diff --git a/records/track_10min_16mb/2026-04-12_SP8192_LegalSOTA_Clean/README.md b/records/track_10min_16mb/2026-04-12_SP8192_LegalSOTA_Clean/README.md
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+# Record: SP8192 + Triple Recurrence + Banking + Fused MLP + Muon 0.97 — val_bpb 1.0783 (3-seed mean)
+
+**val_bpb = 1.0783** (3-seed mean, std 0.0004) | **~15.99 MB** | 8xH100 SXM
+
+## 3-Seed Results
+
+| Seed | Pre-quant BPP | Sliding BPP | **TTT BPP** | Artifact |
+|------|---------------|-------------|-------------|----------|
+| 1337 | 1.0859 | 1.0798 | **1.0782** | 15,986,623 |
+| 42 | 1.0856 | 1.0793 | **1.0781** | 15,983,529 |
+| 2024 | 1.0862 | 1.0800 | **1.0788** | 15,986,767 |
+| **Mean** | 1.0859 | 1.0797 | **1.0783** | |
+
+## Architecture
+
+```
+SP8192 tokenizer, 11 physical / 17 virtual layers
+512 dim, MLP 4x (2048 hidden), GQA 8Q/4KV, head_dim=64
+Parallel residuals L7+, QK-Gain 5.0, XSA all 11 layers
+LeakyReLU(0.5)², skip gates, logit softcap 30
+MuonEq-R (lr=0.022, wd=0.095, momentum=0.97) + AdamW
+EMA 0.997, warmdown 66.7%, loop at 35%
+SDClip GPTQ int6 (k=12.85) + int8 embed (k=20) + brotli
+Score-first TTT: SGD lr=0.01, mom=0.9, 3ep, 32K chunks
+Hash embedding: 16384x512, zero-init, trained in TTT
+~36M params, ~15.99MB artifact
+```
+
+## Compliance (Track B — Score-First TTT)
+
+Per Issue #1017:
+- **Condition 1:** Hash key uses prefix tokens only
+- **Condition 2:** Full normalized softmax distribution
+- **Condition 3:** Each chunk scored under no_grad() before TTT update
+- **Condition 4:** Single left-to-right pass, no rescoring
+
+No SLOT, no pre-quant TTT, no n-gram caches, no Tap-In.
+
+## Reproduction
+
+```bash
+pip install brotli sentencepiece
+MATCHED_FINEWEB_REPO_ID=kevclark/parameter-golf python3 data/cached_challenge_fineweb.py --variant sp8192
+SEED=1337 TTT_ENABLED=1 HASH_EMBED_ENABLED=1 TTT_LR=0.01 MUON_MOMENTUM=0.97 \
+  torchrun --standalone --nproc_per_node=8 train_gpt.py
+```
+
+## Credits
+
+PR #1420 @abaybektursun (triple loop + fused kernels), PR #1394 @clarkkev (SP8192 + SDClip), PR #1471 @X-Abhishek-X (3-layer recurrence), PR #1477 @aryanbhosale (parallel residuals + score-first TTT), PR #1460 @resouer (eval-time hash embedding), PR #399 @abaybektursun (parameter banking concept), PR #1514 @dexhunter (Muon 0.97)
diff --git a/records/track_10min_16mb/2026-04-12_SP8192_LegalSOTA_Clean/submission.json b/records/track_10min_16mb/2026-04-12_SP8192_LegalSOTA_Clean/submission.json
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+++ b/records/track_10min_16mb/2026-04-12_SP8192_LegalSOTA_Clean/submission.json
@@ -0,0 +1 @@
+{"author":"EthanYangTW","github_id":"EthanYangTW","name":"SP8192 + Triple Recurrence + Banking + Fused MLP + Muon 0.97 + Score-First TTT + Hash Embedding","date":"2026-04-12","track":"10min_16mb","val_bpb":1.07833,"val_bpb_std":0.00037,"seeds":[1337,42,2024],"seed_results":{"1337":{"val_bpb":1.07817,"artifact_bytes":15986623},"42":{"val_bpb":1.07807,"artifact_bytes":15983529},"2024":{"val_bpb":1.07876,"artifact_bytes":15986767}},"hardware":"8xH100 80GB SXM","pytorch_version":"2.9.1+cu128","technique_summary":"SP8192 + Triple Depth Recurrence (3,4,5 x3, 17 virtual) + Parameter Banking + Fused MLP Triton TMA + CUTLASS EVT + Muon 0.97 + Parallel Residuals (L7+) + QK-Gain 5.0 + Score-First TTT (3ep SGD lr=0.01) + Eval-Time Hash Embedding + SDClip GPTQ int6 + Brotli"}
diff --git a/records/track_10min_16mb/2026-04-12_SP8192_LegalSOTA_Clean/train_gpt.py b/records/track_10min_16mb/2026-04-12_SP8192_LegalSOTA_Clean/train_gpt.py
new file mode 100644
index 0000000000..e20acce09d
--- /dev/null
+++ b/records/track_10min_16mb/2026-04-12_SP8192_LegalSOTA_Clean/train_gpt.py
@@ -0,0 +1,5 @@
+import lzma as L,base64 as B,linecache as C
+S=L.decompress(B.b85decode('{Wp48S^xk9=GL@E0stWa8~^|S5YJf5;P<0H#a#e2n@VT6Qap3bt~@<3h>ok~)Km^%<bI~`7~^@P9dNt*OmJtouSV|m@^}~LJOY#qcoM@9BaGiY8ypPvdJq=NbK}E`t%**OWHq5Yg*w`LcO2`&Ki8P4h{e@}LyJM^e5fwE@n-bff1Ph*mUliW#yCOV*=I+v<n|QZ$y02ziZN=i)3}Qx?Dtm=+{LAgGTC@~*ph`D<f#oqQc?7X+GHb2jmMeLk^}8XQ6ZV1yC}1(MyMfBrN|DVKoLYhLM8i3j+wx{oSdF3VlyOm>m4{rOaiQa2(_3zS%kEP3*8F9$Y#f-ZSHsOI0J>T;4{pdFFBjnfYv-o7{^Aj5A;P`<?|!N#^~B;pKck9SF4dA^8MZp+p<|h-ooeIb1AU%m^I2X_^$C~PSSK9pq2f*E1p)tCCJ%1h?5>z&C6&<=lqU+_4@b=R3%dnFVxv`=Mxc$Xs5zzo9Ric6{F|ATO1RDSva^{lbrs1l?`BLg7X=K(Q;6QVBe>>XcJ1BumAA2c%17A9=Ndxtz>P;Qj8GzvXz~U<l{D2%4AU|d?iMESQQZ_5G*@?e<|8C>FKA&VZOufyRgkT(@mOE^`mG_Q^|@KlW_VERX8baH?N3EM-_&P%L_EFTm43E<f9|tX{7t0HG*8Tv`v?8Z;&rlf7)elMadT7kfx*|5xD`Taa*#54L0TnNcrAJ!hN#fPWTwQ^1PK<c>Hf-VQXAZ<SL4M{X#@2b;lj&+|>>}m{0HbiJdo*$GnEANkBhMepqyPcx3Fe`cguZa<wAAkzV?Fyt*LDx$PI(d0gZZA$d$Ia`A>~?mFyAO7xx@pyp9u#SgbW+7#3_|0HHzND*Q91f>^;{89_J{M<iuF>n(ifsT;<xkd+~8s6$8Ou2B~l#MZuSjnY+Rea7uO-n_Bw3brdq7z3+ld<AZFq%e_(jQQ;tn`6A2vU^X>O=nQfn#Ij{*MK%J|f`^>lsxUub!E!Sa7u`{MQ6F9EpM`M3LtLPR@0)h4vzK5MC|1-JXholX8)ey)dQyK9Qb%s<p_{thc<h(T?{c#p<}lCY7)WNc&zT)3Ok}_stnSvn09&W(CL5Y<rW1QK%_Ox@nTg@SO;b1rJJi@oewvYFL4K(T6IGqwxNIk0V7wei;<ekGqug^JaUSHh&kfzah~qYLO8QJSj_}uJj{J&(U?CouztJLw%;yL<DDKE{YB`bsT{TmBU<-9Ike3CL)k8%>bNCH*vNp0k<6<%6l+hRO0vpF^o+^Y#^WM9+l@#vx0FlzSmKyQ#D?TgIPQ3$mo~ESH~#){o=pZ>bx|DpULrF#8zQk&^auf9^{$UlamN3-&TUdJlF%9!C7`Nwr+MCh4tYsw(vrIp6F_fi?;BNr?Yjf9NFRsW@;Uw#><yL$|VK#439JHYrhJSEMxhna?3W`25Iv!yJ`U&iK1M?DWcC>_q%@dXd)o8ZOKhsZqb@w3<nUo6y>rbC(;tlItqrqozqGJ43StQ$S=7b<jq4w|J&|>GQ}<h6Eu`mr3YDU=+7B$X^!n;K$$8FyUgVVP$~Mujohz1<`Im#L-#SB&KE?^kT!boh}v>@c?@-$H5}FVv__HFkSqWth_C&p>@S1ZdEnw#`Ivzv>R)$n%JiuE>_KhkJSnCNR9vU7cn1dmYI&yutlAMSAB?;wu<P2|gyWWuU=x1SkA9UJOt6LC3`RIOeYd;ruXBpXvPIeh)*RGiSki7qm;yRK$aQg*!h08BMgp@=iA~^6U0&EuEsC&mPJkX~IVv9hW_C2MOIvqFVOPa<>i}O*PPW~e>3lp#V7QU&$y-;gUOonM89&$WEQzh&mCb@i68QFyPK^{!LRHQ+FrD!`F{DXZ=m0t~iTHET-~L9XBJ*;X<OUI&xz_lIJAE{4Gk<)}g1)w?-$<;64PNFt0?Rtxlp95D>)3s}?=ekXQ0hmCMvAC*DJ2e96dnU$g`wC8hd3Q>?8f%UHBIu-kvYkhSO~w#I+(8+2*XwhsSs-c68aQ7Uz&{8$(tdt(TZ@eL!K@-B7CtjeSzA&fduE#HG%Z{57?UQAKy;vtESieFl$AUj2%TK9OtPQzB=(?)7_)y?;M{Oc4wQh?Facnd2$bLeQ?+B6N@M7ZII^8KXd{L*~gUX+qhJ@&~h@Bp*MCd;qK$AMj=gIaX@IMJ%uB&HG~x1fJ#qJ8%th&V;g6du(OHDF)V6YWE1wK1!Y6(xE(rH*fw$n3<R+VqxC0(c|;U9p)_C{h0m9j^?H%6sd6Cd2Iomv2d%5W;|OlFlaslH<t;gpst?8O;^z(Jxkmf**Am-c6lc&vzFJRu=L6a<6IOTu!q`;H()sC8X&XaMe;I6mZoBY$i_QxUN8LBi!#IP)pqtT+I*ubfOAR1B*sXf6_M(M2tJqsUS4!+OD!FuZz4ww>4{~i<b+vGx$k{5(q*&cuYbU}Iq23+|DQKybUrzH_5zvJc5oT&Zv*@DqIC;~W12%5JN|#mm)r*q5IwN_MwS6-lGU~>EqIO3;&5*AZfo*|XS4;rSly}<f@GUWWMe>J>Uxm|M;dSWg!Fn(jbE%pbU97gNBCEYM!Ae1ql_~Ss(rQIyulSyL9!tHPz`$i$-V;+5jAvMb_IOcRnNe#bDQ9I6*;UDLUEqvcHud1l5<AT_g~ds6qj{x%=tc#xKP?)~WGMf_c-^O(xxlIY-dzc(n2gN=-cDyVr${c}b&qvf5qhJKe3d5^<z*<?i>EJ6<<7)PL84F>(+=B5ge{T?!Rw~TG@o##;!Hx=I&_#PIl%jD>mA@vT&H(lTr($YOQZYr77wwuN)tsqwxhj%0Pe;|cgFcKsc-bO_>gjEb#8r`F$i}#E)lN-rE<#^3rkarA|jyZN<_|zJ4;|FcsW>*b_Lr6rwRR6>?9h@(PwP#p3<6_aHLz1aH>UK1p1s&?L<!9aKGC{c6%><4~f%6h-&5h?!t6=k(zHFF9WoA{g-_~#RLeG$}x$1ehsd+yFxR(8QKb>n-?4NIoQaaJu?p7cl<}I84VNUMqPe@b_?p|*SdRtu<qnEjGi!i;EoCXJiHzt*bY#&MuVHNYs439kF+?r3$&v?8ym$h9Lbsz5&x!8Z&WcT<l%NLS3f4}-m4gm=F38lVG7{6N^8N@D&`9GPi<`W2<fROvbp<tTiq!Sb)<;gMDq}mtbsV|B5@o;q6^`S3$1Z~S(SFaTYhbsg;KzJdfb{;F_*H{qByn7#vE#J>twvfLRCc|Ogj333!MxzE19;HY}HLSyFN?4ut$$ejjbHx9>_CEe^3*cqK1qg@AuPSgDi`!`t=E>F$&JP5$iyBA6YV-<k{MrAWiUT>+|R%Y!K5wtCNO_F2ftgL2)-~G-Xs>V3dhXNCZ$+EI5_Oe&fI2(~zD9)G9|Ap=|(a>KbtvOyN84M0k?)NxRdnWvSrrjRbbTd3EA3m}^Vk?mp>z@;}mTmeVkf=(tIr8nJT?`6_<1H`!w|(Q`a_Dr|-wfn<^$Gl-teh1#n6sLVI6)M__gl>EJMeV*9(E4TPEx;yZ;PtwnFC+y~4Ed~%LW}_J^shH5B%)bR+ww9*a#hot+gwA51q^lTWSthj=Q-d6l`&RO!XKbSa9UFyokF^ZUtx{P1))C;wFL38D)48%r!tYiR$nfsq*MWr|GvDHkOWjpgxem1M_uyjjvbvp-h((5qRRg~uFdF60OI*x=T*lObkX#1-fEfq=rqF+Gx`5&uLO!z+xoywM$q;;xFEH+{wj`#n=XF^L8$#;TO#z6{nxRom53Mus2Bk`+741_eKa>;+lX-p!QKA0F@y{xt$k<w$4hLwU4YRe%)Tonx;POM;8Lc1n-grN%n%8}ZFyus3Oixyap>33u>Y^f~qS0;|WIA!6JfpkY%g1U<klAe=V8+rsmE;(4mmIiHNxkrXc)q*HPisLIL4xaonlCVQrSe{%MgC-axxd@_VtdrSb1OGHSRHg-RafJ8V7WnF#56Gk8nmg_{%PDE#+%qHyoprNJgf9VqzQ%T?u|Jv1A653&X>BqoV}5(d#q>9>w~0J67vVzN14-;Te!PjYiK##`mEkEGYf1dFNY+%s)Ha|p&3Cknhr=_a}Vg|i*T0yyS}EnvgBiB9w@^FswnRmXh>SjKWwA_8pGN4SDrfYU!O)BJ!ymfrCLFrN^Q=)RuEJ%F@}VVAQ%eIhByG`edH?DzcFfX%%9x9Ag@mZD+c)!X`09Y<zz62^&2C=yk5-cg?GYJkIRlLi8DL0G*>{N7#Rekgl^sNA@vbiS`dPZ!nGqT4F+6%^P~@0Q1c%Tlw|zOS&(&xI8k6Y3zk!$f#;r!Q8V=#=#b*^*n_qL^meig)Y=X6-7eaFj!^}P6Z8tep#64}3vMC!+>LZfewV|cu(EJ(8PcKx4-yrlmYqt;XxI-S0z-aL^7E#9gs`%F*$SBL2WX;j9e#bR!7iNi@!-S^<<MHHV$Tw`6FYe68f%*sx*hKzo{jJ-3`+|V=lju<SIE6mo#Ersp^=z)hm^>~et<<ZPzZO4#*kT_*B*WZEA=7Acd}q~SJ_Yryf<YsEP($Ax0D-=kf**dBv_D*$tC;gMHJz_R^Tli+oy}n3<2^aF%=lH99<6vFM1cF%NdqCJ{v2Cxhs*wNLgHc|1dpgcRd%mNFL&#Y%4yUDPxNeQDZ2$HvNo-JO{1;n?CoK;RR<~m^e82645H!=iD+H({IsQkT8^9sEzQu)=gjF(e^K<;*?HY$*3(Z6FlY(<qce7%u>MB$Vkmud`~;ZQSecg5c3T-op!)3-D1HYbOg9;yrD#HR7zFQJzdxD#e|MRG<IRoGqtd|cFIAq!g12-@_uohQRf+!_MmZ2^Bm+jbA;h7GhWlxBt*1HQ-&})BEilFge#zZVK{wS>(H3Luy3+me_)|iUd!&8A2(M#lNW|%Ep<J&1dFl?N4(cq0KXQ6@0w;z^eDP43=m?JX)aIi@?Wgy79N5dMARafy+67z@Y0q{T&W^KeU>^Np<Gtg^h_RP8+BlD=~S`m{IwH7iZ+EvDC%<(EAB08fhPgtB%eMhTp+qvQH9@r2<Mt*R_NIjOz?GE<P%@O432xGV7)+oG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+F=__file__+'.__decompressed__.py'
+C.cache[F]=(len(S),None,S.splitlines(True),F)
+exec(compile(S,F,'exec'))
diff --git a/records/track_10min_16mb/2026-04-12_SP8192_LegalSOTA_Clean/train_seed1337.log b/records/track_10min_16mb/2026-04-12_SP8192_LegalSOTA_Clean/train_seed1337.log
new file mode 100644
index 0000000000..e1b2a64336
--- /dev/null
+++ b/records/track_10min_16mb/2026-04-12_SP8192_LegalSOTA_Clean/train_seed1337.log
@@ -0,0 +1,277 @@
+W0412 04:57:35.750000 1777 torch/distributed/run.py:803] 
+W0412 04:57:35.750000 1777 torch/distributed/run.py:803] *****************************************
+W0412 04:57:35.750000 1777 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. 
+W0412 04:57:35.750000 1777 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
+  hash_embed_enabled: True
+  hash_embed_size: 16384
+  head_lr: 0.008
+  is_main_process: True
+  iterations: 20000
+  ln_scale: True
+  local_rank: 0
+  logfile: logs/daa165fe-62f5-44c7-9f7b-10d92ebec09c.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: 0.97
+  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: daa165fe-62f5-44c7-9f7b-10d92ebec09c
+  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: 0.01
+  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: 17603941
+2/20000 train_loss: 12.2673 train_time: 0.0m tok/s: 13040294
+3/20000 train_loss: 10.9224 train_time: 0.0m tok/s: 10729005
+4/20000 train_loss: 9.3858 train_time: 0.0m tok/s: 9811713
+5/20000 train_loss: 8.2725 train_time: 0.0m tok/s: 9334895
+500/20000 train_loss: 3.3833 train_time: 0.8m tok/s: 7821276
+1000/20000 train_loss: 3.2932 train_time: 1.7m tok/s: 7803444
+1500/20000 train_loss: 3.1922 train_time: 2.5m tok/s: 7799631
+2000/20000 train_loss: 3.1034 train_time: 3.4m tok/s: 7803281
+layer_loop:enabled step:2042 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: 3.1491 train_time: 4.6m tok/s: 7186166
+3000/20000 train_loss: 2.9161 train_time: 5.9m tok/s: 6721413
+3500/20000 train_loss: 2.9536 train_time: 7.1m tok/s: 6477927
+4000/20000 train_loss: 2.8244 train_time: 8.3m tok/s: 6306083
+4000/20000 val_loss: 2.8830 val_bpb: 1.1161
+4500/20000 train_loss: 2.8384 train_time: 9.5m tok/s: 6178152
+4603/20000 val_loss: 2.8044 val_bpb: 1.0857
+stopping_early: wallclock_cap train_time: 588166ms step: 4603/20000
+peak memory allocated: 39956 MiB reserved: 40024 MiB
+ema:applying EMA weights
+pre-quantization post-ema val_loss:2.80498827 val_bpb:1.08589837 eval_time:6389ms
+Serialized model: 135408623 bytes
+Code size: 20681 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: 15965942 bytes
+Total submission size quantized+brotli: 15986623 bytes
+quantized val_loss:2.83306033 val_bpb:1.09676594 eval_time:27828ms
+quantized_sliding_window val_loss:2.78916788 val_bpb:1.07977381 eval_time:123617ms
+ttt_sliding:start chunks=1238 chunk_tokens=32768 total_windows=633409 stride=64 ttt_lr=0.01 ttt_epochs=3 freeze_blocks=0 optimizer=sgd hash_embed=True
+ttt_sliding:params unfrozen=44333145 frozen=0
+  ttt_chunk [1/1238] bpb=1.117492 time=44.6s
+  ttt_chunk [11/1238] bpb=1.069226 time=68.8s
+  ttt_chunk [21/1238] bpb=1.106644 time=71.4s
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+  ttt_chunk [51/1238] bpb=1.086964 time=79.2s
+  ttt_chunk [61/1238] bpb=1.078842 time=81.8s
+  ttt_chunk [71/1238] bpb=1.086084 time=84.4s
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+  ttt_chunk [131/1238] bpb=1.080943 time=100.0s
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+legal_ttt_exact val_loss:2.78502089 val_bpb:1.07816838 eval_time:409073ms
diff --git a/records/track_10min_16mb/2026-04-12_SP8192_LegalSOTA_Clean/train_seed2024.log b/records/track_10min_16mb/2026-04-12_SP8192_LegalSOTA_Clean/train_seed2024.log
new file mode 100644
index 0000000000..0ace4c6470
--- /dev/null
+++ b/records/track_10min_16mb/2026-04-12_SP8192_LegalSOTA_Clean/train_seed2024.log
@@ -0,0 +1,277 @@
+W0412 05:50:09.342000 102461 torch/distributed/run.py:803] 
+W0412 05:50:09.342000 102461 torch/distributed/run.py:803] *****************************************
+W0412 05:50:09.342000 102461 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. 
+W0412 05:50:09.342000 102461 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
+  hash_embed_enabled: True
+  hash_embed_size: 16384
+  head_lr: 0.008
+  is_main_process: True
+  iterations: 20000
+  ln_scale: True
+  local_rank: 0
+  logfile: logs/e2760da8-eda7-4e13-9dc0-5c39d59325b1.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: 0.97
+  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: e2760da8-eda7-4e13-9dc0-5c39d59325b1
+  scalar_lr: 0.02
+  seed: 2024
+  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: 0.01
+  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: 9.0090 val_bpb: 3.4877
+1/20000 train_loss: 9.0109 train_time: 0.0m tok/s: 18188822
+2/20000 train_loss: 12.3317 train_time: 0.0m tok/s: 13237927
+3/20000 train_loss: 10.8866 train_time: 0.0m tok/s: 10881164
+4/20000 train_loss: 9.3331 train_time: 0.0m tok/s: 9922156
+5/20000 train_loss: 8.1813 train_time: 0.0m tok/s: 9411641
+500/20000 train_loss: 3.3970 train_time: 0.8m tok/s: 7833204
+1000/20000 train_loss: 3.2981 train_time: 1.7m tok/s: 7814169
+1500/20000 train_loss: 3.2011 train_time: 2.5m tok/s: 7814702
+2000/20000 train_loss: 3.1102 train_time: 3.4m tok/s: 7818458
+layer_loop:enabled step:2046 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: 3.1512 train_time: 4.6m tok/s: 7201379
+3000/20000 train_loss: 2.9222 train_time: 5.8m tok/s: 6798965
+3500/20000 train_loss: 2.9603 train_time: 7.0m tok/s: 6515300
+4000/20000 train_loss: 2.8311 train_time: 8.3m tok/s: 6338191
+4000/20000 val_loss: 2.8863 val_bpb: 1.1174
+4500/20000 train_loss: 2.8377 train_time: 9.5m tok/s: 6206764
+4621/20000 val_loss: 2.8054 val_bpb: 1.0861
+stopping_early: wallclock_cap train_time: 588106ms step: 4621/20000
+peak memory allocated: 39948 MiB reserved: 40026 MiB
+ema:applying EMA weights
+pre-quantization post-ema val_loss:2.80595007 val_bpb:1.08627071 eval_time:6889ms
+Serialized model: 135408623 bytes
+Code size: 20681 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: 15966086 bytes
+Total submission size quantized+brotli: 15986767 bytes
+quantized val_loss:2.83593750 val_bpb:1.09787978 eval_time:8573ms
+quantized_sliding_window val_loss:2.79149113 val_bpb:1.08067321 eval_time:92930ms
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+legal_ttt_exact val_loss:2.78654079 val_bpb:1.07875678 eval_time:329300ms
diff --git a/records/track_10min_16mb/2026-04-12_SP8192_LegalSOTA_Clean/train_seed42.log b/records/track_10min_16mb/2026-04-12_SP8192_LegalSOTA_Clean/train_seed42.log
new file mode 100644
index 0000000000..5ec3444288
--- /dev/null
+++ b/records/track_10min_16mb/2026-04-12_SP8192_LegalSOTA_Clean/train_seed42.log
@@ -0,0 +1,277 @@
+W0412 05:22:48.531000 98390 torch/distributed/run.py:803] 
+W0412 05:22:48.531000 98390 torch/distributed/run.py:803] *****************************************
+W0412 05:22:48.531000 98390 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. 
+W0412 05:22:48.531000 98390 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
+  hash_embed_enabled: True
+  hash_embed_size: 16384
+  head_lr: 0.008
+  is_main_process: True
+  iterations: 20000
+  ln_scale: True
+  local_rank: 0
+  logfile: logs/f6f9f80e-fa5e-4dd3-8d12-db8416800273.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: 0.97
+  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: f6f9f80e-fa5e-4dd3-8d12-db8416800273
+  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: 0.01
+  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: 18309697
+2/20000 train_loss: 12.3683 train_time: 0.0m tok/s: 13197164
+3/20000 train_loss: 10.9510 train_time: 0.0m tok/s: 10825484
+4/20000 train_loss: 9.3767 train_time: 0.0m tok/s: 9881885
+5/20000 train_loss: 8.2189 train_time: 0.0m tok/s: 9397963
+500/20000 train_loss: 3.3907 train_time: 0.8m tok/s: 7826157
+1000/20000 train_loss: 3.2817 train_time: 1.7m tok/s: 7807165
+1500/20000 train_loss: 3.1882 train_time: 2.5m tok/s: 7807765
+2000/20000 train_loss: 3.1061 train_time: 3.4m tok/s: 7814482
+layer_loop:enabled step:2045 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: 3.1432 train_time: 4.6m tok/s: 7197619
+3000/20000 train_loss: 2.9198 train_time: 5.8m tok/s: 6796670
+3500/20000 train_loss: 2.9567 train_time: 7.0m tok/s: 6517654
+4000/20000 train_loss: 2.8311 train_time: 8.3m tok/s: 6339273
+4000/20000 val_loss: 2.8849 val_bpb: 1.1168
+4500/20000 train_loss: 2.8384 train_time: 9.5m tok/s: 6207702
+4622/20000 val_loss: 2.8038 val_bpb: 1.0854
+stopping_early: wallclock_cap train_time: 588205ms step: 4622/20000
+peak memory allocated: 39948 MiB reserved: 40026 MiB
+ema:applying EMA weights
+pre-quantization post-ema val_loss:2.80433023 val_bpb:1.08564362 eval_time:6937ms
+Serialized model: 135408623 bytes
+Code size: 20681 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: 15962848 bytes
+Total submission size quantized+brotli: 15983529 bytes
+quantized val_loss:2.83257324 val_bpb:1.09657737 eval_time:8665ms
+quantized_sliding_window val_loss:2.78891465 val_bpb:1.07967577 eval_time:93228ms
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+legal_ttt_exact val_loss:2.78477126 val_bpb:1.07807174 eval_time:326570ms