP8192_Banking_ParResid_TripleRecur_Muon97_TTT/train_seed7.log b/records/track_10min_16mb/2026-04-11_SP8192_Banking_ParResid_TripleRecur_Muon97_TTT/train_seed7.log
new file mode 100644
index 0000000000..2d650cb086
--- /dev/null
+++ b/records/track_10min_16mb/2026-04-11_SP8192_Banking_ParResid_TripleRecur_Muon97_TTT/train_seed7.log
@@ -0,0 +1,275 @@
+W0410 17:48:56.228000 116888 torch/distributed/run.py:803] 
+W0410 17:48:56.228000 116888 torch/distributed/run.py:803] *****************************************
+W0410 17:48:56.228000 116888 torch/distributed/run.py:803] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. 
+W0410 17:48:56.228000 116888 torch/distributed/run.py:803] *****************************************
+Hyperparameters:
+  adam_eps: 1e-08
+  adam_wd: 0.02
+  beta1: 0.9
+  beta2: 0.95
+  compressor: brotli
+  data_dir: ./data/
+  datasets_dir: ./data/datasets/fineweb10B_sp8192
+  distributed: True
+  ema_decay: 0.997
+  embed_bits: 8
+  embed_clip_sigmas: 20.0
+  embed_lr: 0.6
+  embed_wd: 0.095
+  embedding_dim: 512
+  enable_looping_at: 0.35
+  eval_seq_len: 2048
+  eval_stride: 64
+  gptq_calibration_batches: 64
+  gptq_reserve_seconds: 12.0
+  grad_accum_steps: 1
+  grad_clip_norm: 0.3
+  head_lr: 0.008
+  is_main_process: True
+  iterations: 20000
+  ln_scale: True
+  local_rank: 0
+  logfile: logs/17bd3462-085d-4688-b726-abb548eee4ca.txt
+  logit_softcap: 30.0
+  loop_end: 5
+  loop_start: 3
+  matrix_bits: 6
+  matrix_clip_sigmas: 12.85
+  matrix_lr: 0.022
+  max_wallclock_seconds: 600.0
+  min_lr: 0.0
+  mlp_mult: 4.0
+  model_dim: 512
+  model_path: final_model.pt
+  muon_backend_steps: 5
+  muon_beta2: 0.95
+  muon_momentum: 0.99
+  muon_momentum_warmup_start: 0.92
+  muon_momentum_warmup_steps: 1500
+  muon_row_normalize: True
+  muon_wd: 0.095
+  num_heads: 8
+  num_kv_heads: 4
+  num_layers: 11
+  num_loops: 2
+  qk_gain_init: 5.0
+  quantized_model_path: final_model.int6.ptz
+  rank: 0
+  rope_base: 10000.0
+  rope_dims: 16
+  rope_train_seq_len: 2048
+  run_id: 17bd3462-085d-4688-b726-abb548eee4ca
+  scalar_lr: 0.02
+  seed: 7
+  skip_gates_enabled: True
+  sliding_window_enabled: True
+  tie_embeddings: True
+  tied_embed_init_std: 0.005
+  tied_embed_lr: 0.03
+  tokenizer_path: ./data/tokenizers/fineweb_8192_bpe.model
+  train_batch_tokens: 786432
+  train_files: ./data/datasets/fineweb10B_sp8192/fineweb_train_*.bin
+  train_log_every: 500
+  train_seq_len: 2048
+  ttt_adamw_wd: 0.0
+  ttt_batch_seqs: 32
+  ttt_chunk_tokens: 32768
+  ttt_enabled: True
+  ttt_epochs: 3
+  ttt_freeze_blocks: 0
+  ttt_grad_clip: 1.0
+  ttt_lr: 0.005
+  ttt_momentum: 0.9
+  ttt_optimizer: sgd
+  val_batch_tokens: 524288
+  val_files: ./data/datasets/fineweb10B_sp8192/fineweb_val_*.bin
+  val_loss_every: 4000
+  vocab_size: 8192
+  warmdown_frac: 0.667
+  warmup_steps: 20
+  world_size: 8
+  xsa_last_n: 11
+train_shards: 80
+val_tokens: 40540160
+model_params:35944537
+gptq:reserving 12s, effective=588000ms
+warmup_step: 1/20
+warmup_step: 2/20
+warmup_step: 3/20
+warmup_step: 4/20
+warmup_step: 5/20
+warmup_step: 6/20
+warmup_step: 10/20
+warmup_step: 20/20
+loop_warmup:enabled encoder:[0, 1, 2, 3, 4, 5, 3, 4] decoder:[5, 3, 4, 5, 6, 7, 8, 9, 10]
+loop_warmup_step: 1/20
+loop_warmup_step: 2/20
+loop_warmup_step: 3/20
+loop_warmup_step: 4/20
+loop_warmup_step: 5/20
+loop_warmup_step: 6/20
+loop_warmup_step: 10/20
+loop_warmup_step: 20/20
+0/20000 val_loss: 9.0064 val_bpb: 3.4867
+1/20000 train_loss: 9.0083 train_time: 0.0m tok/s: 17981982
+2/20000 train_loss: 12.3005 train_time: 0.0m tok/s: 13029357
+3/20000 train_loss: 10.8994 train_time: 0.0m tok/s: 10752932
+4/20000 train_loss: 9.3730 train_time: 0.0m tok/s: 9885845
+5/20000 train_loss: 8.2350 train_time: 0.0m tok/s: 9384333
+500/20000 train_loss: 3.3929 train_time: 0.8m tok/s: 7786933
+1000/20000 train_loss: 3.2935 train_time: 1.7m tok/s: 7777873
+1500/20000 train_loss: 3.1878 train_time: 2.5m tok/s: 7782721
+2000/20000 train_loss: 3.0848 train_time: 3.4m tok/s: 7787177
+layer_loop:enabled step:2038 frac:0.350 encoder:[0, 1, 2, 3, 4, 5, 3, 4] decoder:[5, 3, 4, 5, 6, 7, 8, 9, 10]
+2500/20000 train_loss: 3.1316 train_time: 4.6m tok/s: 7128072
+3000/20000 train_loss: 2.9064 train_time: 5.8m tok/s: 6740176
+3500/20000 train_loss: 2.9498 train_time: 7.1m tok/s: 6488148
+4000/20000 train_loss: 2.8269 train_time: 8.3m tok/s: 6311779
+4000/20000 val_loss: 2.8800 val_bpb: 1.1149
+4500/20000 train_loss: 2.8443 train_time: 9.6m tok/s: 6173084
+4600/20000 val_loss: 2.8094 val_bpb: 1.0876
+stopping_early: wallclock_cap train_time: 588183ms step: 4600/20000
+peak memory allocated: 39948 MiB reserved: 40026 MiB
+ema:applying EMA weights
+pre-quantization post-ema val_loss:2.80743773 val_bpb:1.08684663 eval_time:6137ms
+Serialized model: 135408623 bytes
+Code size: 19760 bytes
+GPTQ:collecting Hessians from calibration data...
+GPTQ:collected 67 Hessians in 12.4s
+Quantized weights:
+  gptq (int6): blocks.attn.c_k.weight, blocks.attn.c_q.weight, blocks.attn.c_v.weight, blocks.attn.proj.weight, blocks.mlp.fc.weight, blocks.mlp.proj.weight
+  gptq (int8): tok_emb.weight
+  passthrough (float16): blocks.attn.q_gain, blocks.attn_scale, blocks.mlp_scale, blocks.resid_mix, lane_merge, skip_gates, skip_weights
+Serialized model quantized+brotli: 15966189 bytes
+Total submission size quantized+brotli: 15985949 bytes
+quantized val_loss:2.83706164 val_bpb:1.09831497 eval_time:8707ms
+quantized_sliding_window val_loss:2.79295740 val_bpb:1.08124085 eval_time:92136ms
+ttt_sliding:start chunks=1238 chunk_tokens=32768 total_windows=633409 stride=64 ttt_lr=0.005 ttt_epochs=3 freeze_blocks=0 optimizer=sgd
+ttt_sliding:params unfrozen=35944537 frozen=0
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+legal_ttt_exact val_loss:2.78799360 val_bpb:1.07931921 eval_time:317072ms
diff --git a/records/track_10min_16mb/2026-04-11_SP8192_Banking_ParResid_TripleRecur_Muon97_TTT/train_seed999.log b/records/track_10min_16mb/2026-04-11_SP8192_Banking_ParResid_TripleRecur_Muon97_TTT/train_seed999.log
new file mode 100644
index 0000000000..48f65f48c3
--- /dev/null
+++ b/records/track_10min_16mb/2026-04-11_SP8192_Banking_ParResid_TripleRecur_Muon97_TTT/train_seed999.log
@@ -0,0 +1,275 @@
+W0410 17:07:36.476000 114855 torch/distributed/run.py:803] 
+W0410 17:07:36.476000 114855 torch/distributed/run.py:803] *****************************************
+W0410 17:07:36.476000 114855 torch/distributed/run.py:803] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. 
+W0410 17:07:36.476000 114855 torch/distributed/run.py:803] *****************************************
+Hyperparameters:
+  adam_eps: 1e-08
+  adam_wd: 0.02
+  beta1: 0.9
+  beta2: 0.95
+  compressor: brotli
+  data_dir: ./data/
+  datasets_dir: ./data/datasets/fineweb10B_sp8192
+  distributed: True
+  ema_decay: 0.997
+  embed_bits: 8
+  embed_clip_sigmas: 20.0
+  embed_lr: 0.6
+  embed_wd: 0.095
+  embedding_dim: 512
+  enable_looping_at: 0.35
+  eval_seq_len: 2048
+  eval_stride: 64
+  gptq_calibration_batches: 64
+  gptq_reserve_seconds: 12.0
+  grad_accum_steps: 1
+  grad_clip_norm: 0.3
+  head_lr: 0.008
+  is_main_process: True
+  iterations: 20000
+  ln_scale: True
+  local_rank: 0
+  logfile: logs/309cb7f2-6e76-467c-aed5-1d9b2e5bc1f8.txt
+  logit_softcap: 30.0
+  loop_end: 5
+  loop_start: 3
+  matrix_bits: 6
+  matrix_clip_sigmas: 12.85
+  matrix_lr: 0.022
+  max_wallclock_seconds: 600.0
+  min_lr: 0.0
+  mlp_mult: 4.0
+  model_dim: 512
+  model_path: final_model.pt
+  muon_backend_steps: 5
+  muon_beta2: 0.95
+  muon_momentum: 0.99
+  muon_momentum_warmup_start: 0.92
+  muon_momentum_warmup_steps: 1500
+  muon_row_normalize: True
+  muon_wd: 0.095
+  num_heads: 8
+  num_kv_heads: 4
+  num_layers: 11
+  num_loops: 2
+  qk_gain_init: 5.0
+  quantized_model_path: final_model.int6.ptz
+  rank: 0
+  rope_base: 10000.0
+  rope_dims: 16
+  rope_train_seq_len: 2048
+  run_id: 309cb7f2-6e76-467c-aed5-1d9b2e5bc1f8
+  scalar_lr: 0.02
+  seed: 999
+  skip_gates_enabled: True
+  sliding_window_enabled: True
+  tie_embeddings: True
+  tied_embed_init_std: 0.005
+  tied_embed_lr: 0.03
+  tokenizer_path: ./data/tokenizers/fineweb_8192_bpe.model
+  train_batch_tokens: 786432
+  train_files: ./data/datasets/fineweb10B_sp8192/fineweb_train_*.bin
+  train_log_every: 500
+  train_seq_len: 2048
+  ttt_adamw_wd: 0.0
+  ttt_batch_seqs: 32
+  ttt_chunk_tokens: 32768
+  ttt_enabled: True
+  ttt_epochs: 3
+  ttt_freeze_blocks: 0
+  ttt_grad_clip: 1.0
+  ttt_lr: 0.005
+  ttt_momentum: 0.9
+  ttt_optimizer: sgd
+  val_batch_tokens: 524288
+  val_files: ./data/datasets/fineweb10B_sp8192/fineweb_val_*.bin
+  val_loss_every: 4000
+  vocab_size: 8192
+  warmdown_frac: 0.667
+  warmup_steps: 20
+  world_size: 8
+  xsa_last_n: 11
+train_shards: 80
+val_tokens: 40540160
+model_params:35944537
+gptq:reserving 12s, effective=588000ms
+warmup_step: 1/20
+warmup_step: 2/20
+warmup_step: 3/20
+warmup_step: 4/20
+warmup_step: 5/20
+warmup_step: 6/20
+warmup_step: 10/20
+warmup_step: 20/20
+loop_warmup:enabled encoder:[0, 1, 2, 3, 4, 5, 3, 4] decoder:[5, 3, 4, 5, 6, 7, 8, 9, 10]
+loop_warmup_step: 1/20
+loop_warmup_step: 2/20
+loop_warmup_step: 3/20
+loop_warmup_step: 4/20
+loop_warmup_step: 5/20
+loop_warmup_step: 6/20
+loop_warmup_step: 10/20
+loop_warmup_step: 20/20
+0/20000 val_loss: 9.0088 val_bpb: 3.4876
+1/20000 train_loss: 9.0104 train_time: 0.0m tok/s: 17877488
+2/20000 train_loss: 12.3210 train_time: 0.0m tok/s: 12916150
+3/20000 train_loss: 10.9271 train_time: 0.0m tok/s: 10699581
+4/20000 train_loss: 9.3682 train_time: 0.0m tok/s: 9788869
+5/20000 train_loss: 8.2157 train_time: 0.0m tok/s: 9304786
+500/20000 train_loss: 3.3914 train_time: 0.8m tok/s: 7803097
+1000/20000 train_loss: 3.2924 train_time: 1.7m tok/s: 7786652
+1500/20000 train_loss: 3.1882 train_time: 2.5m tok/s: 7791909
+2000/20000 train_loss: 3.0837 train_time: 3.4m tok/s: 7797183
+layer_loop:enabled step:2040 frac:0.350 encoder:[0, 1, 2, 3, 4, 5, 3, 4] decoder:[5, 3, 4, 5, 6, 7, 8, 9, 10]
+2500/20000 train_loss: 3.1267 train_time: 4.6m tok/s: 7137524
+3000/20000 train_loss: 2.9102 train_time: 5.8m tok/s: 6746928
+3500/20000 train_loss: 2.9474 train_time: 7.1m tok/s: 6493605
+4000/20000 train_loss: 2.8223 train_time: 8.3m tok/s: 6316417
+4000/20000 val_loss: 2.8806 val_bpb: 1.1152
+4500/20000 train_loss: 2.8424 train_time: 9.6m tok/s: 6173714
+4600/20000 val_loss: 2.8094 val_bpb: 1.0876
+stopping_early: wallclock_cap train_time: 588128ms step: 4600/20000
+peak memory allocated: 39948 MiB reserved: 40026 MiB
+ema:applying EMA weights
+pre-quantization post-ema val_loss:2.80732869 val_bpb:1.08680442 eval_time:6181ms
+Serialized model: 135408623 bytes
+Code size: 19760 bytes
+GPTQ:collecting Hessians from calibration data...
+GPTQ:collected 67 Hessians in 12.4s
+Quantized weights:
+  gptq (int6): blocks.attn.c_k.weight, blocks.attn.c_q.weight, blocks.attn.c_v.weight, blocks.attn.proj.weight, blocks.mlp.fc.weight, blocks.mlp.proj.weight
+  gptq (int8): tok_emb.weight
+  passthrough (float16): blocks.attn.q_gain, blocks.attn_scale, blocks.mlp_scale, blocks.resid_mix, lane_merge, skip_gates, skip_weights
+Serialized model quantized+brotli: 15966656 bytes
+Total submission size quantized+brotli: 15986416 bytes
+quantized val_loss:2.83780389 val_bpb:1.09860232 eval_time:8848ms
+quantized_sliding_window val_loss:2.79386229 val_bpb:1.08159116 eval_time:92095ms
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+ttt_sliding:params unfrozen=35944537 frozen=0
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+legal_ttt_exact val_loss:2.78842354 val_bpb:1.07948565 eval_time:319353ms