openai · G3sparky · Apr 18, 2026 · Apr 18, 2026 · Copilot · Apr 18, 2026
diff --git a/...k_10min_16mb/2026-04-18_QK525to55_DepthRecur_ParResid_SP8192_LegalTTT/README.md b/...k_10min_16mb/2026-04-18_QK525to55_DepthRecur_ParResid_SP8192_LegalTTT/README.md
@@ -0,0 +1,65 @@
+# Record: QK-Gain 5.5 + SP8192 + 3-Layer Recurrence + Parallel Residuals + Legal TTT
+
+**val_bpb = 1.0810** (3-seed mean, std 0.0005) | **< 16 MB** | 8xH100 SXM
+
+## 3-Seed Results
+
+| Seed | **TTT BPB** | Artifact |
+|------|-------------|----------|
+| 42   | **1.0804**  | 15,994,470 |
+| 314  | **1.0812**  | 15,993,777 |
+| 999  | **1.0814**  | 15,991,277 |
+| **Mean** | **1.0810** | |
+| **Std** | **0.0005** | |
+
+## Key Change
+
+**QK_GAIN_INIT=5.5** (up from 5.25). The monotonic improvement trend in query-key gain scaling continues past 5.25. This extends the finding from PR #1394 (@clarkkev) which documented improvement from 4.0 to 5.25.
+
+## Base Architecture
+
+Built on the SOTA foundation from:
+- **@clarkkev** — SP8192 + GPTQ SDClip + MuonEq-R + depth recurrence (PR #1394)
+- **@dexhunter** — 3-layer depth recurrence (PR #1331, #1437), legal TTT on SP8192 (PR #1413)
+- **@abaybektursun** — Score-first TTT framework (PR #549)
+- **@Robby955** — Parallel residuals on SP8192 (PR #1412)
+- **@msisovic** — Parallel residuals concept (PR #1204)
+- **@X-Abhishek-X** — Hyperparameter tuning (PR #1445, #1471)
+
+## Architecture
+
+11L x 512d x 8H / 4KV, MLP 4x, LeakyReLU(0.5)^2, Partial RoPE (16/64 dims), layerwise LN scale, tied embeddings, logit softcap=30.0. Depth recurrence: layers 3-5 loop (num_loops=2, activated at frac=0.35). Parallel residuals from layer 7. Skip gates (sigmoid-gated U-Net connections). XSA on all layers.
+
+## Training
+
+~4600 steps in ~588s on 8xH100 SXM. EMA decay 0.9965. Warmdown frac 0.72. WD=0.095. MuonEq-R (row-normalized, Newton-Schulz 5 steps).
+
+## Quantization
+
+Full-Hessian GPTQ with SDClip: int6 for attention/MLP matrices, int8 for token embeddings. Brotli-11 compression.
+
+## TTT (Test-Time Training)
+
+Legal score-first TTT: SGD (lr=0.005, momentum=0.9), 3 epochs per 32K-token chunk, cosine LR decay. Each chunk scored under `torch.no_grad()` before any SGD update. Each token scored exactly once.
+
+## Compliance
+
+Per Issue #1017 (Track B — legal eval-time adaptation):
+- Condition 1 (Causality): Sliding-window eval is strictly causal
+- Condition 2 (Normalized distribution): Standard softmax over full vocab
+- Condition 3 (Score before update): Each chunk scored under torch.no_grad() before SGD update
+- Condition 4 (Single pass): Each token scored exactly once
+- All artifacts under 16,000,000 bytes on all 3 seeds
+- Training under 600s on all 3 seeds (~588s actual)
+- Eval (sliding + TTT) under 600s on all 3 seeds
+
+## Reproduction
+
+```bash
+pip install brotli sentencepiece
+pip install flash_attn_3 --no-deps --find-links https://windreamer.github.io/flash-attention3-wheels/cu128_torch291/
+MATCHED_FINEWEB_REPO_ID=kevclark/parameter-golf python3 data/cached_challenge_fineweb.py --variant sp8192
+
+SEED=42 QK_GAIN_INIT=5.5 TTT_ENABLED=1 TTT_LR=0.005 TTT_EPOCHS=3 \
+  torchrun --standalone --nproc_per_node=8 train_gpt.py
+```
diff --git a/...track_10min_16mb/2026-04-18_QK525to55_DepthRecur_ParResid_SP8192_LegalTTT/submission.json b/...track_10min_16mb/2026-04-18_QK525to55_DepthRecur_ParResid_SP8192_LegalTTT/submission.json
@@ -0,0 +1,15 @@
+{
+  "val_bpb_mean": 1.08102,
+  "val_bpb_std": 0.00051,
+  "seeds": {
+    "42": {"val_bpb": 1.08044, "artifact_bytes": 15994470},
+    "314": {"val_bpb": 1.08120, "artifact_bytes": 15993777},
+    "999": {"val_bpb": 1.08141, "artifact_bytes": 15991277}
+  },
+  "hardware": "8xH100 80GB SXM",
+  "training_time_seconds": 588,
+  "eval_method": "sliding_window + legal_ttt",
+  "key_change": "QK_GAIN_INIT=5.5 (up from 5.25)",
+  "base": "SP8192 + 3-Layer Recurrence + Parallel Residuals + Legal TTT (PR #1394, #1331, #1412, #549)",
+  "author": "G3sparky (Gavin Saunders)"
+}
diff --git a/...ds/track_10min_16mb/2026-04-18_QK525to55_DepthRecur_ParResid_SP8192_LegalTTT/train_gpt.py b/...ds/track_10min_16mb/2026-04-18_QK525to55_DepthRecur_ParResid_SP8192_LegalTTT/train_gpt.py
@@ -0,0 +1,2 @@
+import lzma as L,base64 as B
+exec(L.decompress(B.b85decode(";Jw{5fL#DIn@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$y02ziZN=i)3}Qx?Dtm=+{LAgGTC@~>c^ys%R{D_%yAk9-_tV7^coUOo3$w>`(`ci)t`2F7>r>Ltx>>S2CRw|7ov>Wn1e~_!RLQ=%V9g?)G3yPsu%SBy!lj1PaC-x%dDmCDOZ^r^!)+WWz}ejKXTJ#^U6Ra!};QocHHXQC+4UM!QQ!-N5Xd|%~a(9)bTYIO+>B~8~@lqmri%^qEkQUy074Rh6w7V_#^s9J-3BNA`G;qyR$LYcI?e+loZVWi~B$n=TKFp{%SeHYp{oNWh;U@Ahk8M2$OU%K8B$lb*dRQXd-GR_@*KAZdRdwSd#v=LSq1v@Puul=a7WXDmh1^kBj}Y2XlER!D2E{&{%lV(hz$#n5%+%sk&Q}>{y0xpRgiQQBJeVV0hy8UD3ntyo@(Pv+K7^zVRDt4bah<BW;bd+Cav&<Xg-~v4E$T<1_zFr^A}Ais)|vX*pO`3FxNShvEi59luzir1VmzF1ek|5CGkl?5qc}ko)L1|6lu6I`m+*bf%)mk)RQ$_JmfE%eS#TV<mEIvI}8;<$6J^=w~cJs5Q7=n#vHa4v1R&1_9=;Q2@QfQORDBW6CkyC4%tXHv%r3D5rF;6FZ>(r8kfsZThb+H1)~K-lIr4`|V#-2R>G7pP*N!fwWd&Dq8C)y=NrG_U_Oz6Q?+@ok1?(VJ5?ZT~&}C4Ks38WRB>3i=I!}H-8qq=&yKJ;tbpwwn~lAseD^q1C*u5T;l<jmq^*|gW0-BBpM5(&T>KQtF;?zv@u0f36%6SXU~txi3v5iSPK*`fNE9531KaQDL`zTPF$MX4U(-3sY-&?>QJe)giBQzpor7H)AZ#4=Hn#`AoAL7tT){&bw(fgz|eQRt`#6-<>;m*+&$!nf|od6&lVKYYHuOoNgZU_L>E@!O%__mlt=);Hwdc43+CM?sh5y+my3XSVYMO8F1pXuq$fvTU<$mpDjr>Lm){DeV)>4AKAhA?jxjH<-3yYQ<QU|mI~;=9eDwNwPw;)v(Mu^%YrcdcwEH`*s$RtDvffGRv||Tctzy{1@`75RzWmpHgSnSab&b&Fyos-0wdom9#6ey3CnhkwfuHfVJc+7~(p~gN6Q?zV1K>#5qz+4c`Utifny+Ydmr4?c_z60#9@FU+U1&O$Lfg$WrX7gCj50O1t`1A`k04LVr;^*~{|@(TS5>#TAjL(B`umc8bVA$bS|F?^2A7E}z7IIgZlY(8Ex#K+nLh0vzlKK=74U!g+sX4T?e3_^_7XB1A(HB{pYd{vHYcak_P3DZ2LAB20wAP+C_9p7R|0}wA=p~JFi&xD8H}n(LxCc5rcmw<AY&of601+p3__P4;S)9wD!!2(*apEWmyI^3Nlf>F`!s(tSf_<O3Y4@Lb^pR}8!JWi5F-x7_`)oM#pVlXHEPzi0_w{=OcHSWtLTLJZ%v~xfE-VLIhk+V3}!ek?mRs^iiZfQr<Cz8wcNzPD3j5B{~Al3+P&zpBEN%rOTMb?hCP$lA?OeffC?6d+83}cL8pBK3e!YNJ~9kmX-Ex)_GbEil+L!j=<aZeLrs`Qx2fCt`V(`tk{k`tP!^_a&cJLbp-5%L!|FGej~o0jPXI6XZYF*KMbV3}lTx@Ln<Lua-O#Rd6MFf|hg{hiGOq{aNC;vw8&q5AhpiiicR}mCe^oxpwpei+d|T&qL>l_TXk(cPZJ_z`)iV4#r^gzawYQ%HE1iaUF=(KAcKXE%%6Hx0i;?;p1w#dN7!-y!(2GUw()t4|BXt%+05bu$y<wAnZSUY=-zn2daV7XQ!FRQ&E39a_e-evNQeH$%?k$j{<f2ReW`))eJS=r+^AO%o{S-|mcA#-J)~|Na8W7N&x7Y%qc}W|PQU3N_33v}9rHTp1gPXKFiZ=ghykdcGL*?F!Cgp^gGOHsjlG6z&`N$_rfTirGgHnbxUmK`ZYAYjsz^O;`V+8FnR!3RjxgtUMj}@iffiyK*40=kBdOKbvmxC&OtL?AD%tI-f#Ue!e@rkDfVbz!EEW?KyfWIX?60i{``J&VX2%Z3u0DFujf-mL7MLYzyLu&oz@SWnnB8as3Q2~X{m^08~{^CZm!I=WZQ(|Ri`w4QD6oqUIp&ghyb1r3)XQIiwz*99LjnA%97e+<(Dy<%Le~b6b^n_z}O<Z)T6;z(P@UO8JGf2q~fNKR$4R3eV1RzaSj7^{@Y+^a&@GhCY(7b`{jL2ry%;_^Q!l>ea+{f!deuk%(g-o}&XEEWm!lO+1^On#i#4rhP{bDYb9ZnbGd5n{*P->hZxI<{=3c-92I#g*mTey8O>cuw%hdwjB!#=GH_0?hY|Lf@L$0Qp+k03PROh)o-cMOQ!b>qfvPNJLTVvCBX24BGgI=_|35Bd%&Vq&!LWECF4!1&J?@uRfe=N2mi{l-S0aW101I*cY_A&2R~zfij0IZST;@;xJYti>{)weL@A2ZOGrq(U-ibnWz0BL;s!=S<i~82|F>;`!K6@M501z-dU(OqY0?!!!Tk6Z{9!iH*tDZsjYG`J8UEqJz~7cNEPh1A#iz_$2*Nxo;S{UehRA^{Mf3GV^9hBEKSL(iD!=aKpjYCTqmhYA|h4zASL-v?9UWx8tzm#N5eHo2h3w*`(kHM2e}vDviz3$~a(Y#jlJL?*}m9&(Oqs1+CNUy7g~Z#lRN#>g9{7u~tot;|0qTu4G4xwdXk3eT+l1l$)Vq%}j^^1b(jIvF|OcNb1Jz&)>b)qGiC5P7yS}AvC}VDK<woZNv4t;T(e%=S+yy9Wl!Mk?)7e{+73*u$1ks?HX%Es<Jb3p=`hTp!8!8l%0)~O(9avW&Tp9s8lf<<!52RHDrrWVny4z`ftyF2ZJ6}qhUWU$@+y3>ORD$#^Ydjg!zDuM#$J+k<}O|o#9dvGrp)*yShv3->joMiF%~orV4^0cl9F!@VqwD`5f<QPLctdikXXI0rQ$mAAaeO)6}Zwp1jH%fdXVoxoZ`z5lV>jekV@3E`STlX!=JDxbOQiv?Jp)$Xy|Z>g)@q_QYKopPeu&ghhPNw0y&{j?$GHwDQoztHvVU)a0ca6}7{#3^KK1uTcBkMSF$IDQp#Nhy>JTHPK2w+%N#FZ(D)=sw?BkfduBK{Owa(SkBq^_S*|NP@DI(VEWNqETjYsZ@bch5-dlWjP>|)xv+AknhsqQ!j3!=TT%CYvl>o#XU4AApoVBJ;db<uv@;Zle>=W0m0#FH7by8-Z*V~$!QptJuPqLkH-bA#`L?*g#-60qO9x7)rWh{~YY77{NX_v_!Mc-#`(n{>OHy|HxotTLyFAdqCe^bQsveKyNdxf%^ECJD<Wa?}FX&QnOVoQL$TZ#&f##R{pUuK(_{#}CVvVm7@+&m{N8R2nI$9!ALHZPv*Rk%b#hP48Q>w1jQaV{3doP1nC-IuYoJBS)BjwI+@*WRZpBQq--|WAHx2WWVue@lE`*T9AY1=3wKyIT}9Ss;d##=nZ>!%19!lx_0W91se3dXzq5oIE}=)Lf4xkby*McKg=z&Qh>gK5l~kV<Y3G|XD^O|uI3K$}GY)tl)92}xD#u_|>7t^lQK_s8TXQqiwiAz+UT7qOmRWvI~~2y<G+o%s+*8rsYGCml^rm)<LgsXmv>t~5_%swZRW#&SB~-uWRFRPsi5WZAzJp1&o@T%9?d1EUEpyP1v5zSN9`Nzg4<9J>%D?ZP~-T(dwGEKqZMPuhgN<|K<!knseUP=32Qh=k%@&WeMpwkJyP5WK?vq@D7&kY*+seMo3rb+KxP@Zkyra8qm)C>igW>x{H0%t_&ya;8v^0F=-)sK*R85LA|5>|ZYqA#XmVbFc92&H9WjeO5C!FX%LsiXg6}0#l(Pg(=6hjd7H_7$6NLIA+rCa;GE_3R75D#&J(7=>z|LN$87?M}UpavJo5jeYlJy>3UxeQ{duojamIjZmWv|*!Tjr5rT-K7C#w~_vZ!oIz>O;(D%nYcBK)`IjO`=SDZo*4vJ4V2bFcFg(2@0lt|4uUCPbO&N6^dv4y}sPBwT(0$|M|*?y;Jv@#8^JCr!hxD0=c#R8ALJkOUZ5;?_TS5GI^kyB>q;{eo<-=N|;JU?~G80$0+y}Bn>nRaoX5bq_lK8&2G2D0K(N6U_xX}HirikYywzHoCpo)+j^d}t`9sXluV$o6?ewHe5Ui+m5Y9oyhGHXI2OTu~#~ow24E&_|NZmvkjEEo{?lrj>I+3}kwNN$<|WFHD7&hT*J`96C?gGpoC>Df4aU8P&s$90m&Ugy{5AY@?hVDTc{$QAjsoHqS{ck6snhl_)o^474tl{Idqaq1M4gTm}}RWDGq`oxuuW;}<=ge*lXX+3Fk7GOExz3(~A%nd`>nKrLxi-U((%)yeb9`|*{}XN<!aZ;K4$uq0Oaz(HwoaYiOebn91@RynR!R8j7s;7Hra4}H!6j$8JPBT!r&nU1}{`9xQ4xJ`pVq(u7*0xR)<BH-?0@E)`61eEbr2+TPRO!dQhz6=IyRVaDDLEI15s(e*LMiSoTYZ)%g@+%W997F?^5x*mgOyrJBo>04z>c7Ok#4FmP|M+baT*Fuu4_Vg~%D*h%0S&xmIOVhF)nXWYKj;F_kSpCA=E?|IY3TP731i4AmJ9{uIj}nvkZA~PCqrx=X%FiI(pX*UhQoq9-g$`cDVZp7ZcaGt$}M)fe&uhR9-a*yxklx#6Au8ICI}z@KWrk3Obx%(^tG4C1D@?bgq2jBnZ(O?j&jyR!8J6j%~%zfEtIAPDKu$5hd4V~`Wco06Vlpvp}HuyKK}fz6t3mD@K4unNqV2DlC&6KFhx<%Ai%h1TodO4gAJ51=G}q7(kO9N(i4X$<eW-!*R9YA;7)@J*L9oP=fK!D6|+fcPENd)?Uh;;D^?V=^lDx@B&4gKK``fb35<plFL>8MWnd^UE!-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		@@ -0,0 +1,2 @@
		import lzma as L,base64 as B
		exec(L.decompress(B.b85decode(";Jw{5fL#DIn@VT6Qap3bt~@<3h>ok~)Km^%<bI~`7~^@P9dNtOmJtouSV\|m@^}~LJOY#qcoM@9BaGiY8ypPvdJq=NbK}E`t%OWHq5Ygw`LcO2`&Ki8P4h{e@}LyJM^e5fwE@n-bff1PhmUliW#yCOV=I+v<n\|QZ$y02ziZN=i)3}Qx?Dtm=+{LAgGTC@~>c^ys%R{D_%yAk9-_tV7^coUOo3$w>`(`ci)t`2F7>r>Ltx>>S2CRw\|7ov>Wn1e~_!RLQ=%V9g?)G3yPsu%SBy!lj1PaC-x%dDmCDOZ^r^!)+WWz}ejKXTJ#^U6Ra!};QocHHXQC+4UM!QQ!-N5Xd\|%~a(9)bTYIO+>B~8~@lqmri%^qEkQUy074Rh6w7V_#^s9J-3BNA`G;qyR$LYcI?e+loZVWi~B$n=TKFp{%SeHYp{oNWh;U@Ahk8M2$OU%K8B$lbdRQXd-GR_@KAZdRdwSd#v=LSq1v@Puul=a7WXDmh1^kBj}Y2XlER!D2E{&{%lV(hz$#n5%+%sk&Q}>{y0xpRgiQQBJeVV0hy8UD3ntyo@(Pv+K7^zVRDt4bah<BW;bd+Cav&<Xg-~v4E$T<1_zFr^A}Ais)\|vXpO`3FxNShvEi59luzir1VmzF1ek\|5CGkl?5qc}ko)L1\|6lu6I`m+bf%)mk)RQ$_JmfE%eS#TV<mEIvI}8;<$6J^=w~cJs5Q7=n#vHa4v1R&1_9=;Q2@QfQORDBW6CkyC4%tXHv%r3D5rF;6FZ>(r8kfsZThb+H1)~K-lIr4`\|V#-2R>G7pPN!fwWd&Dq8C)y=NrG_U_Oz6Q?+@ok1?(VJ5?ZT~&}C4Ks38WRB>3i=I!}H-8qq=&yKJ;tbpwwn~lAseD^q1Cu5T;l<jmq^\|gW0-BBpM5(&T>KQtF;?zv@u0f36%6SXU~txi3v5iSPK`fNE9531KaQDL`zTPF$MX4U(-3sY-&?>QJe)giBQzpor7H)AZ#4=Hn#`AoAL7tT){&bw(fgz\|eQRt`#6-<>;m+&$!nf\|od6&lVKYYHuOoNgZU_L>E@!O%__mlt=);Hwdc43+CM?sh5y+my3XSVYMO8F1pXuq$fvTU<$mpDjr>Lm){DeV)>4AKAhA?jxjH<-3yYQ<QU\|mI~;=9eDwNwPw;)v(Mu^%YrcdcwEH`s$RtDvffGRv\|\|Tctzy{1@`75RzWmpHgSnSab&b&Fyos-0wdom9#6ey3CnhkwfuHfVJc+7~(p~gN6Q?zV1K>#5qz+4c`Utifny+Ydmr4?c_z60#9@FU+U1&O$Lfg$WrX7gCj50O1t`1A`k04LVr;^~{\|@(TS5>#TAjL(B`umc8bVA$bS\|F?^2A7E}z7IIgZlY(8Ex#K+nLh0vzlKK=74U!g+sX4T?e3_^_7XB1A(HB{pYd{vHYcak_P3DZ2LAB20wAP+C_9p7R\|0}wA=p~JFi&xD8H}n(LxCc5rcmw<AY&of601+p3__P4;S)9wD!!2(apEWmyI^3Nlf>F`!s(tSf_<O3Y4@Lb^pR}8!JWi5F-x7_`)oM#pVlXHEPzi0_w{=OcHSWtLTLJZ%v~xfE-VLIhk+V3}!ek?mRs^iiZfQr<Cz8wcNzPD3j5B{~Al3+P&zpBEN%rOTMb?hCP$lA?OeffC?6d+83}cL8pBK3e!YNJ~9kmX-Ex)_GbEil+L!j=<aZeLrs`Qx2fCt`V(`tk{k`tP!^_a&cJLbp-5%L!\|FGej~o0jPXI6XZYFKMbV3}lTx@Ln<Lua-O#Rd6MFf\|hg{hiGOq{aNC;vw8&q5AhpiiicR}mCe^oxpwpei+d\|T&qL>l_TXk(cPZJ_z`)iV4#r^gzawYQ%HE1iaUF=(KAcKXE%%6Hx0i;?;p1w#dN7!-y!(2GUw()t4\|BXt%+05bu$y<wAnZSUY=-zn2daV7XQ!FRQ&E39a_e-evNQeH$%?k$j{<f2ReW`))eJS=r+^AO%o{S-\|mcA#-J)~\|Na8W7N&x7Y%qc}W\|PQU3N_33v}9rHTp1gPXKFiZ=ghykdcGL?F!Cgp^gGOHsjlG6z&`N$_rfTirGgHnbxUmK`ZYAYjsz^O;`V+8FnR!3RjxgtUMj}@iffiyK40=kBdOKbvmxC&OtL?AD%tI-f#Ue!e@rkDfVbz!EEW?KyfWIX?60i{``J&VX2%Z3u0DFujf-mL7MLYzyLu&oz@SWnnB8as3Q2~X{m^08~{^CZm!I=WZQ(\|Ri`w4QD6oqUIp&ghyb1r3)XQIiwz99LjnA%97e+<(Dy<%Le~b6b^n_z}O<Z)T6;z(P@UO8JGf2q~fNKR$4R3eV1RzaSj7^{@Y+^a&@GhCY(7b`{jL2ry%;_^Q!l>ea+{f!deuk%(g-o}&XEEWm!lO+1^On#i#4rhP{bDYb9ZnbGd5n{P->hZxI<{=3c-92I#gmTey8O>cuw%hdwjB!#=GH_0?hY\|Lf@L$0Qp+k03PROh)o-cMOQ!b>qfvPNJLTVvCBX24BGgI=_\|35Bd%&Vq&!LWECF4!1&J?@uRfe=N2mi{l-S0aW101IcY_A&2R~zfij0IZST;@;xJYti>{)weL@A2ZOGrq(U-ibnWz0BL;s!=S<i~82\|F>;`!K6@M501z-dU(OqY0?!!!Tk6Z{9!iHtDZsjYG`J8UEqJz~7cNEPh1A#iz_$2Nxo;S{UehRA^{Mf3GV^9hBEKSL(iD!=aKpjYCTqmhYA\|h4zASL-v?9UWx8tzm#N5eHo2h3w`(kHM2e}vDviz3$~a(Y#jlJL?}m9&(Oqs1+CNUy7g~Z#lRN#>g9{7u~tot;\|0qTu4G4xwdXk3eT+l1l$)Vq%}j^^1b(jIvF\|OcNb1Jz&)>b)qGiC5P7yS}AvC}VDK<woZNv4t;T(e%=S+yy9Wl!Mk?)7e{+73u$1ks?HX%Es<Jb3p=`hTp!8!8l%0)~O(9avW&Tp9s8lf<<!52RHDrrWVny4z`ftyF2ZJ6}qhUWU$@+y3>ORD$#^Ydjg!zDuM#$J+k<}O\|o#9dvGrp)yShv3->joMiF%~orV4^0cl9F!@VqwD`5f<QPLctdikXXI0rQ$mAAaeO)6}Zwp1jH%fdXVoxoZ`z5lV>jekV@3E`STlX!=JDxbOQiv?Jp)$Xy\|Z>g)@q_QYKopPeu&ghhPNw0y&{j?$GHwDQoztHvVU)a0ca6}7{#3^KK1uTcBkMSF$IDQp#Nhy>JTHPK2w+%N#FZ(D)=sw?BkfduBK{Owa(SkBq^_S\|NP@DI(VEWNqETjYsZ@bch5-dlWjP>\|)xv+AknhsqQ!j3!=TT%CYvl>o#XU4AApoVBJ;db<uv@;Zle>=W0m0#FH7by8-ZV~$!QptJuPqLkH-bA#`L?g#-60qO9x7)rWh{~YY77{NX_v_!Mc-#`(n{>OHy\|HxotTLyFAdqCe^bQsveKyNdxf%^ECJD<Wa?}FX&QnOVoQL$TZ#&f##R{pUuK(_{#}CVvVm7@+&m{N8R2nI$9!ALHZPvRk%b#hP48Q>w1jQaV{3doP1nC-IuYoJBS)BjwI+@WRZpBQq--\|WAHx2WWVue@lE`T9AY1=3wKyIT}9Ss;d##=nZ>!%19!lx_0W91se3dXzq5oIE}=)Lf4xkbyMcKg=z&Qh>gK5l~kV<Y3G\|XD^O\|uI3K$}GY)tl)92}xD#u_\|>7t^lQK_s8TXQqiwiAz+UT7qOmRWvI~~2y<G+o%s+8rsYGCml^rm)<LgsXmv>t~5_%swZRW#&SB~-uWRFRPsi5WZAzJp1&o@T%9?d1EUEpyP1v5zSN9`Nzg4<9J>%D?ZP~-T(dwGEKqZMPuhgN<\|K<!knseUP=32Qh=k%@&WeMpwkJyP5WK?vq@D7&kY+seMo3rb+KxP@Zkyra8qm)C>igW>x{H0%t_&ya;8v^0F=-)sKR85LA\|5>\|ZYqA#XmVbFc92&H9WjeO5C!FX%LsiXg6}0#l(Pg(=6hjd7H_7$6NLIA+rCa;GE_3R75D#&J(7=>z\|LN$87?M}UpavJo5jeYlJy>3UxeQ{duojamIjZmWv\|!Tjr5rT-K7C#w~_vZ!oIz>O;(D%nYcBK)`IjO`=SDZo4vJ4V2bFcFg(2@0lt\|4uUCPbO&N6^dv4y}sPBwT(0$\|M\|?y;Jv@#8^JCr!hxD0=c#R8ALJkOUZ5;?_TS5GI^kyB>q;{eo<-=N\|;JU?~G80$0+y}Bn>nRaoX5bq_lK8&2G2D0K(N6U_xX}HirikYywzHoCpo)+j^d}t`9sXluV$o6?ewHe5Ui+m5Y9oyhGHXI2OTu~#~ow24E&_\|NZmvkjEEo{?lrj>I+3}kwNN$<\|WFHD7&hTJ`96C?gGpoC>Df4aU8P&s$90m&Ugy{5AY@?hVDTc{$QAjsoHqS{ck6snhl_)o^474tl{Idqaq1M4gTm}}RWDGq`oxuuW;}<=gelXX+3Fk7GOExz3(~A%nd`>nKrLxi-U((%)yeb9`\|{}XN<!aZ;K4$uq0Oaz(HwoaYiOebn91@RynR!R8j7s;7Hra4}H!6j$8JPBT!r&nU1}{`9xQ4xJ`pVq(u70xR)<BH-?0@E)`61eEbr2+TPRO!dQhz6=IyRVaDDLEI15s(eLMiSoTYZ)%g@+%W997F?^5xmgOyrJBo>04z>c7Ok#4FmP\|M+baTFuu4_Vg~%Dh%0S&xmIOVhF)nXWYKj;F_kSpCA=E?\|IY3TP731i4AmJ9{uIj}nvkZA~PCqrx=X%FiI(pXUhQoq9-g$`cDVZp7ZcaGt$}M)fe&uhR9-ayxklx#6Au8ICI}z@KWrk3Obx%(^tG4C1D@?bgq2jBnZ(O?j&jyR!8J6j%~%zfEtIAPDKu$5hd4V~`Wco06Vlpvp}HuyKK}fz6t3mD@K4unNqV2DlC&6KFhx<%Ai%h1TodO4gAJ51=G}q7(kO9N(i4X$<eW-!R9YA;7)@JL9oP=fK!D6\|+fcPENd)?Uh;;D^?V=^lDx@B&4gKK``fb35<plFL>8MWnd^UE!-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