diff --git "a/applied/_index/APPLIED_RUNS_INDEX.json" "b/applied/_index/APPLIED_RUNS_INDEX.json"
--- "a/applied/_index/APPLIED_RUNS_INDEX.json"
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+        "mechanism": "V = u - s*((1-lam)*u + lam*thresh) + s*v_r; lam=0,v_r=0 is the baseline hard reset exactly, lam=1,v_r=0 discards zero charge",
+        "cost": "2 scalars per spiking layer; identical FMA count per tick, so the reset change is free at inference time",
+        "caveat": "the sweep swaps the reset on ALREADY-TRAINED weights, which under-states what training with each reset would give; and a charge-conserving reset can raise the spike count, so read spikes_per_inference next to the accuracy"
       },
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@@ -2685,455 +1713,278 @@
         "decision_margin": 2.0,
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+        "mechanism": "V = u - s*((1-lam)*u + lam*thresh) + s*v_r; lam=0,v_r=0 is the baseline hard reset exactly, lam=1,v_r=0 discards zero charge",
+        "cost": "2 scalars per spiking layer; identical FMA count per tick, so the reset change is free at inference time",
+        "caveat": "the sweep swaps the reset on ALREADY-TRAINED weights, which under-states what training with each reset would give; and a charge-conserving reset can raise the spike count, so read spikes_per_inference next to the accuracy"
       },
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@@ -3173,455 +2024,278 @@
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     "zip": {
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     "status": "ok",
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-      "script": "opt06_uticket_prune.py",
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+        "mechanism": "V = u - s*((1-lam)*u + lam*thresh) + s*v_r; lam=0,v_r=0 is the baseline hard reset exactly, lam=1,v_r=0 discards zero charge",
+        "cost": "2 scalars per spiking layer; identical FMA count per tick, so the reset change is free at inference time",
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+        "mechanism": "V = u - s*((1-lam)*u + lam*thresh) + s*v_r; lam=0,v_r=0 is the baseline hard reset exactly, lam=1,v_r=0 discards zero charge",
+        "cost": "2 scalars per spiking layer; identical FMA count per tick, so the reset change is free at inference time",
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@@ -4062,167 +2957,179 @@
         "decision_margin": 2.0,
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@@ -4262,7 +3169,7 @@
         "decision_margin": 2.0,
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@@ -4273,180 +3180,164 @@
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-          ],
-          "by_mode": {
-            "hard": {
-              "fp32_acc": 0.645,
-              "acc_by_bits": {
-                "0": 0.645,
-                "4": 0.8457142857142858,
-                "2": 0.21357142857142858
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-            },
-            "soft": {
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-              "acc_by_bits": {
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-                "4": 0.8435714285714285,
-                "2": 0.21357142857142858
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-            "trained": {
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-              "acc_by_bits": {
-                "0": 0.6678571428571428,
-                "4": 0.8307142857142857,
-                "2": 0.16142857142857142
-              }
-            }
+        "pareto": [
+          {
+            "conf": 0.5,
+            "mean_ticks": 4.28,
+            "acc": 0.24928571428571428,
+            "syn_ops": 1762351.5365259738
+          },
+          {
+            "conf": 0.7,
+            "mean_ticks": 4.979285714285714,
+            "acc": 0.27285714285714285,
+            "syn_ops": 1789906.663961039
+          },
+          {
+            "conf": 0.8,
+            "mean_ticks": 5.918571428571428,
+            "acc": 0.3028571428571429,
+            "syn_ops": 1827518.5
+          },
+          {
+            "conf": 0.9,
+            "mean_ticks": 7.942142857142857,
+            "acc": 0.38357142857142856,
+            "syn_ops": 1895163.6818181819
+          },
+          {
+            "conf": 0.95,
+            "mean_ticks": 10.063571428571429,
+            "acc": 0.45357142857142857,
+            "syn_ops": 1956706.423701299
+          },
+          {
+            "conf": 0.99,
+            "mean_ticks": 14.371428571428572,
+            "acc": 0.5792857142857143,
+            "syn_ops": 2056811.2378246754
           }
-        },
-        "mechanism": "V = u - s*((1-lam)*u + lam*thresh) + s*v_r; lam=0,v_r=0 is the baseline hard reset exactly, lam=1,v_r=0 discards zero charge",
-        "cost": "2 scalars per spiking layer; identical FMA count per tick, so the reset change is free at inference time",
-        "caveat": "the sweep swaps the reset on ALREADY-TRAINED weights, which under-states what training with each reset would give; and a charge-conserving reset can raise the spike count, so read spikes_per_inference next to the accuracy"
+        ]
       },
       "config": {
         "method": "harmonic_matched",
@@ -7611,34 +4646,35 @@
         "decision_margin": 2.0,
         "export": false,
         "seed": 1234,
-        "hub_dir": "/kaggle/working/hub/opt09_srif_softreset/u_headroom_4.0",
-        "repo_id": "UWU-R-13/SSVEP-SNN",
-        "push_hf": false,
-        "keep_epoch_weights": 1,
-        "no_resume": true,
-        "selftest": false,
-        "reset_mode": "srif",
-        "learn_reset": true,
-        "lam_init": 0.9,
-        "v_reset_init": 0.0,
-        "u_bits": 4,
-        "u_headroom": 4.0,
-        "reset_sweep": "hard,soft,trained",
-        "bits_sweep": "0,4,2",
         "baseline": false,
         "bench": true,
         "bench_warmup": 10,
         "bench_iters": 50,
         "bench_train_steps": 20,
+        "anytime_fracs": "0.25,0.5,0.75,1.0",
+        "anytime_weights": "1,1,1,1",
+        "ic_layer": 0,
+        "ic_loss_weight": 0.3,
+        "bayes_fusion": true,
+        "fusion_prior_weight": 1.0,
+        "conf_threshold": 0.9,
+        "conf_temp": 2.0,
+        "conf_sweep": "0.5,0.7,0.8,0.9,0.95,0.99",
+        "hub_dir": "/kaggle/working/hub/opt02_seenn_bayes/anytime_uniform",
+        "repo_id": "UWU-R-13/SSVEP-SNN",
+        "push_hf": false,
+        "keep_epoch_weights": 1,
+        "no_resume": true,
+        "selftest": false,
         "sampling_rate": 250.0
       }
     },
     "zip": {
-      "name": "u_headroom_4.0.zip",
+      "name": "anytime_uniform.zip",
       "files": 81,
-      "bytes": 12340512,
-      "MB": 12.3,
-      "sha256": "3394974815eca792e339c4516e7ced59d4a7259d00e7f9b0a8906fdcb18cea8d"
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   }
 ]
\ No newline at end of file