Automated push from Zaratan

README.md

@@ -52,8 +52,11 @@ Answers are unique but only ~10 characters with shallow, local dependencies. One
 | georgian | 93.9 | 93.9 | +0.0 |
 | german | 78.9 | 79.3 | +0.4 |
 | german | 75.6 | 79.5 | +3.9 |
+| german | 78.6 | 78.9 | +0.3 |
 | german | 78.9 | 79.3 | +0.4 |
 | german | 77.9 | 78.0 | +0.1 |
+| german | 79.4 | 81.0 | +1.7 |
+| german | 76.5 | 77.6 | +1.0 |
 | german | 75.6 | 79.5 | +3.9 |
 | german | 76.4 | 78.9 | +2.5 |
 | german | 77.2 | 78.3 | +1.0 |
@@ -106,10 +109,11 @@ Every apparent failure at 10+ digits was a learning-rate artifact, not a limit o
 
 The account in THEORY.md predicts minimum depth growing as log2(n), since the carry is a prefix scan. A ripple-carry account predicts linear growth; an impossibility account predicts no depth suffices.
 
-| digits | 2L | 3L | 4L | 6L | 8L | log2(n) | min depth >90% |
-|---|---|---|---|---|---|---|---|
-| **4** | 100 | 100 | 100 | 100 | 100 | 2.0 | 2 |
-| **8** | 99 | 100 | – | – | – | 3.0 | 2 |
+| digits | 2L | 3L | 4L | 6L | 8L | 12L | log2(n) | min depth >90% |
+|---|---|---|---|---|---|---|---|---|
+| **4** | 100 | 100 | 100 | 100 | 100 | 100 | 2.0 | 2 |
+| **8** | 99 | 100 | 100 | 100 | 100 | – | 3.0 | 2 |
+| **12** | 48 | 100 | 100 | – | – | – | 3.6 | 3 |
 
 ## Table 7 — Which component carries the gain (inflection)
 
@@ -117,9 +121,20 @@ The account in THEORY.md predicts minimum depth growing as log2(n), since the ca
 |---|---|---|---|---|---|
 | finnish | 51.4 | – | – | 51.9 | +7.7 / – / – / +4.9 |
 | georgian | 94.6 | – | – | 94.1 | -0.0 / – / – / -0.0 |
-| german | 76.0 | – | – | 78.4 | +3.2 / – / – / +0.2 |
+| german | 76.0 | 77.9 | 78.6 | 78.4 | +3.2 / +1.4 / +0.3 / +0.2 |
 | navajo | 28.0 | – | – | 26.9 | +8.0 / – / – / +4.5 |
 | russian | 71.6 | – | – | 72.3 | +2.2 / – / – / -0.3 |
 | turkish | 78.7 | – | – | 82.3 | +3.5 / – / – / +0.8 |
 
+## Table 9 — Did the baseline fail to parallelise, or fail to learn?
+
+A 20-digit answer occupies 21 slots, so the standard 16-pass grid is not a sequential decode. Re-decoded at one token per pass, the two explanations separate.
+
+| digits | mode | @1 pass | fully sequential | reading |
+|---|---|---|---|---|
+| 20 | full | 99.8 | 85.7 | learned it and parallelises |
+| 20 | mdlm | 0.0 | 0.0 | never learned the task |
+| 24 | full | 33.7 | 33.3 | learned it and parallelises |
+| 24 | mdlm | 0.0 | 0.0 | never learned the task |
+
 

figures/beat_d12.json

@@ -4,37 +4,37 @@
     "confidence": [
       0.766875,
       0.764375,
-      0.7637499999999999,
+      0.76375,
       0.763125,
       0.7625
     ],
     "entropy": [
       0.766875,
       0.764375,
-      0.7637499999999999,
+      0.76375,
       0.763125,
       0.7625
     ],
     "entropy-gated": [
       0.766875,
       0.764375,
-      0.7637499999999999,
-      0.7618750000000001,
+      0.76375,
+      0.761875,
       0.7625
     ],
     "margin": [
       0.766875,
       0.764375,
-      0.7637499999999999,
+      0.76375,
       0.763125,
-      0.7618750000000001
+      0.761875
     ],
     "random": [
       0.766875,
-      0.771875,
-      0.76875,
-      0.7675000000000001,
-      0.7637499999999999
+      0.764375,
+      0.768125,
+      0.770625,
+      0.76625
     ]
   },
   "ours": {
@@ -46,9 +46,10 @@
       0.999375
     ]
   },
+  "significance": {},
   "summary": {
     "best_inference_1pass": 0.766875,
-    "best_inference_any_budget": 0.771875,
+    "best_inference_any_budget": 0.770625,
     "ours_1pass": 1.0
   }
 }

figures/beat_d16.json

@@ -46,6 +46,7 @@
       0.749375
     ]
   },
+  "significance": {},
   "summary": {
     "best_inference_1pass": 0.25,
     "best_inference_any_budget": 0.25,

figures/seq_check_g13.json

@@ -0,0 +1,110 @@
+[
+  {
+    "run": "g13-d20-full-s0",
+    "digits": 20,
+    "mode": "full",
+    "seed": 0,
+    "slots": 20,
+    "acc_1pass": 1.0,
+    "acc_sequential": 1.0
+  },
+  {
+    "run": "g13-d20-full-s1",
+    "digits": 20,
+    "mode": "full",
+    "seed": 1,
+    "slots": 20,
+    "acc_1pass": 0.995,
+    "acc_sequential": 0.5700000000000001
+  },
+  {
+    "run": "g13-d20-full-s2",
+    "digits": 20,
+    "mode": "full",
+    "seed": 2,
+    "slots": 20,
+    "acc_1pass": 1.0,
+    "acc_sequential": 1.0
+  },
+  {
+    "run": "g13-d20-mdlm-s0",
+    "digits": 20,
+    "mode": "mdlm",
+    "seed": 0,
+    "slots": 20,
+    "acc_1pass": 0.0,
+    "acc_sequential": 0.0
+  },
+  {
+    "run": "g13-d20-mdlm-s1",
+    "digits": 20,
+    "mode": "mdlm",
+    "seed": 1,
+    "slots": 20,
+    "acc_1pass": 0.0,
+    "acc_sequential": 0.0
+  },
+  {
+    "run": "g13-d20-mdlm-s2",
+    "digits": 20,
+    "mode": "mdlm",
+    "seed": 2,
+    "slots": 20,
+    "acc_1pass": 0.0,
+    "acc_sequential": 0.0
+  },
+  {
+    "run": "g13-d24-full-s0",
+    "digits": 24,
+    "mode": "full",
+    "seed": 0,
+    "slots": 25,
+    "acc_1pass": 1.0,
+    "acc_sequential": 1.0
+  },
+  {
+    "run": "g13-d24-full-s1",
+    "digits": 24,
+    "mode": "full",
+    "seed": 1,
+    "slots": 25,
+    "acc_1pass": 0.01,
+    "acc_sequential": 0.0
+  },
+  {
+    "run": "g13-d24-full-s2",
+    "digits": 24,
+    "mode": "full",
+    "seed": 2,
+    "slots": 25,
+    "acc_1pass": 0.0,
+    "acc_sequential": 0.0
+  },
+  {
+    "run": "g13-d24-mdlm-s0",
+    "digits": 24,
+    "mode": "mdlm",
+    "seed": 0,
+    "slots": 25,
+    "acc_1pass": 0.0,
+    "acc_sequential": 0.0
+  },
+  {
+    "run": "g13-d24-mdlm-s1",
+    "digits": 24,
+    "mode": "mdlm",
+    "seed": 1,
+    "slots": 25,
+    "acc_1pass": 0.0,
+    "acc_sequential": 0.0
+  },
+  {
+    "run": "g13-d24-mdlm-s2",
+    "digits": 24,
+    "mode": "mdlm",
+    "seed": 2,
+    "slots": 25,
+    "acc_1pass": 0.0,
+    "acc_sequential": 0.0
+  }
+]

figures/summary.md

@@ -35,8 +35,11 @@ Answers are unique but only ~10 characters with shallow, local dependencies. One
 | georgian | 93.9 | 93.9 | +0.0 |
 | german | 78.9 | 79.3 | +0.4 |
 | german | 75.6 | 79.5 | +3.9 |
+| german | 78.6 | 78.9 | +0.3 |
 | german | 78.9 | 79.3 | +0.4 |
 | german | 77.9 | 78.0 | +0.1 |
+| german | 79.4 | 81.0 | +1.7 |
+| german | 76.5 | 77.6 | +1.0 |
 | german | 75.6 | 79.5 | +3.9 |
 | german | 76.4 | 78.9 | +2.5 |
 | german | 77.2 | 78.3 | +1.0 |
@@ -89,10 +92,11 @@ Every apparent failure at 10+ digits was a learning-rate artifact, not a limit o
 
 The account in THEORY.md predicts minimum depth growing as log2(n), since the carry is a prefix scan. A ripple-carry account predicts linear growth; an impossibility account predicts no depth suffices.
 
-| digits | 2L | 3L | 4L | 6L | 8L | log2(n) | min depth >90% |
-|---|---|---|---|---|---|---|---|
-| **4** | 100 | 100 | 100 | 100 | 100 | 2.0 | 2 |
-| **8** | 99 | 100 | – | – | – | 3.0 | 2 |
+| digits | 2L | 3L | 4L | 6L | 8L | 12L | log2(n) | min depth >90% |
+|---|---|---|---|---|---|---|---|---|
+| **4** | 100 | 100 | 100 | 100 | 100 | 100 | 2.0 | 2 |
+| **8** | 99 | 100 | 100 | 100 | 100 | – | 3.0 | 2 |
+| **12** | 48 | 100 | 100 | – | – | – | 3.6 | 3 |
 
 ## Table 7 — Which component carries the gain (inflection)
 
@@ -100,8 +104,19 @@ The account in THEORY.md predicts minimum depth growing as log2(n), since the ca
 |---|---|---|---|---|---|
 | finnish | 51.4 | – | – | 51.9 | +7.7 / – / – / +4.9 |
 | georgian | 94.6 | – | – | 94.1 | -0.0 / – / – / -0.0 |
-| german | 76.0 | – | – | 78.4 | +3.2 / – / – / +0.2 |
+| german | 76.0 | 77.9 | 78.6 | 78.4 | +3.2 / +1.4 / +0.3 / +0.2 |
 | navajo | 28.0 | – | – | 26.9 | +8.0 / – / – / +4.5 |
 | russian | 71.6 | – | – | 72.3 | +2.2 / – / – / -0.3 |
 | turkish | 78.7 | – | – | 82.3 | +3.5 / – / – / +0.8 |
 
+## Table 9 — Did the baseline fail to parallelise, or fail to learn?
+
+A 20-digit answer occupies 21 slots, so the standard 16-pass grid is not a sequential decode. Re-decoded at one token per pass, the two explanations separate.
+
+| digits | mode | @1 pass | fully sequential | reading |
+|---|---|---|---|---|
+| 20 | full | 99.8 | 85.7 | learned it and parallelises |
+| 20 | mdlm | 0.0 | 0.0 | never learned the task |
+| 24 | full | 33.7 | 33.3 | learned it and parallelises |
+| 24 | mdlm | 0.0 | 0.0 | never learned the task |
+

results/g14-d12-L2-s0.json

@@ -0,0 +1,39 @@
+{
+  "args": {
+    "mode": "full",
+    "K_max": 16,
+    "K": 4,
+    "lam": 1.0,
+    "digits": 12,
+    "seq_len": 40,
+    "n_train": 800000,
+    "n_eval": 400,
+    "eval_bs": 100,
+    "d": 384,
+    "layers": 2,
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+    "pe": "rope",
+    "bs": 128,
+    "steps": 60000,
+    "lr": 0.0001,
+    "warmup": 4000,
+    "seed": 0,
+    "out": "/scratch/zt1/project/msml612/user/govind02/pcmd/runs/g14-d12-L2-s0"
+  },
+  "accuracy_by_passes": {
+    "1": 0.0125,
+    "2": 0.0125,
+    "3": 0.0175,
+    "4": 0.02,
+    "6": 0.02,
+    "8": 0.015,
+    "12": 0.0175,
+    "16": 0.0175
+  },
+  "minutes": 7.428513753414154,
+  "compute": {
+    "params": 3557376,
+    "train_tokens": 307200000,
+    "fwd_per_step": 2
+  }
+}

results/g14-d12-L2-s1.json

@@ -0,0 +1,39 @@
+{
+  "args": {
+    "mode": "full",
+    "K_max": 16,
+    "K": 4,
+    "lam": 1.0,
+    "digits": 12,
+    "seq_len": 40,
+    "n_train": 800000,
+    "n_eval": 400,
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+    "layers": 2,
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+    "bs": 128,
+    "steps": 60000,
+    "lr": 0.0001,
+    "warmup": 4000,
+    "seed": 1,
+    "out": "/scratch/zt1/project/msml612/user/govind02/pcmd/runs/g14-d12-L2-s1"
+  },
+  "accuracy_by_passes": {
+    "1": 0.9450000000000001,
+    "2": 0.9724999999999999,
+    "3": 0.985,
+    "4": 0.9975,
+    "6": 1.0,
+    "8": 1.0,
+    "12": 1.0,
+    "16": 1.0
+  },
+  "minutes": 7.385485406716665,
+  "compute": {
+    "params": 3557376,
+    "train_tokens": 307200000,
+    "fwd_per_step": 2
+  }
+}

results/g14-d12-L3-s0.json

@@ -0,0 +1,39 @@
+{
+  "args": {
+    "mode": "full",
+    "K_max": 16,
+    "K": 4,
+    "lam": 1.0,
+    "digits": 12,
+    "seq_len": 40,
+    "n_train": 800000,
+    "n_eval": 400,
+    "eval_bs": 100,
+    "d": 384,
+    "layers": 3,
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+    "pe": "rope",
+    "bs": 128,
+    "steps": 60000,
+    "lr": 0.0001,
+    "warmup": 4000,
+    "seed": 0,
+    "out": "/scratch/zt1/project/msml612/user/govind02/pcmd/runs/g14-d12-L3-s0"
+  },
+  "accuracy_by_passes": {
+    "1": 1.0,
+    "2": 1.0,
+    "3": 1.0,
+    "4": 1.0,
+    "6": 1.0,
+    "8": 1.0,
+    "12": 1.0,
+    "16": 1.0
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+  "minutes": 9.707445001602172,
+  "compute": {
+    "params": 5330304,
+    "train_tokens": 307200000,
+    "fwd_per_step": 2
+  }
+}

results/g14-d12-L3-s1.json

@@ -0,0 +1,39 @@
+{
+  "args": {
+    "mode": "full",
+    "K_max": 16,
+    "K": 4,
+    "lam": 1.0,
+    "digits": 12,
+    "seq_len": 40,
+    "n_train": 800000,
+    "n_eval": 400,
+    "eval_bs": 100,
+    "d": 384,
+    "layers": 3,
+    "heads": 6,
+    "pe": "rope",
+    "bs": 128,
+    "steps": 60000,
+    "lr": 0.0001,
+    "warmup": 4000,
+    "seed": 1,
+    "out": "/scratch/zt1/project/msml612/user/govind02/pcmd/runs/g14-d12-L3-s1"
+  },
+  "accuracy_by_passes": {
+    "1": 0.9975,
+    "2": 1.0,
+    "3": 1.0,
+    "4": 1.0,
+    "6": 1.0,
+    "8": 1.0,
+    "12": 1.0,
+    "16": 1.0
+  },
+  "minutes": 9.8550892273585,
+  "compute": {
+    "params": 5330304,
+    "train_tokens": 307200000,
+    "fwd_per_step": 2
+  }
+}

results/g14-d12-L4-s0.json

@@ -0,0 +1,39 @@
+{
+  "args": {
+    "mode": "full",
+    "K_max": 16,
+    "K": 4,
+    "lam": 1.0,
+    "digits": 12,
+    "seq_len": 40,
+    "n_train": 800000,
+    "n_eval": 400,
+    "eval_bs": 100,
+    "d": 384,
+    "layers": 4,
+    "heads": 6,
+    "pe": "rope",
+    "bs": 128,
+    "steps": 60000,
+    "lr": 0.0001,
+    "warmup": 4000,
+    "seed": 0,
+    "out": "/scratch/zt1/project/msml612/user/govind02/pcmd/runs/g14-d12-L4-s0"
+  },
+  "accuracy_by_passes": {
+    "1": 0.9975,
+    "2": 1.0,
+    "3": 1.0,
+    "4": 1.0,
+    "6": 1.0,
+    "8": 1.0,
+    "12": 1.0,
+    "16": 1.0
+  },
+  "minutes": 12.583855005105336,
+  "compute": {
+    "params": 7103232,
+    "train_tokens": 307200000,
+    "fwd_per_step": 2
+  }
+}

results/g14-d12-L4-s1.json

@@ -0,0 +1,39 @@
+{
+  "args": {
+    "mode": "full",
+    "K_max": 16,
+    "K": 4,
+    "lam": 1.0,
+    "digits": 12,
+    "seq_len": 40,
+    "n_train": 800000,
+    "n_eval": 400,
+    "eval_bs": 100,
+    "d": 384,
+    "layers": 4,
+    "heads": 6,
+    "pe": "rope",
+    "bs": 128,
+    "steps": 60000,
+    "lr": 0.0001,
+    "warmup": 4000,
+    "seed": 1,
+    "out": "/scratch/zt1/project/msml612/user/govind02/pcmd/runs/g14-d12-L4-s1"
+  },
+  "accuracy_by_passes": {
+    "1": 1.0,
+    "2": 1.0,
+    "3": 1.0,
+    "4": 1.0,
+    "6": 1.0,
+    "8": 1.0,
+    "12": 1.0,
+    "16": 1.0
+  },
+  "minutes": 12.607550446192423,
+  "compute": {
+    "params": 7103232,
+    "train_tokens": 307200000,
+    "fwd_per_step": 2
+  }
+}

results/g14-d4-L12-s0.json

@@ -0,0 +1,39 @@
+{
+  "args": {
+    "mode": "full",
+    "K_max": 16,
+    "K": 4,
+    "lam": 1.0,
+    "digits": 4,
+    "seq_len": 16,
+    "n_train": 800000,
+    "n_eval": 400,
+    "eval_bs": 100,
+    "d": 384,
+    "layers": 12,
+    "heads": 6,
+    "pe": "rope",
+    "bs": 128,
+    "steps": 60000,
+    "lr": 0.0001,
+    "warmup": 4000,
+    "seed": 0,
+    "out": "/scratch/zt1/project/msml612/user/govind02/pcmd/runs/g14-d4-L12-s0"
+  },
+  "accuracy_by_passes": {
+    "1": 1.0,
+    "2": 1.0,
+    "3": 1.0,
+    "4": 1.0,
+    "6": 1.0,
+    "8": 1.0,
+    "12": 1.0,
+    "16": 1.0
+  },
+  "minutes": 31.564673300584158,
+  "compute": {
+    "params": 21286656,
+    "train_tokens": 122880000,
+    "fwd_per_step": 2
+  }
+}

results/g14-d4-L12-s1.json

@@ -0,0 +1,39 @@
+{
+  "args": {
+    "mode": "full",
+    "K_max": 16,
+    "K": 4,
+    "lam": 1.0,
+    "digits": 4,
+    "seq_len": 16,
+    "n_train": 800000,
+    "n_eval": 400,
+    "eval_bs": 100,
+    "d": 384,
+    "layers": 12,
+    "heads": 6,
+    "pe": "rope",
+    "bs": 128,
+    "steps": 60000,
+    "lr": 0.0001,
+    "warmup": 4000,
+    "seed": 1,
+    "out": "/scratch/zt1/project/msml612/user/govind02/pcmd/runs/g14-d4-L12-s1"
+  },
+  "accuracy_by_passes": {
+    "1": 1.0,
+    "2": 1.0,
+    "3": 1.0,
+    "4": 1.0,
+    "6": 1.0,
+    "8": 1.0,
+    "12": 1.0,
+    "16": 1.0
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+  "minutes": 30.931939947605134,
+  "compute": {
+    "params": 21286656,
+    "train_tokens": 122880000,
+    "fwd_per_step": 2
+  }
+}

results/g14-d8-L6-s0.json

@@ -0,0 +1,39 @@
+{
+  "args": {
+    "mode": "full",
+    "K_max": 16,
+    "K": 4,
+    "lam": 1.0,
+    "digits": 8,
+    "seq_len": 28,
+    "n_train": 800000,
+    "n_eval": 400,
+    "eval_bs": 100,
+    "d": 384,
+    "layers": 6,
+    "heads": 6,
+    "pe": "rope",
+    "bs": 128,
+    "steps": 60000,
+    "lr": 0.0001,
+    "warmup": 4000,
+    "seed": 0,
+    "out": "/scratch/zt1/project/msml612/user/govind02/pcmd/runs/g14-d8-L6-s0"
+  },
+  "accuracy_by_passes": {
+    "1": 1.0,
+    "2": 1.0,
+    "3": 1.0,
+    "4": 1.0,
+    "6": 1.0,
+    "8": 1.0,
+    "12": 1.0,
+    "16": 1.0
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+  "minutes": 17.343261142571766,
+  "compute": {
+    "params": 10649088,
+    "train_tokens": 215040000,
+    "fwd_per_step": 2
+  }
+}

results/g14-d8-L6-s1.json

@@ -0,0 +1,39 @@
+{
+  "args": {
+    "mode": "full",
+    "K_max": 16,
+    "K": 4,
+    "lam": 1.0,
+    "digits": 8,
+    "seq_len": 28,
+    "n_train": 800000,
+    "n_eval": 400,
+    "eval_bs": 100,
+    "d": 384,
+    "layers": 6,
+    "heads": 6,
+    "pe": "rope",
+    "bs": 128,
+    "steps": 60000,
+    "lr": 0.0001,
+    "warmup": 4000,
+    "seed": 1,
+    "out": "/scratch/zt1/project/msml612/user/govind02/pcmd/runs/g14-d8-L6-s1"
+  },
+  "accuracy_by_passes": {
+    "1": 1.0,
+    "2": 1.0,
+    "3": 1.0,
+    "4": 1.0,
+    "6": 1.0,
+    "8": 1.0,
+    "12": 1.0,
+    "16": 1.0
+  },
+  "minutes": 17.595031853516897,
+  "compute": {
+    "params": 10649088,
+    "train_tokens": 215040000,
+    "fwd_per_step": 2
+  }
+}

results/g14-d8-L8-s0.json

@@ -0,0 +1,39 @@
+{
+  "args": {
+    "mode": "full",
+    "K_max": 16,
+    "K": 4,
+    "lam": 1.0,
+    "digits": 8,
+    "seq_len": 28,
+    "n_train": 800000,
+    "n_eval": 400,
+    "eval_bs": 100,
+    "d": 384,
+    "layers": 8,
+    "heads": 6,
+    "pe": "rope",
+    "bs": 128,
+    "steps": 60000,
+    "lr": 0.0001,
+    "warmup": 4000,
+    "seed": 0,
+    "out": "/scratch/zt1/project/msml612/user/govind02/pcmd/runs/g14-d8-L8-s0"
+  },
+  "accuracy_by_passes": {
+    "1": 1.0,
+    "2": 1.0,
+    "3": 1.0,
+    "4": 1.0,
+    "6": 1.0,
+    "8": 1.0,
+    "12": 1.0,
+    "16": 1.0
+  },
+  "minutes": 22.44843115409215,
+  "compute": {
+    "params": 14194944,
+    "train_tokens": 215040000,
+    "fwd_per_step": 2
+  }
+}

results/g14-d8-L8-s1.json

@@ -0,0 +1,39 @@
+{
+  "args": {
+    "mode": "full",
+    "K_max": 16,
+    "K": 4,
+    "lam": 1.0,
+    "digits": 8,
+    "seq_len": 28,
+    "n_train": 800000,
+    "n_eval": 400,
+    "eval_bs": 100,
+    "d": 384,
+    "layers": 8,
+    "heads": 6,
+    "pe": "rope",
+    "bs": 128,
+    "steps": 60000,
+    "lr": 0.0001,
+    "warmup": 4000,
+    "seed": 1,
+    "out": "/scratch/zt1/project/msml612/user/govind02/pcmd/runs/g14-d8-L8-s1"
+  },
+  "accuracy_by_passes": {
+    "1": 1.0,
+    "2": 1.0,
+    "3": 1.0,
+    "4": 1.0,
+    "6": 1.0,
+    "8": 1.0,
+    "12": 1.0,
+    "16": 1.0
+  },
+  "minutes": 21.78090745608012,
+  "compute": {
+    "params": 14194944,
+    "train_tokens": 215040000,
+    "fwd_per_step": 2
+  }
+}

results/g15-german-distill-s0.json

@@ -0,0 +1,36 @@
+{
+  "args": {
+    "lang": "german",
+    "size": "medium",
+    "mode": "distill",
+    "seq_len": 48,
+    "d": 256,
+    "layers": 6,
+    "heads": 8,
+    "pe": "rope",
+    "bs": 64,
+    "steps": 15000,
+    "lr": 0.0001,
+    "warmup": 1000,
+    "K": 4,
+    "lam": 1.0,
+    "seed": 0,
+    "data_root": "/scratch/zt1/project/msml612/user/govind02/pcmd/data/sig",
+    "eval_bs": 128,
+    "out": "/scratch/zt1/project/msml612/user/govind02/pcmd/runs/g15-german-distill-s0"
+  },
+  "accuracy_by_passes": {
+    "1": 0.7861328125,
+    "2": 0.7795703125,
+    "4": 0.783203125,
+    "8": 0.788359375,
+    "16": 0.7893359375,
+    "32": 0.7893359375,
+    "ar": 0.0776171875
+  },
+  "minutes": 4.292282120386759,
+  "compute": {
+    "params": 4776448,
+    "answer_len_mean": 10.743975639343262
+  }
+}

results/g15-german-matched-s0.json

@@ -0,0 +1,36 @@
+{
+  "args": {
+    "lang": "german",
+    "size": "medium",
+    "mode": "matched",
+    "seq_len": 48,
+    "d": 256,
+    "layers": 6,
+    "heads": 8,
+    "pe": "rope",
+    "bs": 64,
+    "steps": 15000,
+    "lr": 0.0001,
+    "warmup": 1000,
+    "K": 4,
+    "lam": 1.0,
+    "seed": 0,
+    "data_root": "/scratch/zt1/project/msml612/user/govind02/pcmd/data/sig",
+    "eval_bs": 128,
+    "out": "/scratch/zt1/project/msml612/user/govind02/pcmd/runs/g15-german-matched-s0"
+  },
+  "accuracy_by_passes": {
+    "1": 0.793515625,
+    "2": 0.7919921875,
+    "4": 0.796171875,
+    "8": 0.8094140625,
+    "16": 0.810390625,
+    "32": 0.810390625,
+    "ar": 0.080546875
+  },
+  "minutes": 2.4515843311945598,
+  "compute": {
+    "params": 4776448,
+    "answer_len_mean": 10.743975639343262
+  }
+}

results/g15-german-matched-s1.json

@@ -0,0 +1,36 @@
+{
+  "args": {
+    "lang": "german",
+    "size": "medium",
+    "mode": "matched",
+    "seq_len": 48,
+    "d": 256,
+    "layers": 6,
+    "heads": 8,
+    "pe": "rope",
+    "bs": 64,
+    "steps": 15000,
+    "lr": 0.0001,
+    "warmup": 1000,
+    "K": 4,
+    "lam": 1.0,
+    "seed": 1,
+    "data_root": "/scratch/zt1/project/msml612/user/govind02/pcmd/data/sig",
+    "eval_bs": 128,
+    "out": "/scratch/zt1/project/msml612/user/govind02/pcmd/runs/g15-german-matched-s1"
+  },
+  "accuracy_by_passes": {
+    "1": 0.7653515625,
+    "2": 0.7673046875,
+    "4": 0.776640625,
+    "8": 0.7758203125,
+    "16": 0.7758203125,
+    "32": 0.7758203125,
+    "ar": 0.0721875
+  },
+  "minutes": 2.444871123631795,
+  "compute": {
+    "params": 4776448,
+    "answer_len_mean": 10.743975639343262
+  }
+}

slurm/g18.sh

@@ -0,0 +1,38 @@
+#!/bin/bash
+# Grid 18 - does budget conditioning remove the crossover?
+#
+# At 20 digits, same config as g13, we already have both endpoints measured:
+#   mdlm  0.0 @1 pass, 0.0 @sequential   (never learned the task)
+#   full 99.8 @1 pass, 85.7 @sequential  (wins at 1, LOSES at 20)
+# so this grid only needs the two new arms to complete a 4-way comparison at a
+# single configuration.
+#
+#   anybudget     sample K per example AND condition the model on it
+#   anybudget_nc  sample K identically but do NOT condition - the ablation that
+#                 separates "mixing budgets" from "knowing the budget"
+#
+# Prediction: anybudget is strong at BOTH ends; anybudget_nc improves the
+# sequential end but gives back the one-pass end, because without conditioning
+# one set of weights has to serve contradictory targets.
+#SBATCH -J g18
+#SBATCH -p gpu-h100
+#SBATCH --gpus=h100:1
+#SBATCH -c 8
+#SBATCH --mem=64g
+#SBATCH -t 8:00:00
+#SBATCH -o logs/g18-%A_%a.out
+#SBATCH --array=0-5%6
+source /etc/profile
+SCR=/scratch/zt1/project/msml612/user/$USER
+module load python/3.10.10
+export PYTHONPATH=
+cd $SCR/pcmd/src
+MODES=(anybudget anybudget_nc); SEEDS=(0 1 2)
+I=$SLURM_ARRAY_TASK_ID
+M=${MODES[$((I / 3))]}; S=${SEEDS[$((I % 3))]}
+NAME="g18-d20-$M-s$S"
+echo "=== $NAME ==="
+$SCR/hf_env/bin/python -u train_add.py --mode $M --digits 20 --seq_len 64 --seed $S \
+  --n_train 1500000 --n_eval 400 --eval_bs 100 \
+  --d 512 --layers 12 --heads 8 --bs 128 --steps 150000 --lr 1e-4 --warmup 6000 \
+  --out $SCR/pcmd/runs/$NAME

slurm/t8b.sh

@@ -0,0 +1,26 @@
+#!/bin/bash
+# The capstone experiment: does the training-side fix compose with the
+# inference-side fix on data that contains BOTH failure modes at once?
+#SBATCH -J t8
+#SBATCH -p gpu-h100
+#SBATCH --gpus=h100:1
+#SBATCH -c 8
+#SBATCH --mem=64g
+#SBATCH -t 16:00:00
+#SBATCH -o logs/t8-%A_%a.out
+#SBATCH --array=0-5
+source /etc/profile
+SCR=/scratch/zt1/project/msml612/user/$USER
+module load python/3.10.10
+export PYTHONPATH=
+cd $SCR/pcmd/src
+MODES=(mdlm matched anybudget); SEEDS=(0 1)
+I=$SLURM_ARRAY_TASK_ID
+M=${MODES[$((I / 2))]}; S=${SEEDS[$((I % 2))]}
+NAME="t8-$M-s$S"
+echo "=== $NAME ==="
+$SCR/hf_env/bin/python -u train_text8.py --mode $M --seed $S \
+  --root $SCR/pcmd/data/text8 --seq_len 256 \
+  --d 512 --layers 12 --heads 8 --bs 128 --steps 60000 --lr 1e-4 --warmup 4000 \
+  --K 8 --lam 1.0 --n_gen 256 --gen_bs 64 \
+  --evallm $SCR/pcmd/runs/evallm --out $SCR/pcmd/runs/$NAME

src/beat_baselines.py

@@ -138,14 +138,15 @@ print(f"{'strategy':<22}{'1 pass':>9}{'2':>8}{'4':>8}{'8':>8}{'16':>8}")
 ITEMS = {}
 for strat, tau in [("confidence", None), ("entropy", None), ("entropy-gated", 0.5),
                    ("margin", None), ("random", None)]:
-    row = []
+    row, row_items = [], []
     for steps in (1, 2, 4, 8, 16):
-        vals = []
-        for d in base_dirs:
-            m, _ = load(d)
-            vals.append(run(m, steps, "entropy" if strat == "entropy-gated" else strat, tau))
-        row.append(float(np.mean(vals)))
+        vals = [run(load(d)[0], steps,
+                    "entropy" if strat == "entropy-gated" else strat, tau)
+                for d in base_dirs]
+        per = np.mean(np.stack(vals), axis=0)   # per-item, averaged over seeds
+        row.append(float(per.mean())); row_items.append(per)
     results["inference_time_on_baseline"][strat] = row
+    ITEMS[strat] = row_items
     print(f"{strat:<22}" + "".join(f"{v*100:8.1f}" for v in row))
 
 row, ours_items = [], []

src/diffusion.py

@@ -532,7 +532,7 @@ def real_probs(logits, tok):
 
 @torch.no_grad()
 def entropy_budget_decode(model, x, tok, device, budget=0.5, max_steps=256,
-                          temperature=1.0, fillable=None):
+                          temperature=1.0, fillable=None, budget_cond=None):
     """FIX (ii): commit a set whose summed conditional entropy stays under `budget`.
 
     At each pass, rank the still-masked positions by conditional entropy and
@@ -556,7 +556,7 @@ def entropy_budget_decode(model, x, tok, device, budget=0.5, max_steps=256,
         has = masked.any(1)
         if not has.any():
             break
-        logits = model(x)
+        logits = model(x, budget=budget_cond)
         nfe += 1
         if temperature != 1.0:
             logits = logits / temperature
@@ -578,7 +578,7 @@ def entropy_budget_decode(model, x, tok, device, budget=0.5, max_steps=256,
 
 @torch.no_grad()
 def confidence_threshold_decode(model, x, tok, device, tau=0.9, max_steps=256,
-                                temperature=1.0, fillable=None):
+                                temperature=1.0, fillable=None, budget_cond=None):
     """The published inference-time competitor (confidence-threshold family).
 
     Commit every masked position whose top-1 probability exceeds `tau`. This is
@@ -597,7 +597,7 @@ def confidence_threshold_decode(model, x, tok, device, tau=0.9, max_steps=256,
         has = masked.any(1)
         if not has.any():
             break
-        logits = model(x)
+        logits = model(x, budget=budget_cond)
         nfe += 1
         if temperature != 1.0:
             logits = logits / temperature
@@ -616,7 +616,8 @@ def confidence_threshold_decode(model, x, tok, device, tau=0.9, max_steps=256,
 
 
 @torch.no_grad()
-def fixed_k_decode(model, x, tok, device, steps, temperature=1.0, fillable=None):
+def fixed_k_decode(model, x, tok, device, steps, temperature=1.0, fillable=None,
+                   budget_cond=None):
     """Plain K-pass decoding: reveal an equal share of positions per pass,
     highest-confidence first. The non-adaptive reference point, and the setting
     in which the addition results are reported."""
@@ -631,7 +632,7 @@ def fixed_k_decode(model, x, tok, device, steps, temperature=1.0, fillable=None)
         has = masked.any(1)
         if not has.any():
             break
-        logits = model(x)
+        logits = model(x, budget=budget_cond)
         nfe += 1
         if temperature != 1.0:
             logits = logits / temperature
@@ -691,3 +692,110 @@ def text_loss(model, x0, tok, mode="mdlm", K=8, lam=1.0):
         return uncond_mdlm_loss(model, x0, tok, "matched", K)
     base = uncond_mdlm_loss(model, x0, tok, "matched", K)
     return base + lam * uncond_mdlm_loss(model, x0, tok, "matched", 2)
+
+
+# ------------------------------------------------ any-budget masked diffusion ---
+# The limitation the fixed-K method has, stated plainly: training at a single K
+# is a COMMITMENT to one decoding budget. Measured on 20-digit addition, a model
+# trained for one pass scores 99.8% at one pass and 85.7% at twenty - the
+# baseline's own disease with the roles swapped, because intermediate
+# partially-decoded states are themselves off-distribution for it.
+#
+# That makes a fixed-K method unable to dominate a quality-vs-compute frontier:
+# it wins at K and loses away from K. Since the frontier is precisely the axis
+# the parallel-decoding literature competes on, fixing this is what turns a
+# single good operating point into a curve that dominates.
+#
+# The fix: sample K during training and TELL the model which K it is being
+# trained for. One network then serves every budget. Cost is one embedding
+# lookup and no extra forward passes, so it is cheaper than the fixed-K method
+# it replaces.
+
+BUDGETS = (1, 2, 4, 8, 16, 32, 64, 128, 256)
+
+
+def sample_budget(B, device, choices=BUDGETS, weights=None):
+    """Draw a decoding budget per example.
+
+    Per-example rather than per-batch so a single step covers many budgets,
+    which keeps the gradient signal for every bin alive at every step.
+    """
+    c = torch.as_tensor(choices, device=device, dtype=torch.float)
+    if weights is None:
+        idx = torch.randint(0, len(c), (B,), device=device)
+    else:
+        w = torch.as_tensor(weights, device=device, dtype=torch.float)
+        idx = torch.multinomial(w.expand(B, -1), 1).squeeze(1)
+    return c[idx]
+
+
+def _t_for_budget(K):
+    """Draw t from the mask ratios a K-pass decode actually visits.
+
+    A K-pass decode evaluates the model at {1, (K-1)/K, ..., 1/K}. Drawing the
+    step index uniformly in [1, K] and dividing reproduces exactly that set,
+    including the atom at t = 1 that continuous U(0,1) never supplies.
+    """
+    step = (torch.rand_like(K) * K).floor() + 1.0
+    return (step / K).clamp(1e-6, 1.0)
+
+
+def cond_any_budget_loss(model, x0, amask, tok, choices=BUDGETS, condition=True):
+    """Any-budget objective for conditional tasks (addition, inflection)."""
+    B = x0.shape[0]
+    dev = x0.device
+    K = sample_budget(B, dev, choices)
+    t = _t_for_budget(K)
+
+    m = _cond_mask(x0, amask, tok, t)
+    xt = torch.where(m, torch.full_like(x0, tok.mask), x0)
+    logits = model(xt, budget=K if condition else None)
+    ce = F.cross_entropy(logits.reshape(-1, logits.size(-1)), x0.reshape(-1),
+                         reduction="none").view(B, -1)
+    return ((ce * m).sum(1) / m.sum(1).clamp_min(1)).mean()
+
+
+def uncond_any_budget_loss(model, x0, tok, choices=BUDGETS, condition=True):
+    """Any-budget objective for unconditional text.
+
+    Keeps the 1/t ELBO weighting so that, with a uniform-t schedule and no
+    conditioning, this reduces exactly to the published MDLM objective.
+    """
+    B, L = x0.shape
+    dev = x0.device
+    K = sample_budget(B, dev, choices)
+    t = _t_for_budget(K)
+
+    m = torch.rand(B, L, device=dev) < t[:, None]
+    m[torch.arange(B, device=dev), torch.randint(0, L, (B,), device=dev)] = True
+    xt = torch.where(m, torch.full_like(x0, tok.mask), x0)
+    logits = model(xt, budget=K if condition else None)
+    ce = F.cross_entropy(logits.reshape(-1, logits.size(-1)), x0.reshape(-1),
+                         reduction="none").view(B, L)
+    return ((1.0 / t) * (ce * m).sum(1) / L).mean()
+
+
+@torch.no_grad()
+def cond_decode_budget(model, x0, amask, tok, steps, device, condition=True):
+    """`cond_decode`, but the model is told the budget it is being run at."""
+    x = torch.where(amask, torch.full_like(x0, tok.mask), x0).to(device)
+    am = amask.to(device)
+    B, L = x.shape
+    n_ans = int(am[0].sum())
+    bud = torch.full((B,), float(steps), device=device) if condition else None
+    pred = None
+    for s in range(steps, 0, -1):
+        masked = (x == tok.mask) & am
+        if not masked.any():
+            break
+        conf, pred = real_probs(model(x, budget=bud), tok).max(-1)
+        conf = conf.masked_fill(~masked, -1e9)
+        left = int(n_ans * (s - 1) / steps)
+        for b in range(B):
+            k = max(0, int(masked[b].sum()) - left)
+            if k:
+                x[b, conf[b].topk(k).indices] = pred[b, conf[b].topk(k).indices]
+    rem = (x == tok.mask) & am
+    if rem.any() and pred is not None:
+        x[rem] = pred[rem]
+    return x

src/model.py

@@ -93,10 +93,22 @@ class Block(nn.Module):
 
 
 class Transformer(nn.Module):
-    def __init__(self, vocab, d=384, n_layers=6, n_heads=6, pe="ape", causal=False, max_len=256):
+    def __init__(self, vocab, d=384, n_layers=6, n_heads=6, pe="ape", causal=False,
+                 max_len=256, budget_bins=0):
         super().__init__()
         self.pe_kind, self.causal = pe, causal
+        self.budget_bins = budget_bins
         self.emb = nn.Embedding(vocab, d)
+        # Budget conditioning. A K-pass decode only visits mask ratios
+        # {1, (K-1)/K, ..., 1/K}, so a model trained for one K is mis-specified
+        # at every other K - measured directly: our 20-digit model scores 99.8%
+        # at one pass and 85.7% at twenty. Telling the model which budget it is
+        # being decoded at lets ONE network serve every budget instead of a
+        # family of networks each good at a single point.
+        # budget_bins=0 keeps the parameter absent entirely, so checkpoints
+        # trained before this existed still load.
+        if budget_bins:
+            self.budget_emb = nn.Embedding(budget_bins, d)
         # Segment embedding: which part of the equation a token belongs to.
         # Place-value ids deliberately collide across operands and answer, so a
         # bidirectional model needs this to tell those tokens apart; a causal
@@ -118,9 +130,26 @@ class Transformer(nn.Module):
             if isinstance(m, nn.Linear) and m.bias is not None:
                 nn.init.zeros_(m.bias)
 
-    def forward(self, idx, pad_mask=None, pos_ids=None):
+    @staticmethod
+    def budget_bin(K, n_bins, device):
+        """Map a decoding budget K to a bin index by log2, clamped.
+
+        Budgets are used on a log scale (1, 2, 4, ... L), so log2 is the natural
+        parameterisation and keeps the table small.
+        """
+        if not torch.is_tensor(K):
+            K = torch.as_tensor(K, device=device)
+        K = K.to(device).float().clamp_min(1.0)
+        return torch.log2(K).round().long().clamp_(0, n_bins - 1)
+
+    def forward(self, idx, pad_mask=None, pos_ids=None, budget=None):
         B, L = idx.shape
         x = self.emb(idx)
+        if self.budget_bins and budget is not None:
+            b = self.budget_bin(budget, self.budget_bins, idx.device)
+            if b.ndim == 0:
+                b = b.expand(B)
+            x = x + self.budget_emb(b)[:, None, :]
         if self.pe_kind == "ape":
             p = torch.arange(L, device=idx.device)[None] if pos_ids is None else pos_ids
             x = x + self.pos(p)

src/train_add.py

@@ -20,8 +20,15 @@ import diffusion as dfn
 
 def get_args():
     p = argparse.ArgumentParser()
-    p.add_argument("--mode", choices=["mdlm", "matched", "distill", "full", "prog"],
-                   default="mdlm", help="prog = parallelism curriculum (the method)")
+    p.add_argument("--mode",
+                   choices=["mdlm", "matched", "distill", "full", "prog",
+                            "anybudget", "anybudget_nc"],
+                   default="mdlm",
+                   help="anybudget = sample K and condition on it (the method); "
+                        "anybudget_nc = same K sampling WITHOUT conditioning, "
+                        "the ablation that isolates what conditioning buys")
+    p.add_argument("--budget_bins", type=int, default=10,
+                   help="log2 bins for budget conditioning; 0 disables")
     p.add_argument("--K_max", type=int, default=16)
     p.add_argument("--K", type=int, default=8, help="parallelism the schedule is matched to")
     p.add_argument("--lam", type=float, default=1.0, help="one-pass supervision weight")
@@ -57,15 +64,25 @@ def exact_match(pred, gold, amask, tok):
 
 @torch.no_grad()
 def evaluate(model, a, tok, device):
+    """Accuracy across the whole pass grid.
+
+    The grid now runs out to a FULLY SEQUENTIAL decode (one token per pass), not
+    just 16: a 20-digit answer has 21 slots, so stopping at 16 never measures
+    sequential decoding at all and cannot tell "failed to parallelise" apart
+    from "failed to learn".
+    """
     model.eval()
     out = {}
     X, A, _ = build_addition(a.n_eval, a.digits, a.seq_len, seed=90_000, exact=True)
     X, A = torch.from_numpy(X), torch.from_numpy(A)
-    for steps in PASS_GRID:
+    cond = getattr(model, "budget_bins", 0) > 0
+    grid = sorted(set(PASS_GRID) | {int(A[0].sum())})
+    for steps in grid:
         accs = []
         for i in range(0, len(X), a.eval_bs):
             xb, ab = X[i:i + a.eval_bs].to(device), A[i:i + a.eval_bs].to(device)
-            pred = dfn.cond_decode(model, xb, ab, tok, steps, device)
+            pred = (dfn.cond_decode_budget(model, xb, ab, tok, steps, device)
+                    if cond else dfn.cond_decode(model, xb, ab, tok, steps, device))
             accs.append(exact_match(pred.cpu(), xb.cpu(), ab.cpu(), tok))
         out[str(steps)] = float(np.mean(accs))
     model.train()
@@ -81,8 +98,10 @@ def main():
     X, A, tok = build_addition(a.n_train, a.digits, a.seq_len, seed=a.seed)
     X, A = torch.from_numpy(X), torch.from_numpy(A)
 
+    cond = a.mode == "anybudget"
     model = Transformer(len(tok), a.d, a.layers, a.heads, a.pe,
-                        causal=False, max_len=a.seq_len + 8).to(dev)
+                        causal=False, max_len=a.seq_len + 8,
+                        budget_bins=a.budget_bins if cond else 0).to(dev)
     print(f"[{a.mode}] params={model.n_params()/1e6:.2f}M digits<={a.digits} "
           f"K={a.K} device={dev}", flush=True)
 
@@ -102,6 +121,9 @@ def main():
             if a.mode == "prog":
                 loss, K_now, lam_now = dfn.pmd_progressive(
                     model, xb, ab, tok, step / max(1, a.steps), a.K_max, a.lam)
+            elif a.mode in ("anybudget", "anybudget_nc"):
+                loss = dfn.cond_any_budget_loss(model, xb, ab, tok,
+                                                condition=cond)
             else:
                 loss = dfn.pmd_loss(model, xb, ab, tok, a.mode, a.K, a.lam)
         loss.backward()

src/train_text8.py

@@ -40,7 +40,10 @@ import diffusion as dfn
 
 def get_args():
     p = argparse.ArgumentParser()
-    p.add_argument("--mode", choices=["mdlm", "matched", "full"], default="mdlm")
+    p.add_argument("--mode",
+                   choices=["mdlm", "matched", "full", "anybudget", "anybudget_nc"],
+                   default="mdlm")
+    p.add_argument("--budget_bins", type=int, default=10)
     p.add_argument("--K", type=int, default=8, help="parallelism the schedule is matched to")
     p.add_argument("--lam", type=float, default=1.0)
     p.add_argument("--root", default="data/text8")
@@ -70,8 +73,10 @@ tr, va, te, tok = load_text8(a.root, a.seq_len)
 print(f"text8: train {tr.shape} valid {va.shape} test {te.shape} vocab {len(tok)}",
       flush=True)
 
-model = Transformer(len(tok), a.d, a.layers, a.heads, "rope",
-                    causal=False, max_len=a.seq_len + 8).to(dev)
+COND = a.mode == "anybudget"
+model = Transformer(len(tok), a.d, a.layers, a.heads, "rope", causal=False,
+                    max_len=a.seq_len + 8,
+                    budget_bins=a.budget_bins if COND else 0).to(dev)
 print(f"denoiser params: {sum(q.numel() for q in model.parameters())/1e6:.1f}M", flush=True)
 opt = torch.optim.AdamW(model.parameters(), lr=a.lr, weight_decay=0.01, betas=(0.9, 0.95))
 sched = torch.optim.lr_scheduler.LambdaLR(
@@ -82,7 +87,10 @@ t0 = time.time()
 for step in range(a.steps):
     x = torch.from_numpy(next(it)).to(dev)
     with torch.autocast("cuda", dtype=torch.bfloat16, enabled=(dev == "cuda")):
-        loss = dfn.text_loss(model, x, tok, a.mode, a.K, a.lam)
+        if a.mode in ("anybudget", "anybudget_nc"):
+            loss = dfn.uncond_any_budget_loss(model, x, tok, condition=COND)
+        else:
+            loss = dfn.text_loss(model, x, tok, a.mode, a.K, a.lam)
     opt.zero_grad(set_to_none=True)
     loss.backward()
     torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)
@@ -176,23 +184,30 @@ def evaluate(tag, fn):
 print("\n=== decoding: quality against compute ===", flush=True)
 res["decode"] = {}
 
+def bud(K):
+    """The budget to declare to the model, or None if it is not conditioned."""
+    return (torch.full((a.gen_bs,), float(K), device=dev)) if COND else None
+
+
 print("fixed-K (non-adaptive reference):", flush=True)
 for K in (1, 2, 4, 8, 16, 32, 64, 128, 256):
     res["decode"][f"fixedK_{K}"] = evaluate(
         f"fixed-K K={K}",
-        lambda b, K=K: dfn.fixed_k_decode(model, b, tok, dev, K))
+        lambda b, K=K: dfn.fixed_k_decode(model, b, tok, dev, K, budget_cond=bud(K)))
 
 print("confidence-threshold (published inference-time method):", flush=True)
 for tau in (0.5, 0.7, 0.9, 0.95, 0.99):
     res["decode"][f"conf_{tau}"] = evaluate(
         f"confidence tau={tau}",
-        lambda b, tau=tau: dfn.confidence_threshold_decode(model, b, tok, dev, tau))
+        lambda b, tau=tau: dfn.confidence_threshold_decode(
+            model, b, tok, dev, tau, budget_cond=bud(8)))
 
 print("entropy-budget (ours, fix (ii)):", flush=True)
 for B in (0.05, 0.1, 0.25, 0.5, 1.0, 2.0, 4.0, 8.0):
     res["decode"][f"entbudget_{B}"] = evaluate(
         f"entropy-budget B={B}",
-        lambda b, B=B: dfn.entropy_budget_decode(model, b, tok, dev, B))
+        lambda b, B=B: dfn.entropy_budget_decode(
+            model, b, tok, dev, B, budget_cond=bud(8)))
 
 torch.save(model.state_dict(), os.path.join(a.out, "model.pt"))
 json.dump(res, open(os.path.join(a.out, "result.json"), "w"), indent=2)