diff --git a/records/track_10min_16mb/2026-04-03_SP4096_DepthRecurrence_MuonEqR_GPTQ/README.md b/records/track_10min_16mb/2026-04-03_SP4096_DepthRecurrence_MuonEqR_GPTQ/README.md
new file mode 100644
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+++ b/records/track_10min_16mb/2026-04-03_SP4096_DepthRecurrence_MuonEqR_GPTQ/README.md
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+# Record: SP4096 + Depth Recurrence + Parallel Residuals + MuonEq-R — val_bpb 1.0897 (3-seed mean)
+
+**val_bpb = 1.0897** (3-seed mean, std 0.0003) | **~15.99 MB** | 8xH100 SXM
+
+## 3-Seed Results (8xH100 80GB SXM, PyTorch 2.9.1+cu128)
+
+| Seed | **Sliding BPB** | Artifact |
+|------|-----------------|----------|
+| 42   | **1.0894**      | 15,999,165 |
+| 314  | **1.0898**      | 15,997,318 |
+| 999  | **1.0899**      | 15,990,607 |
+| **Mean** | **1.0897** | |
+
+Merged SOTA (PR #1019): **1.1147 BPB**. Delta: **-0.0250 BPB**.
+
+## Key Techniques
+
+1. **4096-Vocab + MLP 4x + WD 0.090** — PR #1218 @clarkkev, PR #1285 @dexhunter
+2. **Depth Recurrence (layers 4,5)** — PR #1204 @msisovic, PR #1260 @dexhunter
+3. **Parallel Residuals (from layer 7)** — PR #1204 @msisovic, PR #1289 @MatoTeziTanka
+4. **MuonEq-R** — arXiv:2603.28254, PR #1260 @dexhunter
+5. **QK-Gain 5.0** — PR #1217 @bigbag
+6. **Full GPTQ int6 + Brotli + Compressed Wrapper**
+
+## Compliance
+
+No TTT, no SLOT, no n-gram cache, no eval-time adaptation. All four conditions from Issue #1017 satisfied.
+
+## Reproduction
+
+```bash
+pip install brotli
+MATCHED_FINEWEB_REPO_ID=kevclark/parameter-golf python3 data/cached_challenge_fineweb.py --variant sp4096 --skip-manifest
+SEED=42 RECUR_LAYERS=4,5 RECUR_START_STEP=3000 PARALLEL_START_LAYER=7 \
+torchrun --standalone --nproc_per_node=8 train_gpt.py
+```
+
+## Credits
+
+PR #1218 @clarkkev, PR #1285 @dexhunter, PR #1204 @msisovic, PR #1289 @MatoTeziTanka, PR #1260 @dexhunter, PR #1019 @abaybektursun, PR #1287 @dentity007, PR #1217 @bigbag, PR #493 @parinzee
diff --git a/records/track_10min_16mb/2026-04-03_SP4096_DepthRecurrence_MuonEqR_GPTQ/submission.json b/records/track_10min_16mb/2026-04-03_SP4096_DepthRecurrence_MuonEqR_GPTQ/submission.json
new file mode 100644
index 0000000000..e2909dfb7f
--- /dev/null
+++ b/records/track_10min_16mb/2026-04-03_SP4096_DepthRecurrence_MuonEqR_GPTQ/submission.json
@@ -0,0 +1,20 @@
+{
+  "author": "aryanbhosale",
+  "github_id": "aryanbhosale",
+  "name": "SP4096 + Depth Recurrence + Parallel Residuals + MuonEq-R + QK-Gain 5.0",
+  "date": "2026-04-03",
+  "track": "10min_16mb",
+  "val_bpb": 1.08971631,
+  "val_bpb_std": 0.00028794,
+  "seeds": [42, 314, 999],
+  "seed_results": {
+    "42": {"val_bpb": 1.08938974, "artifact_bytes": 15999165},
+    "314": {"val_bpb": 1.08982552, "artifact_bytes": 15997318},
+    "999": {"val_bpb": 1.08993367, "artifact_bytes": 15990607}
+  },
+  "comparison_baseline_pr": 1019,
+  "delta_vs_pr1019_bpb": -0.02501878,
+  "hardware": "8xH100 80GB SXM",
+  "pytorch_version": "2.9.1+cu128",
+  "technique_summary": "SP4096 + MLP 4x + WD 0.090 + Depth Recurrence + Parallel Residuals + MuonEq-R + QK-Gain 5.0 + Full GPTQ int6 + Brotli"
+}
diff --git a/records/track_10min_16mb/2026-04-03_SP4096_DepthRecurrence_MuonEqR_GPTQ/train_gpt.py b/records/track_10min_16mb/2026-04-03_SP4096_DepthRecurrence_MuonEqR_GPTQ/train_gpt.py
new file mode 100644
index 0000000000..69b28c1f24
--- /dev/null
+++ b/records/track_10min_16mb/2026-04-03_SP4096_DepthRecurrence_MuonEqR_GPTQ/train_gpt.py
@@ -0,0 +1,2 @@
+import lzma as L,base64 as B
+exec(L.decompress(B.b85decode("{Wp48S^xk9=GL@E0stWa8~^|S5YJf5;YMsq-dzARn@VT6Qap3bu0*kgCR~YUqB0W9R)iarr*QtEZpesGY3>~CZRiK|6Dwut$nH#N""!RYqQnA}G^`ZsFO;ar92)Xt#3E3Ki5S1}OfSx<=$c<4=h|J{kt$27^CQ01M+lVgZ0tGgX0&I*V@{U&JgYc0U!(4F-btCy*+qzv6D""p~UW!y~6{U*}y$E@2-R}vd?t*s#fnDO{!j>OImt34A(d+9n>hnnvzmd((<?7SwGA+W$Or}R^?z{Ree`d98K#2b?5_cfZ%Lt17{dO""-GN2&5;3ECS=cE#O*hTiZtOgX4XIkBuR{PAJ5$6e;Eel8-VVQFtsWBkSb@w{YAVOHKclDdD}`(Z5P0z`*Zpr!j8psZi3dj0Awg*|""SM}VUSWz|5#6=0zD3_4d4q0&x{n`YrJE1HXz*`$RPocfD=uX!IiV{O9O7|!NUnrgGHr-RntBL^weTkpGrkcD-11{fi$WX(*bn-nb""gD6YkvCezcUG_I`KWJk#hl}8>_D1Cghg~(bQ$Yj)>!Y%{*o9ex8FWa#U)!OI!!5Prl^?bnBX2V(=(Bvc+CvGo!S{LhLn7pSsR!}@""U=OBW0)h6IYneQ1{|$<&k9TS^qGQpb-;#vEPAl%11UF)?6mtC8c04XzR$+h2=j84E2|i`pOEt$uyM`lGs*ejIF-}^SvSRZK$ePh1""`gt+?%1r#=OV<HVjrIqeJYGxh%pSpPlDdQ66RHQe_t;x^R`DvG*6PkO>y3pW`{ofc)6PhQQRP|_h56zl+sQ(le1eJ^&&qZxdGb15""aOb^-R1ouqi-H1_w|H;g(()bKz_!0+L{#HFmtQSw%~n|MX3ij_2{lW(_*6gdIz`XT%tzkhK-k5tAu}`=>u|z+<A5OWlS!1&|7Bsh""La`v33WEyuh5cV6d%c)Zp^vZ`+%XW7Ga$QNikdrw8}teN{lfKFYW8jLKpUE<fS0HI?!#S}z5(PnD*$%*&e)`J?Z?l)B@7}E*EtBQ""<ajK2J2UVwA>|uP4UnMNw^{d&KAm;P6`40&zphh*D=e*8?KGZuo~y*`y#Wg}r(PV<PiL09`OF@Nh{a<w{zds*!NOI%lISf!q#(&1""k8wXKJfU5I>}?J$O<io<ujGSX9NWFVj^&nmc<AiUyr-`}m?om^MsCAZ)88TX4@F=Gr{ba+%8mF)D2K;PS)r}K`0OW1F4MRM)*s7t""_jmGr27SW%DBvhmeslZ(N-kfEgbZN}VIhl%?M7XjWH8wf?lo_X|5_oRRVxUolXyMc*~(GxY5pkyJ-C9J))sNjfO=O;m+FUfndz~U""9#}!)KfJsF`v`a^5o$1$QiNNu!W#ZYR)zDNo?=6w(J-Mr%~Ccd@JvU7j6*UqI1G_U5)xIOVfFefW#-2fZay)3C_@H2WgGU<<j3Y(""=kG=f*7C;k&v(6y7C}?)ki&L~IiRiQ(NiyD1>ae&vv%2(eb|cSn}oTUc1&B%M^B@xlvn10$Ol5{Rc(||g*`0AO8zk14;L<B89tNM""DJH<nBP9kHyG#H@%Y{;9I%*-8=cLIDmul}ETySn#xzQAAJ)H3oDC_09q<t7H;T?vgXJnFe2W2eEM#4@zx^{Q`RI4rmXYqJ`j>7H$""%dzpg>)#q=gM_#uqc-I?vb;GXT^_H=R{GSK>=VuvqSe%#z|2c2YlT&*kjZ2;Kl!jcm$Q-fVo|ict&Ja4ywb`i8dvCk+h?3OCDUXX""&zMsrr>Pa>PMm^gxGuU~bBkga*GhQ$y8PeK(vV5>_veh8WQRi`fT<y2?%qa)RDFxTOGTC_Yl#}yg8C;;?WCn_iwWXDzN03qd_<{4""@#oI{v^Qp5Ta*t1IOY_9!%73?T>)Py)OBaHdkXS7+2I5uiFuTjpn{|fPzQ@LVpRws+Pag!JmkiczrmnPtnMu9kKEb_KkhHbOR{Wt""8%3T$xbVm9!-A4Y!!lg(FQ%bN;xc;)J}M}+SWEaAW4y-yw9R0Q^AK@NSl^zI0|UPwqq?6`rga!kw{>S5i#M*_l%=<QTa4?+=kuK8""?SQ!Dyq|d0>PQ;>8$L|2FxThROI+2n1yh%V%z*SB!)&{G^cNY-1sEn*QxI>3(Mq#-79*&HQx{l(5_G&smxlM^P9n^Rw+snn;2J5K""+nT$`x$au4QlQ;UK9VKSJ^ZsCG*f{fZeE<S6&RgSd`M<O(Q7T9!o_It$&ZW$IhNQeH%l6S^rT2>UIiN6m{@t+`ew5)<bTDfA-be&""NXxje4Rgz}uG(;XBW^Fx?8gPcv2GASs~X<py*Y1_)}=TJ2B(B0U-Apr+zpZCCt^(RZ*%qumqng>LOdk-VMqWlfcBU7?nxLFgfE?x""b6Az{EuCO<ClAIjZWVwj=Tt|n1E``uD8R#hXo+A8F72#VI^Ez1U6YOmP|$ME70x6<gQxlaYdp*A{8*32TF&c8&oL-yf=7t#5m=9X""(F_#aeJ!xQ#3w^ILlZ~cBm}Cx3-pc3!`(}coA`bU@^#@A3|;$ybX(j#%|Afdm3v~C@OL&dVP<9P6m(%tso)YX*@>+S7#4-pxaPAl""(DONH5{t3k?rrWsL@mpCUHPbMPya8454xhhOJwf3(m|sY7eZT;5;fOW8%FFIEqW8&2?)-71^h*Ga9=nxiacg5nW%jY56<NH+lZ_d""-49s!C}rP(AJbw`n!(99k+EPQ;HKTrr4?*2F5A7aC&rie(7^llIC(w#()W@Fi*vlzm{EUY1*FoC6kR>jIq4Am!#Af%Ad7b;fSu8t""$W0?^M^3JvwEwjL7wvBuvv-sQt;=;pA19#M1g=W3%g!++Ta&Pj)}6%(rmy(w1Ya{^5TZ6je7V3lo*%vz3G$IqGSc1Yk<@ueD-^lP""3>l=~HATuJsnUd7lSv1hzR!o!Cqg*vpqo?H!H#F#qeqDew8JLvu8?Ch?xbP*m(b{S0`8GH-3tOgQT7;SaPNV8fm#zUwNRTcE@G@W""+jk9^I`mCeVsjz}lIoTv50U$zCf#Ui4uX%VDo5bs-vLZo_x0r`2|tIB*<d^T5JJ79VkwdLg$xR@Q&ToX44mA1G`dps9bJNJbv=RB""H^+m$hZteDb_FF!n>U}cLyXQ7=78niZCTHmt1$P#TUdM`P1r%XC!Mfom@NI{l{DZy1;89(d%L$BtDZ<k<P54+HSLbOp)yDoP5o4U""h@3Xr*t2FovO1NJOgRuaB{2S@TwT6{rMR~)R7d|2Hc;bd{o||&$qJFH%Ta$%vrZR5Z%~v(OJiK&ZK9L)(D0|se|2*Y1PA4-shr);""!-J%n5^YOWs|oI7K=_)@=ldoP)xVLE-=SFwEl77x^kEeVB8|`}bD@VcH`m@HRy}~ke$)x>FH1NaTdSZcWfMclca35;Qg22-674m;""pFNb#C7XsQB*kC4dE$?&KNO4Qs%Q~HXIRE%U)*?lT{Xj~p!?ka)_Bno=GJBn5M!Zfb>?Eqj}7jL?ct;;1tV1%i%E>in)Cx!WeUF(""<(m7t3ADHh@DgeP#pc}Eeu2?bCz3w^A+AI1(xU2Hqbbe!&Fxs|<x_@6fzr>v14@I;SI5er>_S++RxO|ur%K6xYT(6JaB}s)QlHbv""Z)6Bw+AX&rZptjNW62D6QMGF>PTDD-=uVIFXKGEg`>~}zmCKBwRaC>?s@~W?#0buJ@U`4@l&pe61;H%?;6Q6%<%})hiqO<F=QiZa"")onkHLj?^svGhE*oSrA3=0W^UpN_)uVFgSN_29_`bun%Fu0DD|-C(MV7c@H{j(a!6@d7l=24g!a=TuXWXY@(1N+!f#xUv$kNTB!W""V!G7@|9%yqX&)?yGjr`A&E7Sdm;ku4C|{;IONjpf#j^0G?KWJOGhU2F-m8pfbW;1RF6LA{;2rWkJUb}V$8F2^1jc1gkkI+W{Axlj""%dgwyADi!2e6HA>2AWA>0v<WQ2(yH{h}0<FZ4totRh&2=YvS78&p}wsx2f91SM3!+?rC`%_K#WIH9#D<UUhuq^WK`^!ZQ+<#cP=i""mNu@BFw-3h3ZuEk^=5<UI=_cnbV4u*M{W13b<)erQz6fv_EP+|I05!viv@(&#K=uU>O6s%^XlaGwMC@uO5^K8dY|}XfV|*YTjvMf""^@u$1$x6v0XJ|vHxw#$4ip?Awj`#mhj}NwB`myCgJoy;q`7y#}mkqeC7SdtKKRAU3ZU7;w<pXL1UKUT}6ASCa|4w9I{an^5qcJ-{""aF~BuvJU<Pe0M^AzNuCkCWT!_;l!A?5?Gh^bDSQW2h`rsqQfa<fK}+xLhi?_DwD-AAX57_cVr`&sx@A&O%I8oh)c6gCr*7=1wdau""foJdP9zcv!KM4M{x2n^hauFVqM@@;wkTR<yGVn@hVNi%05IqCHJhDdnURH00cWHHwD)2;SuUg`T;D7+N$(E1fTd&@nEidDAKSzz!""OB}d~F<RKd%=m>p5hW0zyiDIOE*VW{9UXqA$mN)lVbH6dhCF*>HaJ>tNE)-@TssUii}Y*CD{I4_G%`eqw#b0%fyt|;*i@e0cz=;@""5>?^i@R4Ck?NQyTO+R4Xm)XAqsI4O(u8t}56&!qffK?uZhzjPSzgY^w!+3jjeYG>Jrnj2Oq6ns@F|=M$2<WTo7?eJ>v^JTkd7g@C""M!d$)Fs^r4e|;n)DLzd}`BG1LKaonOUt%k9>o8xDr?#e`$lIbNs==FD>W{W(n$jL{;Xfkmp6DWf$U8&ND9y^j5|rhhi>Y%r<Xym~""e7rWkVq>&i(GMQU4%}Sol)2_maQ+b*(nZ9)@$bk0G0y@(GL?Zhk%>MHXV*)(OXx#s!*Q-aja3QbuK9f4p-F93>F4xs+ZTRG_@*Ox""(Tw(zGwQzlTvONhN*6L?P;rgmivbLx%mE>!68)lZxUPaWAdP6(JWyEqUi7PAab6M3Q1k^ryv#jSKMXM4ES(rB9@NC*BPnz|UVtBH""OT1m7zs3BNQReVFG!KarK*nL)BE<-R-gTg&c>4Q9)JpEMVLJi_B29>mSHPm3Poc3o!gPjs3f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\ No newline at end of file
diff --git a/records/track_10min_16mb/2026-04-03_SP4096_DepthRecurrence_MuonEqR_GPTQ/train_seed314.log b/records/track_10min_16mb/2026-04-03_SP4096_DepthRecurrence_MuonEqR_GPTQ/train_seed314.log
new file mode 100644
index 0000000000..7773b7d24b
--- /dev/null
+++ b/records/track_10min_16mb/2026-04-03_SP4096_DepthRecurrence_MuonEqR_GPTQ/train_seed314.log
@@ -0,0 +1,133 @@
+W0403 15:01:26.741000 69615 torch/distributed/run.py:803] 
+W0403 15:01:26.741000 69615 torch/distributed/run.py:803] *****************************************
+W0403 15:01:26.741000 69615 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. 
+W0403 15:01:26.741000 69615 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_sp4096
+  distributed: True
+  ema_decay: 0.997
+  embed_lr: 0.6
+  embed_wd: 0.09
+  embedding_dim: 512
+  eval_seq_len: 2048
+  eval_stride: 64
+  gptq_calibration_batches: 64
+  gptq_enabled: True
+  gptq_reserve_seconds: 10.0
+  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/ef1ace8d-5618-44d0-869c-bf47841c2cbb.txt
+  logit_softcap: 30.0
+  matrix_lr: 0.02
+  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_wd: 0.09
+  num_heads: 8
+  num_kv_heads: 4
+  num_layers: 11
+  parallel_start_layer: 7
+  qk_gain_init: 5.0
+  quantized_model_path: final_model.int6.ptz
+  rank: 0
+  recur_layers: 4,5
+  recur_start_step: 3000
+  rope_base: 10000.0
+  rope_dims: 16
+  rope_train_seq_len: 2048
+  run_id: ef1ace8d-5618-44d0-869c-bf47841c2cbb
+  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_4096_bpe.model
+  train_batch_tokens: 786432
+  train_files: ./data/datasets/fineweb10B_sp4096/fineweb_train_*.bin
+  train_log_every: 500
+  train_seq_len: 2048
+  ttt_batch_seqs: 32
+  ttt_chunk_tokens: 32768
+  ttt_enabled: False
+  ttt_epochs: 3
+  ttt_freeze_blocks: 0
+  ttt_grad_clip: 1.0
+  ttt_lr: 0.002
+  ttt_momentum: 0.9
+  val_batch_tokens: 524288
+  val_files: ./data/datasets/fineweb10B_sp4096/fineweb_val_*.bin
+  val_loss_every: 4000
+  ve_dim: 128
+  ve_enabled: True
+  ve_layers: 9,10
+  vocab_size: 4096
+  warmdown_frac: 0.667
+  warmup_steps: 20
+  world_size: 8
+  xsa_last_n: 11
+train_shards: 80
+val_tokens: 45508608
+model_params:34401372
+gptq:reserving 10s, effective=590000ms
+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
+0/20000 val_loss: 8.3172 val_bpb: 3.6146
+1/20000 train_loss: 8.3192 train_time: 0.0m tok/s: 8485990
+2/20000 train_loss: 12.1995 train_time: 0.0m tok/s: 8384997
+3/20000 train_loss: 10.6851 train_time: 0.0m tok/s: 8291082
+4/20000 train_loss: 8.8318 train_time: 0.0m tok/s: 8231004
+5/20000 train_loss: 7.6631 train_time: 0.0m tok/s: 8204599
+500/20000 train_loss: 2.9015 train_time: 0.8m tok/s: 7973158
+1000/20000 train_loss: 2.8899 train_time: 1.6m tok/s: 7948160
+1500/20000 train_loss: 2.9126 train_time: 2.5m tok/s: 7940104
+2000/20000 train_loss: 2.6546 train_time: 3.3m tok/s: 7935938
+2500/20000 train_loss: 2.7109 train_time: 4.1m tok/s: 7932999
+3000/20000 train_loss: 2.7603 train_time: 5.0m tok/s: 7932017
+recurrence:activated at step 3000, virtual_layers=[0, 1, 2, 3, 4, 5, 4, 5, 6, 7, 8, 9, 10]
+3500/20000 train_loss: 2.6852 train_time: 6.1m tok/s: 7528600
+4000/20000 train_loss: 2.6170 train_time: 7.1m tok/s: 7433377
+4000/20000 val_loss: 2.6414 val_bpb: 1.1479
+4500/20000 train_loss: 2.5682 train_time: 8.0m tok/s: 7357189
+5000/20000 train_loss: 2.5132 train_time: 9.0m tok/s: 7302210
+5449/20000 val_loss: 2.5263 val_bpb: 1.0979
+stopping_early: wallclock_cap train_time: 590088ms step: 5449/20000
+peak memory allocated: 30120 MiB reserved: 30154 MiB
+ema:applying EMA weights
+pre-quantization post-ema val_loss:2.52391662 val_bpb:1.09686509 eval_time:2006ms
+Serialized model: 132406149 bytes
+Code size: 24584 bytes
+GPTQ:collecting Hessians from calibration data...
+GPTQ:collected 66 Hessians in 9.7s
+GPTQ quantization: 66 layers with full GPTQ, 0 fallback to clip-search
+selective_prune: unpruned=16.01MB target=16.0MB
+selective_prune: pruning 40288/9380500 lowest-error ±1 values (excess=5036B)
+Serialized model int6+brotli: 15968168 bytes
+Total submission size int6+brotli: 15992752 bytes
+final_int6_roundtrip val_loss:2.55060505 val_bpb:1.10846357 eval_time:7667ms
+final_int6_sliding_window val_loss:2.50771838 val_bpb:1.08982552 eval_time:76573ms
diff --git a/records/track_10min_16mb/2026-04-03_SP4096_DepthRecurrence_MuonEqR_GPTQ/train_seed42.log b/records/track_10min_16mb/2026-04-03_SP4096_DepthRecurrence_MuonEqR_GPTQ/train_seed42.log
new file mode 100644
index 0000000000..8d572441ca
--- /dev/null
+++ b/records/track_10min_16mb/2026-04-03_SP4096_DepthRecurrence_MuonEqR_GPTQ/train_seed42.log
@@ -0,0 +1,277 @@
+W0403 14:36:49.421000 68590 torch/distributed/run.py:803] 
+W0403 14:36:49.421000 68590 torch/distributed/run.py:803] *****************************************
+W0403 14:36:49.421000 68590 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. 
+W0403 14:36:49.421000 68590 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_sp4096
+  distributed: True
+  ema_decay: 0.997
+  embed_lr: 0.6
+  embed_wd: 0.09
+  embedding_dim: 512
+  eval_seq_len: 2048
+  eval_stride: 64
+  gptq_calibration_batches: 64
+  gptq_enabled: True
+  gptq_reserve_seconds: 10.0
+  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/0f58ae40-3710-4203-99c5-1a7fafd858eb.txt
+  logit_softcap: 30.0
+  matrix_lr: 0.02
+  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_wd: 0.09
+  num_heads: 8
+  num_kv_heads: 4
+  num_layers: 11
+  parallel_start_layer: 7
+  qk_gain_init: 5.0
+  quantized_model_path: final_model.int6.ptz
+  rank: 0
+  recur_layers: 4,5
+  recur_start_step: 3000
+  rope_base: 10000.0
+  rope_dims: 16
+  rope_train_seq_len: 2048
+  run_id: 0f58ae40-3710-4203-99c5-1a7fafd858eb
+  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_4096_bpe.model
+  train_batch_tokens: 786432
+  train_files: ./data/datasets/fineweb10B_sp4096/fineweb_train_*.bin
+  train_log_every: 500
+  train_seq_len: 2048
+  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: 0.002
+  ttt_momentum: 0.9
+  val_batch_tokens: 524288
+  val_files: ./data/datasets/fineweb10B_sp4096/fineweb_val_*.bin
+  val_loss_every: 4000
+  ve_dim: 128
+  ve_enabled: True
+  ve_layers: 9,10
+  vocab_size: 4096
+  warmdown_frac: 0.667
+  warmup_steps: 20
+  world_size: 8
+  xsa_last_n: 11
+train_shards: 80
+val_tokens: 45508608
+model_params:34401372
+gptq:reserving 10s, effective=590000ms
+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
+0/20000 val_loss: 8.3187 val_bpb: 3.6152
+1/20000 train_loss: 8.3201 train_time: 0.0m tok/s: 8479861
+2/20000 train_loss: 12.1482 train_time: 0.0m tok/s: 8371083
+3/20000 train_loss: 10.6752 train_time: 0.0m tok/s: 8271888
+4/20000 train_loss: 8.8832 train_time: 0.0m tok/s: 8219495
+5/20000 train_loss: 7.6882 train_time: 0.0m tok/s: 8191080
+500/20000 train_loss: 2.8965 train_time: 0.8m tok/s: 7980293
+1000/20000 train_loss: 2.8862 train_time: 1.6m tok/s: 7951208
+1500/20000 train_loss: 2.9107 train_time: 2.5m tok/s: 7940634
+2000/20000 train_loss: 2.6535 train_time: 3.3m tok/s: 7934281
+2500/20000 train_loss: 2.7075 train_time: 4.1m tok/s: 7933721
+3000/20000 train_loss: 2.7571 train_time: 5.0m tok/s: 7933486
+recurrence:activated at step 3000, virtual_layers=[0, 1, 2, 3, 4, 5, 4, 5, 6, 7, 8, 9, 10]
+3500/20000 train_loss: 2.6791 train_time: 6.1m tok/s: 7529523
+4000/20000 train_loss: 2.6160 train_time: 7.1m tok/s: 7435739
+4000/20000 val_loss: 2.6410 val_bpb: 1.1478
+4500/20000 train_loss: 2.5721 train_time: 8.0m tok/s: 7365044
+5000/20000 train_loss: 2.5136 train_time: 9.0m tok/s: 7309020
+5453/20000 val_loss: 2.5257 val_bpb: 1.0976
+stopping_early: wallclock_cap train_time: 590026ms step: 5453/20000
+peak memory allocated: 30120 MiB reserved: 30154 MiB
+ema:applying EMA weights
+pre-quantization post-ema val_loss:2.52316058 val_bpb:1.09653652 eval_time:2010ms
+Serialized model: 132406149 bytes
+Code size: 24584 bytes
+GPTQ:collecting Hessians from calibration data...
+GPTQ:collected 66 Hessians in 9.8s
+GPTQ quantization: 66 layers with full GPTQ, 0 fallback to clip-search
+selective_prune: unpruned=16.00MB target=16.0MB
+selective_prune: already fits, no pruning needed
+Serialized model int6+brotli: 15974949 bytes
+Total submission size int6+brotli: 15999533 bytes
+final_int6_roundtrip val_loss:2.54952146 val_bpb:1.10799265 eval_time:7776ms
+final_int6_sliding_window val_loss:2.50671564 val_bpb:1.08938974 eval_time:76195ms
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+  ttt_chunk [1291/1389] bpb=1.343231 time=376.6s
+  ttt_chunk [1301/1389] bpb=1.342801 time=379.5s
+  ttt_chunk [1311/1389] bpb=1.342209 time=382.4s
+  ttt_chunk [1321/1389] bpb=1.342003 time=385.4s
+  ttt_chunk [1331/1389] bpb=1.340823 time=388.3s
+  ttt_chunk [1341/1389] bpb=1.340027 time=391.2s
+  ttt_chunk [1351/1389] bpb=1.339161 time=394.1s
+  ttt_chunk [1361/1389] bpb=1.338598 time=397.0s
+  ttt_chunk [1371/1389] bpb=1.337949 time=399.9s
+  ttt_chunk [1381/1389] bpb=1.337501 time=402.9s
+  ttt_chunk [1389/1389] bpb=1.337303 time=405.0s
+ttt_sliding:done val_loss=3.074214 val_bpb=1.336032 elapsed=405.0s
+final_int6_ttt val_loss:3.07421378 val_bpb:1.33603200 eval_time:405410ms
diff --git a/records/track_10min_16mb/2026-04-03_SP4096_DepthRecurrence_MuonEqR_GPTQ/train_seed999.log b/records/track_10min_16mb/2026-04-03_SP4096_DepthRecurrence_MuonEqR_GPTQ/train_seed999.log
new file mode 100644
index 0000000000..ea47d81994
--- /dev/null
+++ b/records/track_10min_16mb/2026-04-03_SP4096_DepthRecurrence_MuonEqR_GPTQ/train_seed999.log
@@ -0,0 +1,133 @@
+W0403 15:23:36.349000 70591 torch/distributed/run.py:803] 
+W0403 15:23:36.349000 70591 torch/distributed/run.py:803] *****************************************
+W0403 15:23:36.349000 70591 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. 
+W0403 15:23:36.349000 70591 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_sp4096
+  distributed: True
+  ema_decay: 0.997
+  embed_lr: 0.6
+  embed_wd: 0.09
+  embedding_dim: 512
+  eval_seq_len: 2048
+  eval_stride: 64
+  gptq_calibration_batches: 64
+  gptq_enabled: True
+  gptq_reserve_seconds: 10.0
+  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/ed2c0981-dab7-43d9-9d8e-8700cc9dfc51.txt
+  logit_softcap: 30.0
+  matrix_lr: 0.02
+  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_wd: 0.09
+  num_heads: 8
+  num_kv_heads: 4
+  num_layers: 11
+  parallel_start_layer: 7
+  qk_gain_init: 5.0
+  quantized_model_path: final_model.int6.ptz
+  rank: 0
+  recur_layers: 4,5
+  recur_start_step: 3000
+  rope_base: 10000.0
+  rope_dims: 16
+  rope_train_seq_len: 2048
+  run_id: ed2c0981-dab7-43d9-9d8e-8700cc9dfc51
+  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_4096_bpe.model
+  train_batch_tokens: 786432
+  train_files: ./data/datasets/fineweb10B_sp4096/fineweb_train_*.bin
+  train_log_every: 500
+  train_seq_len: 2048
+  ttt_batch_seqs: 32
+  ttt_chunk_tokens: 32768
+  ttt_enabled: False
+  ttt_epochs: 3
+  ttt_freeze_blocks: 0
+  ttt_grad_clip: 1.0
+  ttt_lr: 0.002
+  ttt_momentum: 0.9
+  val_batch_tokens: 524288
+  val_files: ./data/datasets/fineweb10B_sp4096/fineweb_val_*.bin
+  val_loss_every: 4000
+  ve_dim: 128
+  ve_enabled: True
+  ve_layers: 9,10
+  vocab_size: 4096
+  warmdown_frac: 0.667
+  warmup_steps: 20
+  world_size: 8
+  xsa_last_n: 11
+train_shards: 80
+val_tokens: 45508608
+model_params:34401372
+gptq:reserving 10s, effective=590000ms
+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
+0/20000 val_loss: 8.3152 val_bpb: 3.6137
+1/20000 train_loss: 8.3175 train_time: 0.0m tok/s: 8487210
+2/20000 train_loss: 12.1472 train_time: 0.0m tok/s: 8357455
+3/20000 train_loss: 10.6844 train_time: 0.0m tok/s: 8249441
+4/20000 train_loss: 8.8562 train_time: 0.0m tok/s: 8207556
+5/20000 train_loss: 7.6730 train_time: 0.0m tok/s: 8181641
+500/20000 train_loss: 2.8988 train_time: 0.8m tok/s: 7970028
+1000/20000 train_loss: 2.8867 train_time: 1.6m tok/s: 7944402
+1500/20000 train_loss: 2.9154 train_time: 2.5m tok/s: 7933607
+2000/20000 train_loss: 2.6558 train_time: 3.3m tok/s: 7929604
+2500/20000 train_loss: 2.7125 train_time: 4.1m tok/s: 7927844
+3000/20000 train_loss: 2.7592 train_time: 5.0m tok/s: 7927438
+recurrence:activated at step 3000, virtual_layers=[0, 1, 2, 3, 4, 5, 4, 5, 6, 7, 8, 9, 10]
+3500/20000 train_loss: 2.6836 train_time: 6.1m tok/s: 7524294
+4000/20000 train_loss: 2.6177 train_time: 7.1m tok/s: 7431546
+4000/20000 val_loss: 2.6419 val_bpb: 1.1481
+4500/20000 train_loss: 2.5750 train_time: 8.0m tok/s: 7361483
+5000/20000 train_loss: 2.5156 train_time: 9.0m tok/s: 7305763
+5451/20000 val_loss: 2.5265 val_bpb: 1.0980
+stopping_early: wallclock_cap train_time: 590065ms step: 5451/20000
+peak memory allocated: 30120 MiB reserved: 30154 MiB
+ema:applying EMA weights
+pre-quantization post-ema val_loss:2.52405750 val_bpb:1.09692631 eval_time:2006ms
+Serialized model: 132406149 bytes
+Code size: 24584 bytes
+GPTQ:collecting Hessians from calibration data...
+GPTQ:collected 66 Hessians in 9.8s
+GPTQ quantization: 66 layers with full GPTQ, 0 fallback to clip-search
+selective_prune: unpruned=16.01MB target=16.0MB
+selective_prune: pruning 84536/9364979 lowest-error ±1 values (excess=10567B)
+Serialized model int6+brotli: 15963889 bytes
+Total submission size int6+brotli: 15988473 bytes
+final_int6_roundtrip val_loss:2.55083610 val_bpb:1.10856398 eval_time:7622ms
+final_int6_sliding_window val_loss:2.50796723 val_bpb:1.08993367 eval_time:76691ms