diff --git a/circuit-shotting/artifacts/percentile_proxy/issue42_percentile_proxy_20260518T110628Z/README.md b/circuit-shotting/artifacts/percentile_proxy/issue42_percentile_proxy_20260518T110628Z/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..e9391093c251711a324bfa3af106bc82280a33f2
--- /dev/null
+++ b/circuit-shotting/artifacts/percentile_proxy/issue42_percentile_proxy_20260518T110628Z/README.md
@@ -0,0 +1,5 @@
+# Issue 42 Percentile Proxy Artifacts
+
+This package contains the 100x 1%ile destructive MLP proxy scan, top-20 union mask, and teacher-forced proxy substrate-only metrics.
+
+Upstream HY-MT model weights are not included.
diff --git a/circuit-shotting/artifacts/percentile_proxy/issue42_percentile_proxy_20260518T110628Z/damage/percentile_results.jsonl b/circuit-shotting/artifacts/percentile_proxy/issue42_percentile_proxy_20260518T110628Z/damage/percentile_results.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..a35118d5acb25bd09ca2e0fbbab4bb68d032fe79
--- /dev/null
+++ b/circuit-shotting/artifacts/percentile_proxy/issue42_percentile_proxy_20260518T110628Z/damage/percentile_results.jsonl
@@ -0,0 +1,100 @@
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diff --git a/circuit-shotting/artifacts/percentile_proxy/issue42_percentile_proxy_20260518T110628Z/damage/percentile_results_shard0.jsonl b/circuit-shotting/artifacts/percentile_proxy/issue42_percentile_proxy_20260518T110628Z/damage/percentile_results_shard0.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..716050d9e5d5df6ed0d66588d4718fa2316cf5dd
--- /dev/null
+++ b/circuit-shotting/artifacts/percentile_proxy/issue42_percentile_proxy_20260518T110628Z/damage/percentile_results_shard0.jsonl
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diff --git a/circuit-shotting/artifacts/percentile_proxy/issue42_percentile_proxy_20260518T110628Z/damage/percentile_results_shard0.summary.json b/circuit-shotting/artifacts/percentile_proxy/issue42_percentile_proxy_20260518T110628Z/damage/percentile_results_shard0.summary.json
new file mode 100644
index 0000000000000000000000000000000000000000..af2190cfa74426f89cf9693b22d66b1637185c4d
--- /dev/null
+++ b/circuit-shotting/artifacts/percentile_proxy/issue42_percentile_proxy_20260518T110628Z/damage/percentile_results_shard0.summary.json
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+  "out_jsonl": "/root/runs/issue42_percentile_proxy_8b46e31/damage/percentile_results_shard0.jsonl"
+}
diff --git a/circuit-shotting/artifacts/percentile_proxy/issue42_percentile_proxy_20260518T110628Z/damage/percentile_results_shard1.jsonl b/circuit-shotting/artifacts/percentile_proxy/issue42_percentile_proxy_20260518T110628Z/damage/percentile_results_shard1.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..6d57bb0002ef1750abc27607b06d96d61614d7ea
--- /dev/null
+++ b/circuit-shotting/artifacts/percentile_proxy/issue42_percentile_proxy_20260518T110628Z/damage/percentile_results_shard1.jsonl
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--- /dev/null
+++ b/circuit-shotting/artifacts/percentile_proxy/issue42_percentile_proxy_20260518T110628Z/damage/percentile_results_shard1.summary.json
@@ -0,0 +1,14 @@
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+  "out_jsonl": "/root/runs/issue42_percentile_proxy_8b46e31/damage/percentile_results_shard1.jsonl"
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diff --git a/circuit-shotting/artifacts/percentile_proxy/issue42_percentile_proxy_20260518T110628Z/damage/percentile_results_shard2.jsonl b/circuit-shotting/artifacts/percentile_proxy/issue42_percentile_proxy_20260518T110628Z/damage/percentile_results_shard2.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..f907512c28faa93f2dbe32673f4a5735b85922ef
--- /dev/null
+++ b/circuit-shotting/artifacts/percentile_proxy/issue42_percentile_proxy_20260518T110628Z/damage/percentile_results_shard2.jsonl
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diff --git a/circuit-shotting/artifacts/percentile_proxy/issue42_percentile_proxy_20260518T110628Z/damage/percentile_results_shard2.summary.json b/circuit-shotting/artifacts/percentile_proxy/issue42_percentile_proxy_20260518T110628Z/damage/percentile_results_shard2.summary.json
new file mode 100644
index 0000000000000000000000000000000000000000..41af2026c02cc0356fb996102e10e4382d1a47e7
--- /dev/null
+++ b/circuit-shotting/artifacts/percentile_proxy/issue42_percentile_proxy_20260518T110628Z/damage/percentile_results_shard2.summary.json
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+  "out_jsonl": "/root/runs/issue42_percentile_proxy_8b46e31/damage/percentile_results_shard2.jsonl"
+}
diff --git a/circuit-shotting/artifacts/percentile_proxy/issue42_percentile_proxy_20260518T110628Z/damage/percentile_results_shard3.jsonl b/circuit-shotting/artifacts/percentile_proxy/issue42_percentile_proxy_20260518T110628Z/damage/percentile_results_shard3.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..305abf4f85014fc22b1f60684cc247f8acc91b12
--- /dev/null
+++ b/circuit-shotting/artifacts/percentile_proxy/issue42_percentile_proxy_20260518T110628Z/damage/percentile_results_shard3.jsonl
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diff --git a/circuit-shotting/artifacts/percentile_proxy/issue42_percentile_proxy_20260518T110628Z/eval/top20_proxy.json b/circuit-shotting/artifacts/percentile_proxy/issue42_percentile_proxy_20260518T110628Z/eval/top20_proxy.json
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+{
+  "model": "tencent/HY-MT1.5-1.8B",
+  "n_rows": 512,
+  "n_examples": 512,
+  "n_layers": 32,
+  "hidden_size": 2048,
+  "d_ffn": 6144,
+  "mlp_total": 196608,
+  "zero_all_mean_token_kl": 12.821661838961273,
+  "results": {
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+      "channels": 0,
+      "channel_fraction": 0.0,
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+        "teacher_top5_contains": 0.00040575267120508544,
+        "teacher_argmax_nll": 13.000545934583652,
+        "answer_tokens": 22181,
+        "n_sentences": 512,
+        "elapsed_s": 5.4914586544036865,
+        "kl_gap_recovery_vs_zero_all": 0.0,
+        "kl_ratio_vs_zero_all": 1.0
+      }
+    },
+    "nuked_percentile_top_20": {
+      "intervention": "nuke-zero",
+      "mask_path": "/root/runs/issue42_percentile_proxy_8b46e31/selection/masks/percentile_top_20.full.npz",
+      "channels": 39321,
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+    "native_percentile_top_20_substrate_only": {
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+        "kl_gap_recovery_vs_zero_all": 0.15042278143481724,
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+    }
+  }
+}
diff --git a/circuit-shotting/artifacts/percentile_proxy/issue42_percentile_proxy_20260518T110628Z/logs/eval_top20_proxy.log b/circuit-shotting/artifacts/percentile_proxy/issue42_percentile_proxy_20260518T110628Z/logs/eval_top20_proxy.log
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+{"name": "native_percentile_top_20_substrate_only", "intervention": "keep-only", "mask_path": "/root/runs/issue42_percentile_proxy_8b46e31/selection/masks/percentile_top_20.full.npz", "channels": 39321, "channel_fraction": 0.1999969482421875, "metrics": {"mean_token_kl": 10.892991802528064, "mean_sentence_kl": 10.8990478515625, "tail90_sentence_kl": 11.5, "teacher_top1_agreement": 0.001172174383481358, "teacher_top5_contains": 0.0051846174653983135, "teacher_argmax_nll": 11.062509069558406, "answer_tokens": 22181, "n_sentences": 512, "elapsed_s": 5.3054163455963135, "kl_gap_recovery_vs_zero_all": 0.15042278143481724, "kl_ratio_vs_zero_all": 0.8495772185651828}}
diff --git a/circuit-shotting/artifacts/percentile_proxy/issue42_percentile_proxy_20260518T110628Z/logs/issue42_progress.log b/circuit-shotting/artifacts/percentile_proxy/issue42_percentile_proxy_20260518T110628Z/logs/issue42_progress.log
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+++ b/circuit-shotting/artifacts/percentile_proxy/issue42_percentile_proxy_20260518T110628Z/logs/issue42_progress.log
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+[2026-05-18T11:02:53+00:00] issue42 start percentile proxy gpus=0 1 2 3
+[2026-05-18T11:02:53+00:00] run 100x 1%ile MLP nuke KL damage scan over 4 GPUs
+[2026-05-18T11:05:50+00:00] select top-20 destructive 1%ile union
+[2026-05-18T11:05:54+00:00] run teacher-forced proxy eval for all-zero, nuked top-20, and substrate-only top-20
+[2026-05-18T11:06:27+00:00] summarize issue42
+[2026-05-18T11:06:28+00:00] package and upload issue42 artifacts
diff --git a/circuit-shotting/artifacts/percentile_proxy/issue42_percentile_proxy_20260518T110628Z/logs/merge_percentile_results.log b/circuit-shotting/artifacts/percentile_proxy/issue42_percentile_proxy_20260518T110628Z/logs/merge_percentile_results.log
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+++ b/circuit-shotting/artifacts/percentile_proxy/issue42_percentile_proxy_20260518T110628Z/logs/merge_percentile_results.log
@@ -0,0 +1 @@
+/root/runs/issue42_percentile_proxy_8b46e31/damage/percentile_results.jsonl
diff --git a/circuit-shotting/artifacts/percentile_proxy/issue42_percentile_proxy_20260518T110628Z/logs/nohup.log b/circuit-shotting/artifacts/percentile_proxy/issue42_percentile_proxy_20260518T110628Z/logs/nohup.log
new file mode 100644
index 0000000000000000000000000000000000000000..15a61ee5faef9e89cf6fb4a637466f5c67ccaa90
--- /dev/null
+++ b/circuit-shotting/artifacts/percentile_proxy/issue42_percentile_proxy_20260518T110628Z/logs/nohup.log
@@ -0,0 +1,6 @@
+[2026-05-18T11:02:53+00:00] issue42 start percentile proxy gpus=0 1 2 3
+[2026-05-18T11:02:53+00:00] run 100x 1%ile MLP nuke KL damage scan over 4 GPUs
+[2026-05-18T11:05:50+00:00] select top-20 destructive 1%ile union
+[2026-05-18T11:05:54+00:00] run teacher-forced proxy eval for all-zero, nuked top-20, and substrate-only top-20
+[2026-05-18T11:06:27+00:00] summarize issue42
+[2026-05-18T11:06:28+00:00] package and upload issue42 artifacts
diff --git a/circuit-shotting/artifacts/percentile_proxy/issue42_percentile_proxy_20260518T110628Z/logs/package_issue42_hf_upload.log b/circuit-shotting/artifacts/percentile_proxy/issue42_percentile_proxy_20260518T110628Z/logs/package_issue42_hf_upload.log
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/circuit-shotting/artifacts/percentile_proxy/issue42_percentile_proxy_20260518T110628Z/logs/percentile_scan_shard0.log b/circuit-shotting/artifacts/percentile_proxy/issue42_percentile_proxy_20260518T110628Z/logs/percentile_scan_shard0.log
new file mode 100644
index 0000000000000000000000000000000000000000..0475cf7b17273f559d338ae0f5902ed64c6b3009
--- /dev/null
+++ b/circuit-shotting/artifacts/percentile_proxy/issue42_percentile_proxy_20260518T110628Z/logs/percentile_scan_shard0.log
@@ -0,0 +1,25 @@
+{"name": "sector_0_chunk_0", "channels": 1966, "score": 0.3821094371378422, "mean_sentence_kl": 0.18972662463784218, "tail90_sentence_kl": 0.384765625}
+{"name": "sector_0_chunk_4", "channels": 1966, "score": 0.1307773245498538, "mean_sentence_kl": 0.0599765432998538, "tail90_sentence_kl": 0.1416015625}
+{"name": "sector_0_chunk_8", "channels": 1966, "score": 0.08590446738526225, "mean_sentence_kl": 0.04122673301026225, "tail90_sentence_kl": 0.08935546875}
+{"name": "sector_0_chunk_12", "channels": 1967, "score": 0.06948038563132286, "mean_sentence_kl": 0.03139444813132286, "tail90_sentence_kl": 0.076171875}
+{"name": "sector_0_chunk_16", "channels": 1966, "score": 0.058142575435340405, "mean_sentence_kl": 0.028113278560340405, "tail90_sentence_kl": 0.06005859375}
+{"name": "sector_0_chunk_20", "channels": 1966, "score": 0.10601149499416351, "mean_sentence_kl": 0.05474196374416351, "tail90_sentence_kl": 0.1025390625}
+{"name": "sector_0_chunk_24", "channels": 1967, "score": 0.06613910850137472, "mean_sentence_kl": 0.03122699912637472, "tail90_sentence_kl": 0.06982421875}
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+{"name": "sector_0_chunk_60", "channels": 1966, "score": 0.08616224676370621, "mean_sentence_kl": 0.04392591863870621, "tail90_sentence_kl": 0.08447265625}
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+{"name": "sector_0_chunk_80", "channels": 1966, "score": 0.03990514762699604, "mean_sentence_kl": 0.02037389762699604, "tail90_sentence_kl": 0.0390625}
+{"name": "sector_0_chunk_84", "channels": 1966, "score": 0.044009044766426086, "mean_sentence_kl": 0.022280529141426086, "tail90_sentence_kl": 0.04345703125}
+{"name": "sector_0_chunk_88", "channels": 1966, "score": 0.05478079244494438, "mean_sentence_kl": 0.02768118306994438, "tail90_sentence_kl": 0.05419921875}
+{"name": "sector_0_chunk_92", "channels": 1966, "score": 0.05573820322751999, "mean_sentence_kl": 0.03095792979001999, "tail90_sentence_kl": 0.049560546875}
+{"name": "sector_0_chunk_96", "channels": 1966, "score": 0.060733892023563385, "mean_sentence_kl": 0.031192876398563385, "tail90_sentence_kl": 0.05908203125}
diff --git a/circuit-shotting/artifacts/percentile_proxy/issue42_percentile_proxy_20260518T110628Z/logs/percentile_scan_shard1.log b/circuit-shotting/artifacts/percentile_proxy/issue42_percentile_proxy_20260518T110628Z/logs/percentile_scan_shard1.log
new file mode 100644
index 0000000000000000000000000000000000000000..fcc895e8613b95a58ff1f699712896ac74c65dd2
--- /dev/null
+++ b/circuit-shotting/artifacts/percentile_proxy/issue42_percentile_proxy_20260518T110628Z/logs/percentile_scan_shard1.log
@@ -0,0 +1,25 @@
+{"name": "sector_0_chunk_1", "channels": 1966, "score": 0.36368532478809357, "mean_sentence_kl": 0.17325563728809357, "tail90_sentence_kl": 0.380859375}
+{"name": "sector_0_chunk_5", "channels": 1966, "score": 0.11249938979744911, "mean_sentence_kl": 0.05122009292244911, "tail90_sentence_kl": 0.12255859375}
+{"name": "sector_0_chunk_9", "channels": 1966, "score": 0.08937827311456203, "mean_sentence_kl": 0.041526710614562035, "tail90_sentence_kl": 0.095703125}
+{"name": "sector_0_chunk_13", "channels": 1966, "score": 0.06319744884967804, "mean_sentence_kl": 0.02975018322467804, "tail90_sentence_kl": 0.06689453125}
+{"name": "sector_0_chunk_17", "channels": 1966, "score": 0.05681737884879112, "mean_sentence_kl": 0.028130855411291122, "tail90_sentence_kl": 0.057373046875}
+{"name": "sector_0_chunk_21", "channels": 1966, "score": 0.059916903264820576, "mean_sentence_kl": 0.029399325139820576, "tail90_sentence_kl": 0.06103515625}
+{"name": "sector_0_chunk_25", "channels": 1966, "score": 0.04557039123028517, "mean_sentence_kl": 0.022621172480285168, "tail90_sentence_kl": 0.0458984375}
+{"name": "sector_0_chunk_29", "channels": 1966, "score": 0.03763967251870781, "mean_sentence_kl": 0.018230492831207812, "tail90_sentence_kl": 0.038818359375}
+{"name": "sector_0_chunk_33", "channels": 1966, "score": 0.03674356290139258, "mean_sentence_kl": 0.01867715665139258, "tail90_sentence_kl": 0.0361328125}
+{"name": "sector_0_chunk_37", "channels": 1967, "score": 0.037289292085915804, "mean_sentence_kl": 0.018124253023415804, "tail90_sentence_kl": 0.038330078125}
+{"name": "sector_0_chunk_41", "channels": 1966, "score": 0.038610585033893585, "mean_sentence_kl": 0.018835194408893585, "tail90_sentence_kl": 0.03955078125}
+{"name": "sector_0_chunk_45", "channels": 1966, "score": 0.048113323748111725, "mean_sentence_kl": 0.024065472185611725, "tail90_sentence_kl": 0.048095703125}
+{"name": "sector_0_chunk_49", "channels": 1967, "score": 0.04839302971959114, "mean_sentence_kl": 0.02471138909459114, "tail90_sentence_kl": 0.04736328125}
+{"name": "sector_0_chunk_53", "channels": 1966, "score": 0.08128458261489868, "mean_sentence_kl": 0.04124552011489868, "tail90_sentence_kl": 0.080078125}
+{"name": "sector_0_chunk_57", "channels": 1966, "score": 0.08864056691527367, "mean_sentence_kl": 0.044695254415273666, "tail90_sentence_kl": 0.087890625}
+{"name": "sector_0_chunk_61", "channels": 1966, "score": 0.08519613370299339, "mean_sentence_kl": 0.04320394620299339, "tail90_sentence_kl": 0.083984375}
+{"name": "sector_0_chunk_65", "channels": 1966, "score": 0.0985150970518589, "mean_sentence_kl": 0.0460248626768589, "tail90_sentence_kl": 0.10498046875}
+{"name": "sector_0_chunk_69", "channels": 1966, "score": 0.06442795693874359, "mean_sentence_kl": 0.03220139443874359, "tail90_sentence_kl": 0.064453125}
+{"name": "sector_0_chunk_73", "channels": 1966, "score": 0.04775017872452736, "mean_sentence_kl": 0.02394646778702736, "tail90_sentence_kl": 0.047607421875}
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+{"name": "sector_0_chunk_97", "channels": 1966, "score": 0.079761803150177, "mean_sentence_kl": 0.041920006275177, "tail90_sentence_kl": 0.07568359375}
diff --git a/circuit-shotting/artifacts/percentile_proxy/issue42_percentile_proxy_20260518T110628Z/logs/percentile_scan_shard2.log b/circuit-shotting/artifacts/percentile_proxy/issue42_percentile_proxy_20260518T110628Z/logs/percentile_scan_shard2.log
new file mode 100644
index 0000000000000000000000000000000000000000..f19a58c281b5aeb7c4ee39de78bff451c1d88079
--- /dev/null
+++ b/circuit-shotting/artifacts/percentile_proxy/issue42_percentile_proxy_20260518T110628Z/logs/percentile_scan_shard2.log
@@ -0,0 +1,25 @@
+{"name": "sector_0_chunk_2", "channels": 1966, "score": 0.32993651553988457, "mean_sentence_kl": 0.15220214053988457, "tail90_sentence_kl": 0.35546875}
+{"name": "sector_0_chunk_6", "channels": 1966, "score": 0.09350507520139217, "mean_sentence_kl": 0.041991403326392174, "tail90_sentence_kl": 0.10302734375}
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+{"name": "sector_0_chunk_26", "channels": 1966, "score": 0.05126772075891495, "mean_sentence_kl": 0.025022603571414948, "tail90_sentence_kl": 0.052490234375}
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+{"name": "sector_0_chunk_38", "channels": 1966, "score": 0.03427674435079098, "mean_sentence_kl": 0.017675181850790977, "tail90_sentence_kl": 0.033203125}
+{"name": "sector_0_chunk_42", "channels": 1966, "score": 0.03477866016328335, "mean_sentence_kl": 0.017322605475783348, "tail90_sentence_kl": 0.034912109375}
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diff --git a/circuit-shotting/artifacts/percentile_proxy/issue42_percentile_proxy_20260518T110628Z/logs/percentile_scan_shard3.log b/circuit-shotting/artifacts/percentile_proxy/issue42_percentile_proxy_20260518T110628Z/logs/percentile_scan_shard3.log
new file mode 100644
index 0000000000000000000000000000000000000000..f61cd81ce1fc87dd060027f44b261d9f81d610ee
--- /dev/null
+++ b/circuit-shotting/artifacts/percentile_proxy/issue42_percentile_proxy_20260518T110628Z/logs/percentile_scan_shard3.log
@@ -0,0 +1,25 @@
+{"name": "sector_0_chunk_3", "channels": 1966, "score": 0.12398696690797806, "mean_sentence_kl": 0.05855727940797806, "tail90_sentence_kl": 0.130859375}
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diff --git a/circuit-shotting/artifacts/percentile_proxy/issue42_percentile_proxy_20260518T110628Z/logs/select_percentile_mix.log b/circuit-shotting/artifacts/percentile_proxy/issue42_percentile_proxy_20260518T110628Z/logs/select_percentile_mix.log
new file mode 100644
index 0000000000000000000000000000000000000000..34168c4b3c1a2f8ff6f8adcb6764d30becc4a10c
--- /dev/null
+++ b/circuit-shotting/artifacts/percentile_proxy/issue42_percentile_proxy_20260518T110628Z/logs/select_percentile_mix.log
@@ -0,0 +1,6 @@
+{
+  "top_n": 20,
+  "final_mix": "percentile_top_20",
+  "channels": 39321,
+  "out_dir": "/root/runs/issue42_percentile_proxy_8b46e31/selection"
+}
diff --git a/circuit-shotting/artifacts/percentile_proxy/issue42_percentile_proxy_20260518T110628Z/logs/summarize_issue42.log b/circuit-shotting/artifacts/percentile_proxy/issue42_percentile_proxy_20260518T110628Z/logs/summarize_issue42.log
new file mode 100644
index 0000000000000000000000000000000000000000..249133369b5f75304a61ac3d52f214896ddbe933
--- /dev/null
+++ b/circuit-shotting/artifacts/percentile_proxy/issue42_percentile_proxy_20260518T110628Z/logs/summarize_issue42.log
@@ -0,0 +1,4 @@
+{
+  "out_json": "/root/runs/issue42_percentile_proxy_8b46e31/summaries/issue42_summary.json",
+  "out_md": "/root/runs/issue42_percentile_proxy_8b46e31/summaries/issue42_summary.md"
+}
diff --git a/circuit-shotting/artifacts/percentile_proxy/issue42_percentile_proxy_20260518T110628Z/manifests/SHA256SUMS b/circuit-shotting/artifacts/percentile_proxy/issue42_percentile_proxy_20260518T110628Z/manifests/SHA256SUMS
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index 0000000000000000000000000000000000000000..a72317c7ae5d9c84047841666e716e93dfda2f37
--- /dev/null
+++ b/circuit-shotting/artifacts/percentile_proxy/issue42_percentile_proxy_20260518T110628Z/manifests/SHA256SUMS
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+21d3d5e6c7d617aa503ec9a18f44c957d3ba9827f80114c0b1dd5eb72bab46f4  /root/runs/issue42_percentile_proxy_8b46e31/package/issue42_percentile_proxy_20260518T110628Z/summaries/issue42_summary.json
+e17a3c7a580cd6bb852a8b9543f07cb435911ebb15656edf2926771b7a862044  /root/runs/issue42_percentile_proxy_8b46e31/package/issue42_percentile_proxy_20260518T110628Z/summaries/issue42_summary.md
diff --git a/circuit-shotting/artifacts/percentile_proxy/issue42_percentile_proxy_20260518T110628Z/manifests/manifest.json b/circuit-shotting/artifacts/percentile_proxy/issue42_percentile_proxy_20260518T110628Z/manifests/manifest.json
new file mode 100644
index 0000000000000000000000000000000000000000..6d10e6f1310cc0bbbf8523159e6fee3082af5baf
--- /dev/null
+++ b/circuit-shotting/artifacts/percentile_proxy/issue42_percentile_proxy_20260518T110628Z/manifests/manifest.json
@@ -0,0 +1,7 @@
+{
+  "upload_prefix": "issue42_percentile_proxy_20260518T110628Z",
+  "run_root": "/root/runs/issue42_percentile_proxy_8b46e31",
+  "file_count": 155,
+  "bytes": 3936795,
+  "weights_policy": "Includes proxy outputs, masks, summaries, scripts, and logs. Excludes upstream HY-MT weights, HF caches, API keys, and service tokens."
+}
diff --git a/circuit-shotting/artifacts/percentile_proxy/issue42_percentile_proxy_20260518T110628Z/scripts/actdelta_mlp_common.py b/circuit-shotting/artifacts/percentile_proxy/issue42_percentile_proxy_20260518T110628Z/scripts/actdelta_mlp_common.py
new file mode 100644
index 0000000000000000000000000000000000000000..483fdfef4ad29732263af57ebe03f9ad9ce8d14c
--- /dev/null
+++ b/circuit-shotting/artifacts/percentile_proxy/issue42_percentile_proxy_20260518T110628Z/scripts/actdelta_mlp_common.py
@@ -0,0 +1,252 @@
+#!/usr/bin/env python3
+"""Shared helpers for ActDeltaLoRA MLP MVC experiments."""
+
+from __future__ import annotations
+
+import json
+from dataclasses import dataclass
+from pathlib import Path
+from typing import Any, Iterable
+
+import numpy as np
+import torch
+import torch.nn.functional as F
+
+from translation_io import DEFAULT_PROMPT_STYLE, Pair, load_flores_devtest_any
+from translation_region_student import (
+    collate_translation,
+    decoder_root,
+    encode_supervised_row,
+    load_mask_npz,
+    save_mask_npz,
+    text_config,
+)
+
+
+@dataclass(frozen=True)
+class Candidate:
+    name: str
+    ranges: tuple[tuple[int, int], ...]
+    meta: dict[str, Any]
+
+
+def dtype_from_name(name: str) -> torch.dtype:
+    return {
+        "float32": torch.float32,
+        "float16": torch.float16,
+        "bfloat16": torch.bfloat16,
+    }[name]
+
+
+def mlp_total(n_layers: int, d_ffn: int) -> int:
+    return int(n_layers) * int(d_ffn)
+
+
+def flat_to_layer_channel(flat_idx: int, d_ffn: int) -> tuple[int, int]:
+    return int(flat_idx) // int(d_ffn), int(flat_idx) % int(d_ffn)
+
+
+def sector_ranges(total: int, sectors: int) -> list[tuple[int, int]]:
+    return [
+        (int(total * i // sectors), int(total * (i + 1) // sectors))
+        for i in range(sectors)
+    ]
+
+
+def split_range(start: int, end: int, parts: int) -> list[tuple[int, int]]:
+    size = end - start
+    return [
+        (start + int(size * i // parts), start + int(size * (i + 1) // parts))
+        for i in range(parts)
+    ]
+
+
+def normalize_ranges(ranges: Iterable[tuple[int, int]]) -> tuple[tuple[int, int], ...]:
+    out = []
+    for start, end in ranges:
+        start = int(start)
+        end = int(end)
+        if end > start:
+            out.append((start, end))
+    return tuple(out)
+
+
+def mask_from_ranges(ranges: Iterable[tuple[int, int]], n_layers: int,
+                     d_ffn: int) -> dict[int, torch.Tensor]:
+    mask = {layer: torch.zeros(d_ffn, dtype=torch.bool) for layer in range(n_layers)}
+    total = mlp_total(n_layers, d_ffn)
+    for raw_start, raw_end in ranges:
+        start = max(0, int(raw_start))
+        end = min(total, int(raw_end))
+        for flat in range(start, end):
+            layer, channel = flat_to_layer_channel(flat, d_ffn)
+            mask[layer][channel] = True
+    return mask
+
+
+def count_mask(mask: dict[int, torch.Tensor]) -> int:
+    return int(sum(int(v.sum().item()) for v in mask.values()))
+
+
+def mask_to_ranges(mask: dict[int, torch.Tensor], d_ffn: int) -> list[tuple[int, int]]:
+    flats: list[int] = []
+    for layer, values in sorted(mask.items()):
+        idx = torch.nonzero(values.cpu(), as_tuple=False).flatten().tolist()
+        flats.extend([int(layer) * d_ffn + int(i) for i in idx])
+    if not flats:
+        return []
+    flats.sort()
+    ranges: list[tuple[int, int]] = []
+    start = prev = flats[0]
+    for flat in flats[1:]:
+        if flat == prev + 1:
+            prev = flat
+            continue
+        ranges.append((start, prev + 1))
+        start = prev = flat
+    ranges.append((start, prev + 1))
+    return ranges
+
+
+def load_candidate_jsonl(path: str | Path) -> list[dict[str, Any]]:
+    rows: list[dict[str, Any]] = []
+    with Path(path).open() as f:
+        for line in f:
+            if line.strip():
+                rows.append(json.loads(line))
+    return rows
+
+
+def write_jsonl(path: str | Path, rows: Iterable[dict[str, Any]]) -> None:
+    out = Path(path)
+    out.parent.mkdir(parents=True, exist_ok=True)
+    with out.open("w") as f:
+        for row in rows:
+            f.write(json.dumps(row, ensure_ascii=False) + "\n")
+
+
+def load_pairs(path: str | None, *, src_lang: str = "eng_Latn",
+               tgt_lang: str = "por_Latn", max_rows: int | None = None) -> list[Pair]:
+    if path is None:
+        return load_flores_devtest_any(
+            src_lang=src_lang,
+            tgt_lang=tgt_lang,
+            max_examples=max_rows,
+        )
+    rows: list[Pair] = []
+    with Path(path).open() as f:
+        for line in f:
+            if not line.strip():
+                continue
+            raw = json.loads(line)
+            src = (raw.get("en") or raw.get("src") or "").strip()
+            tgt = (raw.get("model_hyp") or raw.get("pt") or raw.get("tgt") or "").strip()
+            if src and tgt:
+                rows.append(Pair(src=src, tgt=tgt))
+                if max_rows is not None and len(rows) >= max_rows:
+                    break
+    return rows
+
+
+def build_supervised_examples(pairs: list[Pair], tokenizer, *, target_language: str,
+                              prompt_style: str = DEFAULT_PROMPT_STYLE,
+                              max_seq_length: int = 1024) -> list[dict[str, Any]]:
+    examples: list[dict[str, Any]] = []
+    for pair in pairs:
+        row = {"en": pair.src, "target": pair.tgt, "raw": {"en": pair.src, "pt": pair.tgt}}
+        enc = encode_supervised_row(
+            row,
+            tokenizer,
+            target_language=target_language,
+            prompt_style=prompt_style,
+            max_seq_length=max_seq_length,
+            kl_on="answer",
+        )
+        if enc is not None:
+            examples.append(enc)
+    return examples
+
+
+def iter_batches(examples: list[dict[str, Any]], *, batch_size: int, pad_id: int):
+    for start in range(0, len(examples), batch_size):
+        yield collate_translation(examples[start:start + batch_size], pad_id)
+
+
+def move_batch(batch: dict[str, Any], device: str) -> dict[str, Any]:
+    return {k: v.to(device) if torch.is_tensor(v) else v for k, v in batch.items()}
+
+
+def tokenwise_kl(teacher_logits: torch.Tensor, student_logits: torch.Tensor,
+                 logit_mask: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]:
+    mask = logit_mask[:, :-1]
+    t_logits = teacher_logits[:, :-1, :]
+    s_logits = student_logits[:, :-1, :]
+    log_t = F.log_softmax(t_logits, dim=-1)
+    prob_t = log_t.exp()
+    log_s = F.log_softmax(s_logits, dim=-1)
+    kl = (prob_t * (log_t - log_s)).sum(dim=-1)
+    return kl, mask
+
+
+def sentence_kl_values(teacher_logits: torch.Tensor, student_logits: torch.Tensor,
+                       logit_mask: torch.Tensor) -> list[float]:
+    kl, mask = tokenwise_kl(teacher_logits, student_logits, logit_mask)
+    values: list[float] = []
+    for row_idx in range(kl.shape[0]):
+        row_mask = mask[row_idx]
+        if bool(row_mask.any().item()):
+            values.append(float(kl[row_idx][row_mask].mean().detach().cpu().item()))
+        else:
+            values.append(0.0)
+    return values
+
+
+def install_mlp_nuke_hooks(model, nuke_mask: dict[int, torch.Tensor],
+                           *, device: str, dtype: torch.dtype) -> list[Any]:
+    hooks: list[Any] = []
+    root = decoder_root(model)
+    for layer_idx, layer in enumerate(root.layers):
+        selected = nuke_mask[layer_idx].to(device)
+        if not bool(selected.any().item()):
+            continue
+        keep = (~selected).to(dtype=dtype).view(1, 1, -1)
+
+        def make_hook(keep: torch.Tensor):
+            def hook_fn(module, hook_args):
+                act = hook_args[0]
+                return (act * keep,) + hook_args[1:]
+            return hook_fn
+
+        hooks.append(layer.mlp.down_proj.register_forward_pre_hook(make_hook(keep)))
+    return hooks
+
+
+def install_mlp_keep_only_hooks(model, keep_mask: dict[int, torch.Tensor],
+                                *, device: str, dtype: torch.dtype) -> list[Any]:
+    """Keep selected MLP down-proj input channels and zero every other MLP channel."""
+    hooks: list[Any] = []
+    root = decoder_root(model)
+    for layer_idx, layer in enumerate(root.layers):
+        keep = keep_mask[layer_idx].to(device=device, dtype=dtype).view(1, 1, -1)
+
+        def make_hook(keep: torch.Tensor):
+            def hook_fn(module, hook_args):
+                act = hook_args[0]
+                return (act * keep,) + hook_args[1:]
+            return hook_fn
+
+        hooks.append(layer.mlp.down_proj.register_forward_pre_hook(make_hook(keep)))
+    return hooks
+
+
+def save_mask_for_candidate(out_dir: Path, candidate: Candidate, n_layers: int,
+                            d_ffn: int) -> str:
+    mask = mask_from_ranges(candidate.ranges, n_layers, d_ffn)
+    path = out_dir / f"{candidate.name}.full.npz"
+    save_mask_npz(path, mask)
+    return str(path)
+
+
+def model_mlp_shape(model) -> tuple[int, int, int]:
+    cfg = text_config(model)
+    return int(cfg.num_hidden_layers), int(cfg.hidden_size), int(cfg.intermediate_size)
diff --git a/circuit-shotting/artifacts/percentile_proxy/issue42_percentile_proxy_20260518T110628Z/scripts/actdelta_mlp_damage_scan.py b/circuit-shotting/artifacts/percentile_proxy/issue42_percentile_proxy_20260518T110628Z/scripts/actdelta_mlp_damage_scan.py
new file mode 100644
index 0000000000000000000000000000000000000000..05b5a68b38d46fcc0acb7bb7fdeb96bfd8d4479a
--- /dev/null
+++ b/circuit-shotting/artifacts/percentile_proxy/issue42_percentile_proxy_20260518T110628Z/scripts/actdelta_mlp_damage_scan.py
@@ -0,0 +1,274 @@
+#!/usr/bin/env python3
+"""Teacher-forced MLP nuke damage scan for ActDeltaLoRA MVC."""
+
+from __future__ import annotations
+
+import argparse
+import json
+import statistics
+import time
+from pathlib import Path
+from typing import Any
+
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+from actdelta_mlp_common import (
+    Candidate,
+    build_supervised_examples,
+    count_mask,
+    dtype_from_name,
+    install_mlp_nuke_hooks,
+    iter_batches,
+    load_candidate_jsonl,
+    load_pairs,
+    mask_from_ranges,
+    mlp_total,
+    model_mlp_shape,
+    move_batch,
+    normalize_ranges,
+    save_mask_for_candidate,
+    sector_ranges,
+    sentence_kl_values,
+    split_range,
+    write_jsonl,
+)
+
+
+def parse_args() -> argparse.Namespace:
+    p = argparse.ArgumentParser(description=__doc__)
+    p.add_argument("--model", default="tencent/HY-MT1.5-1.8B")
+    p.add_argument("--mode", choices=["sectors", "chunks", "cumulative"], required=True)
+    p.add_argument("--out-dir", required=True)
+    p.add_argument("--out-jsonl", required=True)
+    p.add_argument("--input-jsonl", default=None)
+    p.add_argument("--max-rows", type=int, default=512)
+    p.add_argument("--batch-size", type=int, default=4)
+    p.add_argument("--max-seq-length", type=int, default=1024)
+    p.add_argument("--target-language", default="Portuguese")
+    p.add_argument("--src-lang", default="eng_Latn")
+    p.add_argument("--tgt-lang", default="por_Latn")
+    p.add_argument("--device", default="cuda")
+    p.add_argument("--dtype", default="bfloat16", choices=["float32", "float16", "bfloat16"])
+    p.add_argument("--sectors", type=int, default=10)
+    p.add_argument("--parent-sectors", default=None, help="comma-separated sector ids for --mode chunks")
+    p.add_argument("--chunks-per-sector", type=int, default=10)
+    p.add_argument("--chunk-results", default=None, help="jsonl from chunks mode for cumulative mode")
+    p.add_argument("--top-n", default="1,2,3,4,5,6,7,8,9,10")
+    p.add_argument("--num-shards", type=int, default=1)
+    p.add_argument("--shard-index", type=int, default=0)
+    p.add_argument("--write-masks", action=argparse.BooleanOptionalAction, default=True)
+    return p.parse_args()
+
+
+def parse_int_list(value: str | None) -> list[int]:
+    if value is None or not value.strip():
+        return []
+    return [int(x) for x in value.split(",") if x.strip()]
+
+
+def build_candidates(args: argparse.Namespace, *, n_layers: int, d_ffn: int) -> list[Candidate]:
+    total = mlp_total(n_layers, d_ffn)
+    sectors = sector_ranges(total, args.sectors)
+    candidates: list[Candidate] = []
+    if args.mode == "sectors":
+        for idx, (start, end) in enumerate(sectors):
+            candidates.append(Candidate(
+                name=f"sector_{idx}",
+                ranges=((start, end),),
+                meta={"kind": "sector", "sector": idx, "flat_start": start, "flat_end": end},
+            ))
+        return candidates
+
+    if args.mode == "chunks":
+        parent_ids = parse_int_list(args.parent_sectors)
+        if not parent_ids:
+            raise ValueError("--parent-sectors is required for chunks mode")
+        for sector_id in parent_ids:
+            start, end = sectors[sector_id]
+            for chunk_idx, (chunk_start, chunk_end) in enumerate(
+                split_range(start, end, args.chunks_per_sector)
+            ):
+                candidates.append(Candidate(
+                    name=f"sector_{sector_id}_chunk_{chunk_idx}",
+                    ranges=((chunk_start, chunk_end),),
+                    meta={
+                        "kind": "chunk",
+                        "sector": sector_id,
+                        "chunk": chunk_idx,
+                        "flat_start": chunk_start,
+                        "flat_end": chunk_end,
+                    },
+                ))
+        return candidates
+
+    rows = load_candidate_jsonl(args.chunk_results)
+    rows.sort(key=lambda row: float(row["score"]), reverse=True)
+    top_ns = parse_int_list(args.top_n)
+    for top_n in top_ns:
+        selected = rows[:top_n]
+        ranges: list[tuple[int, int]] = []
+        for row in selected:
+            ranges.extend((int(a), int(b)) for a, b in row["ranges"])
+        candidates.append(Candidate(
+            name=f"cumulative_top_{top_n}",
+            ranges=normalize_ranges(ranges),
+            meta={
+                "kind": "cumulative",
+                "top_n": top_n,
+                "chunk_names": [row["name"] for row in selected],
+            },
+        ))
+    return candidates
+
+
+def quantile(values: list[float], q: float) -> float:
+    if not values:
+        return 0.0
+    ordered = sorted(values)
+    idx = min(len(ordered) - 1, max(0, int(round(q * (len(ordered) - 1)))))
+    return float(ordered[idx])
+
+
+def evaluate_candidate(model, examples: list[dict[str, Any]], candidate: Candidate,
+                       *, n_layers: int, d_ffn: int, pad_id: int, args: argparse.Namespace,
+                       dtype: torch.dtype) -> dict[str, Any]:
+    mask = mask_from_ranges(candidate.ranges, n_layers, d_ffn)
+    kept = count_mask(mask)
+    sent_values: list[float] = []
+    total_tokens = 0
+    t0 = time.time()
+    for batch_cpu in iter_batches(examples, batch_size=args.batch_size, pad_id=pad_id):
+        batch = move_batch(batch_cpu, args.device)
+        with torch.no_grad():
+            teacher = model(
+                input_ids=batch["input_ids"],
+                attention_mask=batch["attention_mask"],
+                use_cache=False,
+            ).logits.detach()
+            hooks = install_mlp_nuke_hooks(model, mask, device=args.device, dtype=dtype)
+            try:
+                nuked = model(
+                    input_ids=batch["input_ids"],
+                    attention_mask=batch["attention_mask"],
+                    use_cache=False,
+                ).logits.detach()
+            finally:
+                for hook in hooks:
+                    hook.remove()
+        values = sentence_kl_values(teacher, nuked, batch["kl_logit_mask"])
+        sent_values.extend(values)
+        total_tokens += int(batch["kl_logit_mask"][:, :-1].sum().item())
+
+    mean_kl = statistics.fmean(sent_values) if sent_values else 0.0
+    tail_kl = quantile(sent_values, 0.90)
+    score = mean_kl + 0.5 * tail_kl
+    return {
+        "name": candidate.name,
+        "ranges": [list(r) for r in candidate.ranges],
+        "channels": kept,
+        "channel_fraction": kept / max(mlp_total(n_layers, d_ffn), 1),
+        "mean_sentence_kl": mean_kl,
+        "tail90_sentence_kl": tail_kl,
+        "score": score,
+        "sentence_kl": sent_values,
+        "answer_tokens": total_tokens,
+        "elapsed_s": time.time() - t0,
+        **candidate.meta,
+    }
+
+
+def main() -> None:
+    args = parse_args()
+    if args.shard_index < 0 or args.shard_index >= args.num_shards:
+        raise ValueError("--shard-index must be in [0, num_shards)")
+    out_dir = Path(args.out_dir)
+    mask_dir = out_dir / "masks"
+    out_dir.mkdir(parents=True, exist_ok=True)
+    mask_dir.mkdir(parents=True, exist_ok=True)
+    dtype = dtype_from_name(args.dtype)
+
+    tokenizer = AutoTokenizer.from_pretrained(args.model)
+    if tokenizer.pad_token_id is None:
+        tokenizer.pad_token = tokenizer.eos_token
+    model = AutoModelForCausalLM.from_pretrained(
+        args.model,
+        dtype=dtype,
+        attn_implementation="eager",
+    ).to(args.device).eval()
+    model.config.use_cache = False
+    for param in model.parameters():
+        param.requires_grad_(False)
+
+    n_layers, hidden_size, d_ffn = model_mlp_shape(model)
+    pairs = load_pairs(
+        args.input_jsonl,
+        src_lang=args.src_lang,
+        tgt_lang=args.tgt_lang,
+        max_rows=args.max_rows,
+    )
+    examples = build_supervised_examples(
+        pairs,
+        tokenizer,
+        target_language=args.target_language,
+        max_seq_length=args.max_seq_length,
+    )
+    if not examples:
+        raise ValueError("no supervised examples built")
+
+    candidates = build_candidates(args, n_layers=n_layers, d_ffn=d_ffn)
+    candidates = [
+        candidate for idx, candidate in enumerate(candidates)
+        if idx % args.num_shards == args.shard_index
+    ]
+
+    rows = []
+    for candidate in candidates:
+        row = evaluate_candidate(
+            model,
+            examples,
+            candidate,
+            n_layers=n_layers,
+            d_ffn=d_ffn,
+            pad_id=int(tokenizer.pad_token_id),
+            args=args,
+            dtype=dtype,
+        )
+        if args.write_masks:
+            row["mask_path"] = save_mask_for_candidate(mask_dir, candidate, n_layers, d_ffn)
+        row["n_layers"] = n_layers
+        row["hidden_size"] = hidden_size
+        row["d_ffn"] = d_ffn
+        row["mode"] = args.mode
+        row["shard_index"] = args.shard_index
+        rows.append(row)
+        print(json.dumps({
+            "name": row["name"],
+            "channels": row["channels"],
+            "score": row["score"],
+            "mean_sentence_kl": row["mean_sentence_kl"],
+            "tail90_sentence_kl": row["tail90_sentence_kl"],
+        }), flush=True)
+
+    write_jsonl(args.out_jsonl, rows)
+    summary = {
+        "model": args.model,
+        "mode": args.mode,
+        "n_rows": len(pairs),
+        "n_examples": len(examples),
+        "n_layers": n_layers,
+        "hidden_size": hidden_size,
+        "d_ffn": d_ffn,
+        "mlp_total": mlp_total(n_layers, d_ffn),
+        "num_shards": args.num_shards,
+        "shard_index": args.shard_index,
+        "candidate_count": len(rows),
+        "out_jsonl": args.out_jsonl,
+    }
+    Path(args.out_jsonl).with_suffix(".summary.json").write_text(
+        json.dumps(summary, indent=2, ensure_ascii=False) + "\n"
+    )
+
+
+if __name__ == "__main__":
+    main()
diff --git a/circuit-shotting/artifacts/percentile_proxy/issue42_percentile_proxy_20260518T110628Z/scripts/evaluate_mlp_proxy_translation.py b/circuit-shotting/artifacts/percentile_proxy/issue42_percentile_proxy_20260518T110628Z/scripts/evaluate_mlp_proxy_translation.py
new file mode 100644
index 0000000000000000000000000000000000000000..1f42521bbf44ed32483377baddf3cc2cb10ba626
--- /dev/null
+++ b/circuit-shotting/artifacts/percentile_proxy/issue42_percentile_proxy_20260518T110628Z/scripts/evaluate_mlp_proxy_translation.py
@@ -0,0 +1,245 @@
+#!/usr/bin/env python3
+"""Teacher-forced proxy evals for MLP nuke and substrate-only interventions."""
+
+from __future__ import annotations
+
+import argparse
+from collections import defaultdict
+import json
+import statistics
+import time
+from pathlib import Path
+from typing import Any
+
+import torch
+import torch.nn.functional as F
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+from actdelta_mlp_common import (
+    build_supervised_examples,
+    count_mask,
+    dtype_from_name,
+    install_mlp_keep_only_hooks,
+    install_mlp_nuke_hooks,
+    iter_batches,
+    load_pairs,
+    model_mlp_shape,
+    move_batch,
+    tokenwise_kl,
+)
+from translation_region_student import load_mask_npz
+
+
+def parse_args() -> argparse.Namespace:
+    p = argparse.ArgumentParser(description=__doc__)
+    p.add_argument("--model", default="tencent/HY-MT1.5-1.8B")
+    p.add_argument("--system", action="append", default=[],
+                   help="NAME:INTERVENTION:PATH, where intervention is nuke-zero or keep-only.")
+    p.add_argument("--include-zero-all", action="store_true")
+    p.add_argument("--out-json", required=True)
+    p.add_argument("--input-jsonl", default=None)
+    p.add_argument("--max-rows", type=int, default=512)
+    p.add_argument("--batch-size", type=int, default=4)
+    p.add_argument("--max-seq-length", type=int, default=1024)
+    p.add_argument("--target-language", default="Portuguese")
+    p.add_argument("--src-lang", default="eng_Latn")
+    p.add_argument("--tgt-lang", default="por_Latn")
+    p.add_argument("--device", default="cuda")
+    p.add_argument("--dtype", default="bfloat16", choices=["float32", "float16", "bfloat16"])
+    return p.parse_args()
+
+
+def parse_systems(values: list[str]) -> list[dict[str, Any]]:
+    systems = []
+    for value in values:
+        parts = value.split(":", 2)
+        if len(parts) != 3:
+            raise ValueError(f"--system must be NAME:INTERVENTION:PATH, got {value!r}")
+        name, intervention, path = parts
+        if intervention not in {"nuke-zero", "keep-only"}:
+            raise ValueError(f"bad intervention {intervention!r}")
+        systems.append({"name": name, "intervention": intervention, "path": path})
+    return systems
+
+
+def quantile(values: list[float], q: float) -> float:
+    if not values:
+        return 0.0
+    ordered = sorted(values)
+    idx = min(len(ordered) - 1, max(0, int(round(q * (len(ordered) - 1)))))
+    return float(ordered[idx])
+
+
+def update_metrics(acc: dict[str, Any], teacher_logits: torch.Tensor,
+                   student_logits: torch.Tensor, logit_mask: torch.Tensor) -> None:
+    kl, mask = tokenwise_kl(teacher_logits, student_logits, logit_mask)
+    mask = mask.bool()
+    token_count = int(mask.sum().item())
+    if token_count == 0:
+        return
+    masked_kl = kl[mask].detach().to(torch.float32)
+    acc["kl_sum"] += float(masked_kl.sum().cpu().item())
+    acc["token_count"] += token_count
+
+    for row_idx in range(kl.shape[0]):
+        row_mask = mask[row_idx]
+        if bool(row_mask.any().item()):
+            acc["sentence_kl"].append(float(kl[row_idx][row_mask].mean().detach().cpu().item()))
+
+    teacher_slice = teacher_logits[:, :-1, :]
+    student_slice = student_logits[:, :-1, :]
+    teacher_argmax = teacher_slice.argmax(dim=-1)
+    student_argmax = student_slice.argmax(dim=-1)
+    acc["teacher_top1_match"] += int((teacher_argmax[mask] == student_argmax[mask]).sum().item())
+
+    student_top5 = student_slice.topk(k=5, dim=-1).indices
+    teacher_selected = teacher_argmax[mask].unsqueeze(-1)
+    student_top5_selected = student_top5[mask]
+    acc["teacher_top5_contains"] += int((student_top5_selected == teacher_selected).any(dim=-1).sum().item())
+
+    log_probs = F.log_softmax(student_slice, dim=-1)
+    teacher_token_nll = -log_probs.gather(dim=-1, index=teacher_argmax.unsqueeze(-1)).squeeze(-1)
+    acc["teacher_argmax_nll_sum"] += float(teacher_token_nll[mask].detach().to(torch.float32).sum().cpu().item())
+
+
+def finalize_metrics(acc: dict[str, Any], *, elapsed_s: float,
+                     zero_all_mean_token_kl: float | None = None) -> dict[str, Any]:
+    token_count = int(acc["token_count"])
+    sentence_kl = list(acc["sentence_kl"])
+    mean_token_kl = acc["kl_sum"] / max(token_count, 1)
+    row = {
+        "mean_token_kl": mean_token_kl,
+        "mean_sentence_kl": statistics.fmean(sentence_kl) if sentence_kl else 0.0,
+        "tail90_sentence_kl": quantile(sentence_kl, 0.90),
+        "teacher_top1_agreement": acc["teacher_top1_match"] / max(token_count, 1),
+        "teacher_top5_contains": acc["teacher_top5_contains"] / max(token_count, 1),
+        "teacher_argmax_nll": acc["teacher_argmax_nll_sum"] / max(token_count, 1),
+        "answer_tokens": token_count,
+        "n_sentences": len(sentence_kl),
+        "elapsed_s": elapsed_s,
+    }
+    if zero_all_mean_token_kl is not None:
+        row["kl_gap_recovery_vs_zero_all"] = 1.0 - (mean_token_kl / max(zero_all_mean_token_kl, 1e-12))
+        row["kl_ratio_vs_zero_all"] = mean_token_kl / max(zero_all_mean_token_kl, 1e-12)
+    return row
+
+
+def empty_mask(n_layers: int, d_ffn: int) -> dict[int, torch.Tensor]:
+    return {layer: torch.zeros(d_ffn, dtype=torch.bool) for layer in range(n_layers)}
+
+
+def main() -> None:
+    args = parse_args()
+    dtype = dtype_from_name(args.dtype)
+    out_path = Path(args.out_json)
+    out_path.parent.mkdir(parents=True, exist_ok=True)
+
+    tokenizer = AutoTokenizer.from_pretrained(args.model)
+    if tokenizer.pad_token_id is None:
+        tokenizer.pad_token = tokenizer.eos_token
+    model = AutoModelForCausalLM.from_pretrained(
+        args.model,
+        dtype=dtype,
+        attn_implementation="eager",
+    ).to(args.device).eval()
+    model.config.use_cache = False
+    for param in model.parameters():
+        param.requires_grad_(False)
+
+    n_layers, hidden_size, d_ffn = model_mlp_shape(model)
+    systems = parse_systems(args.system)
+    if args.include_zero_all:
+        systems.insert(0, {"name": "all_mlp_zero", "intervention": "keep-only", "path": None})
+    if not systems:
+        raise ValueError("no proxy systems requested")
+
+    loaded_systems = []
+    for spec in systems:
+        mask = empty_mask(n_layers, d_ffn) if spec["path"] is None else load_mask_npz(spec["path"], n_layers, d_ffn)
+        loaded_systems.append({**spec, "mask": mask, "channels": count_mask(mask)})
+
+    pairs = load_pairs(
+        args.input_jsonl,
+        src_lang=args.src_lang,
+        tgt_lang=args.tgt_lang,
+        max_rows=args.max_rows,
+    )
+    examples = build_supervised_examples(
+        pairs,
+        tokenizer,
+        target_language=args.target_language,
+        max_seq_length=args.max_seq_length,
+    )
+    if not examples:
+        raise ValueError("no supervised examples built")
+
+    accs = {
+        system["name"]: defaultdict(float, {"sentence_kl": []})
+        for system in loaded_systems
+    }
+    start_times = {system["name"]: time.time() for system in loaded_systems}
+    elapsed = {system["name"]: 0.0 for system in loaded_systems}
+
+    for batch_cpu in iter_batches(examples, batch_size=args.batch_size, pad_id=int(tokenizer.pad_token_id)):
+        batch = move_batch(batch_cpu, args.device)
+        with torch.no_grad():
+            teacher_logits = model(
+                input_ids=batch["input_ids"],
+                attention_mask=batch["attention_mask"],
+                use_cache=False,
+            ).logits.detach()
+
+            for system in loaded_systems:
+                t0 = time.time()
+                if system["intervention"] == "nuke-zero":
+                    hooks = install_mlp_nuke_hooks(model, system["mask"], device=args.device, dtype=dtype)
+                else:
+                    hooks = install_mlp_keep_only_hooks(model, system["mask"], device=args.device, dtype=dtype)
+                try:
+                    student_logits = model(
+                        input_ids=batch["input_ids"],
+                        attention_mask=batch["attention_mask"],
+                        use_cache=False,
+                    ).logits.detach()
+                finally:
+                    for hook in hooks:
+                        hook.remove()
+                elapsed[system["name"]] += time.time() - t0
+                update_metrics(accs[system["name"]], teacher_logits, student_logits, batch["kl_logit_mask"])
+
+    zero_kl = None
+    if "all_mlp_zero" in accs:
+        zero_kl = accs["all_mlp_zero"]["kl_sum"] / max(int(accs["all_mlp_zero"]["token_count"]), 1)
+
+    result_rows = {}
+    for system in loaded_systems:
+        metrics = finalize_metrics(
+            accs[system["name"]],
+            elapsed_s=elapsed[system["name"]],
+            zero_all_mean_token_kl=zero_kl,
+        )
+        result_rows[system["name"]] = {
+            "intervention": system["intervention"],
+            "mask_path": system["path"],
+            "channels": system["channels"],
+            "channel_fraction": system["channels"] / max(n_layers * d_ffn, 1),
+            "metrics": metrics,
+        }
+        print(json.dumps({"name": system["name"], **result_rows[system["name"]]}, ensure_ascii=False), flush=True)
+
+    payload = {
+        "model": args.model,
+        "n_rows": len(pairs),
+        "n_examples": len(examples),
+        "n_layers": n_layers,
+        "hidden_size": hidden_size,
+        "d_ffn": d_ffn,
+        "mlp_total": n_layers * d_ffn,
+        "zero_all_mean_token_kl": zero_kl,
+        "results": result_rows,
+    }
+    out_path.write_text(json.dumps(payload, indent=2, ensure_ascii=False) + "\n")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/circuit-shotting/artifacts/percentile_proxy/issue42_percentile_proxy_20260518T110628Z/scripts/issue42_percentile_proxy_runner.sh b/circuit-shotting/artifacts/percentile_proxy/issue42_percentile_proxy_20260518T110628Z/scripts/issue42_percentile_proxy_runner.sh
new file mode 100644
index 0000000000000000000000000000000000000000..c2a9f681413ac4ef06b3df6399c4d4975e00655e
--- /dev/null
+++ b/circuit-shotting/artifacts/percentile_proxy/issue42_percentile_proxy_20260518T110628Z/scripts/issue42_percentile_proxy_runner.sh
@@ -0,0 +1,164 @@
+#!/usr/bin/env bash
+# Issue #42 proxy-only destructive 1%ile search and substrate eval.
+set -euo pipefail
+
+export TOKENIZERS_PARALLELISM="${TOKENIZERS_PARALLELISM:-false}"
+export TORCH_ALLOW_TF32_CUBLAS_OVERRIDE="${TORCH_ALLOW_TF32_CUBLAS_OVERRIDE:-1}"
+
+REPO_DIR="${REPO_DIR:-/root/work/circuit-shotting}"
+cd "$REPO_DIR"
+
+MODEL="${MODEL:-tencent/HY-MT1.5-1.8B}"
+RUN_ROOT="${RUN_ROOT:-/root/runs/issue42_percentile_proxy}"
+TRAIN_JSONL="${TRAIN_JSONL:-}"
+GPU_LIST="${GPU_LIST:-}"
+DAMAGE_MAX_ROWS="${DAMAGE_MAX_ROWS:-256}"
+PROXY_MAX_ROWS="${PROXY_MAX_ROWS:-512}"
+BATCH_SIZE_KL="${BATCH_SIZE_KL:-4}"
+PROXY_BATCH_SIZE="${PROXY_BATCH_SIZE:-4}"
+TOP_N="${TOP_N:-20}"
+UPLOAD_AFTER="${UPLOAD_AFTER:-0}"
+
+mkdir -p "$RUN_ROOT"/{logs,damage,selection,eval,summaries,package}
+
+mark() {
+  echo "[$(date -Iseconds)] $*" | tee -a "$RUN_ROOT/logs/issue42_progress.log"
+}
+
+detect_gpus() {
+  if [[ -n "$GPU_LIST" ]]; then
+    echo "$GPU_LIST" | tr ',' ' '
+    return
+  fi
+  nvidia-smi --query-gpu=index --format=csv,noheader | tr '\n' ' '
+}
+
+read -r -a GPU_ARRAY <<<"$(detect_gpus)"
+GPU_COUNT="${#GPU_ARRAY[@]}"
+if ((GPU_COUNT < 1)); then
+  echo "no GPUs found" >&2
+  exit 2
+fi
+
+wait_pids() {
+  local failed=0
+  for pid in "$@"; do
+    if ! wait "$pid"; then
+      failed=1
+    fi
+  done
+  return "$failed"
+}
+
+merge_jsonl() {
+  local out="$1"
+  shift
+  python3 - "$out" "$@" <<'PY'
+import sys
+from pathlib import Path
+out = Path(sys.argv[1])
+parts = [Path(p) for p in sys.argv[2:]]
+out.parent.mkdir(parents=True, exist_ok=True)
+with out.open("w") as w:
+    for part in parts:
+        if not part.exists():
+            continue
+        with part.open() as r:
+            for line in r:
+                if line.strip():
+                    w.write(line)
+print(out)
+PY
+}
+
+run_percentile_damage_scan() {
+  local merged="$RUN_ROOT/damage/percentile_results.jsonl"
+  if [[ -s "$merged" ]]; then
+    return 0
+  fi
+  mark "run 100x 1%ile MLP nuke KL damage scan over $GPU_COUNT GPUs"
+  pids=()
+  parts=()
+  for ((j=0; j<GPU_COUNT; j++)); do
+    part="$RUN_ROOT/damage/percentile_results_shard${j}.jsonl"
+    parts+=("$part")
+    CUDA_VISIBLE_DEVICES="${GPU_ARRAY[$j]}" python3 actdelta_mlp_damage_scan.py \
+      --model "$MODEL" \
+      --mode chunks \
+      --sectors 1 \
+      --parent-sectors 0 \
+      --chunks-per-sector 100 \
+      --out-dir "$RUN_ROOT/damage/percentile_shard${j}" \
+      --out-jsonl "$part" \
+      ${TRAIN_JSONL:+--input-jsonl "$TRAIN_JSONL"} \
+      --max-rows "$DAMAGE_MAX_ROWS" \
+      --batch-size "$BATCH_SIZE_KL" \
+      --num-shards "$GPU_COUNT" \
+      --shard-index "$j" \
+      > "$RUN_ROOT/logs/percentile_scan_shard${j}.log" 2>&1 &
+    pids+=("$!")
+  done
+  wait_pids "${pids[@]}"
+  merge_jsonl "$merged" "${parts[@]}" > "$RUN_ROOT/logs/merge_percentile_results.log" 2>&1
+}
+
+select_top20() {
+  if [[ -s "$RUN_ROOT/selection/selection_summary.json" ]]; then
+    return 0
+  fi
+  mark "select top-$TOP_N destructive 1%ile union"
+  python3 select_percentile_mlp_mix.py \
+    --chunk-results "$RUN_ROOT/damage/percentile_results.jsonl" \
+    --out-dir "$RUN_ROOT/selection" \
+    --top-n "$TOP_N" \
+    > "$RUN_ROOT/logs/select_percentile_mix.log" 2>&1
+}
+
+top20_mask() {
+  python3 - "$RUN_ROOT/selection/selection_summary.json" <<'PY'
+import json, sys
+s = json.load(open(sys.argv[1]))
+print(s["final_mix"]["mask_path"])
+PY
+}
+
+run_proxy_eval() {
+  local out="$RUN_ROOT/eval/top20_proxy.json"
+  if [[ -s "$out" ]]; then
+    return 0
+  fi
+  local mask
+  mask="$(top20_mask)"
+  mark "run teacher-forced proxy eval for all-zero, nuked top-$TOP_N, and substrate-only top-$TOP_N"
+  CUDA_VISIBLE_DEVICES="${GPU_ARRAY[0]}" python3 evaluate_mlp_proxy_translation.py \
+    --model "$MODEL" \
+    --out-json "$out" \
+    ${TRAIN_JSONL:+--input-jsonl "$TRAIN_JSONL"} \
+    --max-rows "$PROXY_MAX_ROWS" \
+    --batch-size "$PROXY_BATCH_SIZE" \
+    --include-zero-all \
+    --system "nuked_percentile_top_${TOP_N}:nuke-zero:$mask" \
+    --system "native_percentile_top_${TOP_N}_substrate_only:keep-only:$mask" \
+    > "$RUN_ROOT/logs/eval_top20_proxy.log" 2>&1
+}
+
+summarize() {
+  mark "summarize issue42"
+  python3 summarize_issue42_percentile_proxy.py \
+    --run-root "$RUN_ROOT" \
+    --out-json "$RUN_ROOT/summaries/issue42_summary.json" \
+    --out-md "$RUN_ROOT/summaries/issue42_summary.md" \
+    > "$RUN_ROOT/logs/summarize_issue42.log" 2>&1
+}
+
+mark "issue42 start percentile proxy gpus=${GPU_ARRAY[*]}"
+run_percentile_damage_scan
+select_top20
+run_proxy_eval
+summarize
+if [[ "$UPLOAD_AFTER" == "1" || "$UPLOAD_AFTER" == "true" ]]; then
+  mark "package and upload issue42 artifacts"
+  RUN_ROOT="$RUN_ROOT" REPO_DIR="$REPO_DIR" bash scripts/package_issue42_hf_upload.sh \
+    > "$RUN_ROOT/logs/package_issue42_hf_upload.log" 2>&1
+fi
+mark "issue42 done"
diff --git a/circuit-shotting/artifacts/percentile_proxy/issue42_percentile_proxy_20260518T110628Z/scripts/package_issue42_hf_upload.sh b/circuit-shotting/artifacts/percentile_proxy/issue42_percentile_proxy_20260518T110628Z/scripts/package_issue42_hf_upload.sh
new file mode 100644
index 0000000000000000000000000000000000000000..8f1420dca40c2e995ff01f4970badabf089376dd
--- /dev/null
+++ b/circuit-shotting/artifacts/percentile_proxy/issue42_percentile_proxy_20260518T110628Z/scripts/package_issue42_hf_upload.sh
@@ -0,0 +1,71 @@
+#!/usr/bin/env bash
+# Package and upload issue #42 percentile proxy artifacts.
+set -euo pipefail
+
+RUN_ROOT="${RUN_ROOT:-/root/runs/issue42_percentile_proxy}"
+REPO_DIR="${REPO_DIR:-/root/work/circuit-shotting}"
+STAMP="$(date -u +%Y%m%dT%H%M%SZ)"
+UPLOAD_PREFIX="${UPLOAD_PREFIX:-issue42_percentile_proxy_$STAMP}"
+SYNTH_HF_REPO="${SYNTH_HF_REPO:-TokenBender/synth-data-en-pt-circuit}"
+CIRCUIT_HF_REPO="${CIRCUIT_HF_REPO:-TokenBender/circuit-discovery}"
+UPLOAD_DIR="$RUN_ROOT/package/$UPLOAD_PREFIX"
+
+rm -rf "$UPLOAD_DIR"
+mkdir -p "$UPLOAD_DIR"/{spec,scripts,damage,selection,eval,summaries,logs,manifests}
+
+copy_if_present() {
+  local src="$1"
+  local dst="$2"
+  if [[ -e "$src" ]]; then
+    mkdir -p "$(dirname "$dst")"
+    cp -a "$src" "$dst"
+  fi
+}
+
+copy_if_present "$REPO_DIR/configs/issue42_percentile_proxy.json" "$UPLOAD_DIR/spec/issue42_percentile_proxy.json"
+
+for path in \
+  actdelta_mlp_common.py \
+  actdelta_mlp_damage_scan.py \
+  select_percentile_mlp_mix.py \
+  evaluate_mlp_proxy_translation.py \
+  summarize_issue42_percentile_proxy.py \
+  issue42_percentile_proxy_runner.sh \
+  scripts/package_issue42_hf_upload.sh; do
+  copy_if_present "$REPO_DIR/$path" "$UPLOAD_DIR/scripts/$(basename "$path")"
+done
+
+cp -a "$RUN_ROOT/damage"/* "$UPLOAD_DIR/damage/" 2>/dev/null || true
+cp -a "$RUN_ROOT/selection"/* "$UPLOAD_DIR/selection/" 2>/dev/null || true
+cp -a "$RUN_ROOT/eval"/* "$UPLOAD_DIR/eval/" 2>/dev/null || true
+cp -a "$RUN_ROOT/summaries"/* "$UPLOAD_DIR/summaries/" 2>/dev/null || true
+cp -a "$RUN_ROOT/logs"/* "$UPLOAD_DIR/logs/" 2>/dev/null || true
+
+find "$UPLOAD_DIR" -type f -print0 | sort -z | xargs -0 sha256sum > "$UPLOAD_DIR/manifests/SHA256SUMS"
+python3 - "$UPLOAD_DIR" "$RUN_ROOT" "$UPLOAD_PREFIX" <<'PY'
+import json, sys
+from pathlib import Path
+upload = Path(sys.argv[1])
+run_root = Path(sys.argv[2])
+prefix = sys.argv[3]
+files = [p for p in upload.rglob("*") if p.is_file()]
+manifest = {
+    "upload_prefix": prefix,
+    "run_root": str(run_root),
+    "file_count": len(files),
+    "bytes": sum(p.stat().st_size for p in files),
+    "weights_policy": "Includes proxy outputs, masks, summaries, scripts, and logs. Excludes upstream HY-MT weights, HF caches, API keys, and service tokens.",
+}
+(upload / "manifests" / "manifest.json").write_text(json.dumps(manifest, indent=2) + "\n")
+(upload / "README.md").write_text(
+    "# Issue 42 Percentile Proxy Artifacts\n\n"
+    "This package contains the 100x 1%ile destructive MLP proxy scan, top-20 union mask, "
+    "and teacher-forced proxy substrate-only metrics.\n\n"
+    "Upstream HY-MT model weights are not included.\n"
+)
+PY
+
+hf upload "$SYNTH_HF_REPO" "$UPLOAD_DIR" "$UPLOAD_PREFIX" --repo-type dataset
+hf upload "$CIRCUIT_HF_REPO" "$UPLOAD_DIR" "circuit-shotting/artifacts/percentile_proxy/$UPLOAD_PREFIX" --repo-type dataset
+
+echo "$UPLOAD_PREFIX"
diff --git a/circuit-shotting/artifacts/percentile_proxy/issue42_percentile_proxy_20260518T110628Z/scripts/select_percentile_mlp_mix.py b/circuit-shotting/artifacts/percentile_proxy/issue42_percentile_proxy_20260518T110628Z/scripts/select_percentile_mlp_mix.py
new file mode 100644
index 0000000000000000000000000000000000000000..05ba4d96a8b538017823c327bcf8c6d61c73d9fc
--- /dev/null
+++ b/circuit-shotting/artifacts/percentile_proxy/issue42_percentile_proxy_20260518T110628Z/scripts/select_percentile_mlp_mix.py
@@ -0,0 +1,104 @@
+#!/usr/bin/env python3
+"""Select destructive 1%ile MLP chunks and emit cumulative union masks."""
+
+from __future__ import annotations
+
+import argparse
+import json
+from pathlib import Path
+
+from actdelta_mlp_common import (
+    Candidate,
+    count_mask,
+    load_candidate_jsonl,
+    mask_from_ranges,
+    mlp_total,
+    normalize_ranges,
+    save_mask_npz,
+    write_jsonl,
+)
+
+
+def parse_args() -> argparse.Namespace:
+    p = argparse.ArgumentParser(description=__doc__)
+    p.add_argument("--chunk-results", required=True)
+    p.add_argument("--out-dir", required=True)
+    p.add_argument("--n-layers", type=int, default=32)
+    p.add_argument("--d-ffn", type=int, default=6144)
+    p.add_argument("--top-n", type=int, default=20)
+    return p.parse_args()
+
+
+def main() -> None:
+    args = parse_args()
+    out_dir = Path(args.out_dir)
+    mask_dir = out_dir / "masks"
+    out_dir.mkdir(parents=True, exist_ok=True)
+    mask_dir.mkdir(parents=True, exist_ok=True)
+
+    rows = load_candidate_jsonl(args.chunk_results)
+    ranked = sorted(rows, key=lambda row: float(row["score"]), reverse=True)
+    write_jsonl(out_dir / "ranked_percentiles.jsonl", ranked)
+
+    cumulative_rows = []
+    for top_n in range(1, min(args.top_n, len(ranked)) + 1):
+        selected = ranked[:top_n]
+        ranges = []
+        for row in selected:
+            ranges.extend((int(start), int(end)) for start, end in row["ranges"])
+        ranges = normalize_ranges(ranges)
+        name = f"percentile_top_{top_n}"
+        candidate = Candidate(
+            name=name,
+            ranges=ranges,
+            meta={"top_n": top_n, "chunk_names": [row["name"] for row in selected]},
+        )
+        mask = mask_from_ranges(candidate.ranges, args.n_layers, args.d_ffn)
+        mask_path = mask_dir / f"{name}.full.npz"
+        save_mask_npz(mask_path, mask)
+        cumulative_rows.append({
+            "name": name,
+            "top_n": top_n,
+            "ranges": [list(r) for r in candidate.ranges],
+            "channels": count_mask(mask),
+            "channel_fraction": count_mask(mask) / max(mlp_total(args.n_layers, args.d_ffn), 1),
+            "chunk_names": candidate.meta["chunk_names"],
+            "mask_path": str(mask_path),
+            "selected_chunks": [
+                {
+                    "rank": idx + 1,
+                    "name": row["name"],
+                    "score": row["score"],
+                    "mean_sentence_kl": row["mean_sentence_kl"],
+                    "tail90_sentence_kl": row["tail90_sentence_kl"],
+                    "channels": row["channels"],
+                    "ranges": row["ranges"],
+                }
+                for idx, row in enumerate(selected)
+            ],
+        })
+    write_jsonl(out_dir / "cumulative_percentile_mixes.jsonl", cumulative_rows)
+    final = cumulative_rows[-1] if cumulative_rows else None
+
+    summary = {
+        "n_layers": args.n_layers,
+        "d_ffn": args.d_ffn,
+        "mlp_total": mlp_total(args.n_layers, args.d_ffn),
+        "top_n": args.top_n,
+        "ranked_percentiles_jsonl": str(out_dir / "ranked_percentiles.jsonl"),
+        "cumulative_percentile_mixes_jsonl": str(out_dir / "cumulative_percentile_mixes.jsonl"),
+        "final_mix": final,
+    }
+    (out_dir / "selection_summary.json").write_text(
+        json.dumps(summary, indent=2, ensure_ascii=False) + "\n"
+    )
+    print(json.dumps({
+        "top_n": args.top_n,
+        "final_mix": None if final is None else final["name"],
+        "channels": None if final is None else final["channels"],
+        "out_dir": str(out_dir),
+    }, indent=2), flush=True)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/circuit-shotting/artifacts/percentile_proxy/issue42_percentile_proxy_20260518T110628Z/scripts/summarize_issue42_percentile_proxy.py b/circuit-shotting/artifacts/percentile_proxy/issue42_percentile_proxy_20260518T110628Z/scripts/summarize_issue42_percentile_proxy.py
new file mode 100644
index 0000000000000000000000000000000000000000..16ec6f461aa5d188d62d562489f075678bf2e29d
--- /dev/null
+++ b/circuit-shotting/artifacts/percentile_proxy/issue42_percentile_proxy_20260518T110628Z/scripts/summarize_issue42_percentile_proxy.py
@@ -0,0 +1,96 @@
+#!/usr/bin/env python3
+"""Summarize issue #42 percentile damage and substrate-only proxy results."""
+
+from __future__ import annotations
+
+import argparse
+import json
+from pathlib import Path
+
+from actdelta_mlp_common import load_candidate_jsonl
+
+
+def parse_args() -> argparse.Namespace:
+    p = argparse.ArgumentParser(description=__doc__)
+    p.add_argument("--run-root", required=True)
+    p.add_argument("--out-json", required=True)
+    p.add_argument("--out-md", required=True)
+    return p.parse_args()
+
+
+def maybe_json(path: Path):
+    if path.exists():
+        return json.load(open(path))
+    return None
+
+
+def main() -> None:
+    args = parse_args()
+    root = Path(args.run_root)
+    selection = maybe_json(root / "selection" / "selection_summary.json") or {}
+    proxy = maybe_json(root / "eval" / "top20_proxy.json") or {}
+    ranked_path = root / "selection" / "ranked_percentiles.jsonl"
+    ranked = load_candidate_jsonl(ranked_path) if ranked_path.exists() else []
+    top_ranked = ranked[:20]
+    final_mix = selection.get("final_mix") or {}
+
+    out = {
+        "run_root": str(root),
+        "selection": selection,
+        "proxy": proxy,
+        "top_20_ranked_percentiles": top_ranked,
+    }
+    out_json = Path(args.out_json)
+    out_md = Path(args.out_md)
+    out_json.parent.mkdir(parents=True, exist_ok=True)
+    out_md.parent.mkdir(parents=True, exist_ok=True)
+    out_json.write_text(json.dumps(out, indent=2, ensure_ascii=False) + "\n")
+
+    lines = [
+        "# Issue 42 Percentile Proxy Summary",
+        "",
+    ]
+    if final_mix:
+        lines.extend([
+            f"- Final mix: `{final_mix.get('name')}`",
+            f"- 1%ile chunks: `{final_mix.get('top_n')}`",
+            f"- Channels: `{final_mix.get('channels')}`",
+            f"- Channel fraction: `{final_mix.get('channel_fraction')}`",
+            "",
+        ])
+
+    lines.extend([
+        "## Proxy Metrics",
+        "",
+        "| system | intervention | channels | mean token KL | mean sentence KL | tail90 sentence KL | top1 agree | top5 contains | KL recovery vs all-zero |",
+        "|---|---|---:|---:|---:|---:|---:|---:|---:|",
+    ])
+    for name, row in (proxy.get("results") or {}).items():
+        metrics = row.get("metrics") or {}
+        lines.append(
+            f"| `{name}` | `{row.get('intervention')}` | `{row.get('channels')}` | "
+            f"`{metrics.get('mean_token_kl')}` | `{metrics.get('mean_sentence_kl')}` | "
+            f"`{metrics.get('tail90_sentence_kl')}` | `{metrics.get('teacher_top1_agreement')}` | "
+            f"`{metrics.get('teacher_top5_contains')}` | `{metrics.get('kl_gap_recovery_vs_zero_all')}` |"
+        )
+
+    lines.extend([
+        "",
+        "## Top 20 Destructive 1%iles",
+        "",
+        "| rank | chunk | score | mean sentence KL | tail90 sentence KL | channels |",
+        "|---:|---|---:|---:|---:|---:|",
+    ])
+    for idx, row in enumerate(top_ranked, start=1):
+        lines.append(
+            f"| {idx} | `{row.get('name')}` | `{row.get('score')}` | "
+            f"`{row.get('mean_sentence_kl')}` | `{row.get('tail90_sentence_kl')}` | "
+            f"`{row.get('channels')}` |"
+        )
+
+    out_md.write_text("\n".join(lines) + "\n")
+    print(json.dumps({"out_json": str(out_json), "out_md": str(out_md)}, indent=2), flush=True)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/circuit-shotting/artifacts/percentile_proxy/issue42_percentile_proxy_20260518T110628Z/selection/cumulative_percentile_mixes.jsonl b/circuit-shotting/artifacts/percentile_proxy/issue42_percentile_proxy_20260518T110628Z/selection/cumulative_percentile_mixes.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..d141f6eebbc3d344d6be195789caf5a4e1055efd
--- /dev/null
+++ b/circuit-shotting/artifacts/percentile_proxy/issue42_percentile_proxy_20260518T110628Z/selection/cumulative_percentile_mixes.jsonl
@@ -0,0 +1,20 @@
+{"name": "percentile_top_1", "top_n": 1, "ranges": [[0, 1966]], "channels": 1966, "channel_fraction": 0.009999593098958334, "chunk_names": ["sector_0_chunk_0"], "mask_path": "/root/runs/issue42_percentile_proxy_8b46e31/selection/masks/percentile_top_1.full.npz", "selected_chunks": [{"rank": 1, "name": "sector_0_chunk_0", "score": 0.3821094371378422, "mean_sentence_kl": 0.18972662463784218, "tail90_sentence_kl": 0.384765625, "channels": 1966, "ranges": [[0, 1966]]}]}
+{"name": "percentile_top_2", "top_n": 2, "ranges": [[0, 1966], [1966, 3932]], "channels": 3932, "channel_fraction": 0.019999186197916668, "chunk_names": ["sector_0_chunk_0", "sector_0_chunk_1"], "mask_path": "/root/runs/issue42_percentile_proxy_8b46e31/selection/masks/percentile_top_2.full.npz", "selected_chunks": [{"rank": 1, "name": "sector_0_chunk_0", "score": 0.3821094371378422, "mean_sentence_kl": 0.18972662463784218, "tail90_sentence_kl": 0.384765625, "channels": 1966, "ranges": [[0, 1966]]}, {"rank": 2, "name": "sector_0_chunk_1", "score": 0.36368532478809357, "mean_sentence_kl": 0.17325563728809357, "tail90_sentence_kl": 0.380859375, "channels": 1966, "ranges": [[1966, 3932]]}]}
+{"name": "percentile_top_3", "top_n": 3, "ranges": [[0, 1966], [1966, 3932], [3932, 5898]], "channels": 5898, "channel_fraction": 0.029998779296875, "chunk_names": ["sector_0_chunk_0", "sector_0_chunk_1", "sector_0_chunk_2"], "mask_path": "/root/runs/issue42_percentile_proxy_8b46e31/selection/masks/percentile_top_3.full.npz", "selected_chunks": [{"rank": 1, "name": "sector_0_chunk_0", "score": 0.3821094371378422, "mean_sentence_kl": 0.18972662463784218, "tail90_sentence_kl": 0.384765625, "channels": 1966, "ranges": [[0, 1966]]}, {"rank": 2, "name": "sector_0_chunk_1", "score": 0.36368532478809357, "mean_sentence_kl": 0.17325563728809357, "tail90_sentence_kl": 0.380859375, "channels": 1966, "ranges": [[1966, 3932]]}, {"rank": 3, "name": "sector_0_chunk_2", "score": 0.32993651553988457, "mean_sentence_kl": 0.15220214053988457, "tail90_sentence_kl": 0.35546875, "channels": 1966, "ranges": [[3932, 5898]]}]}
+{"name": "percentile_top_4", "top_n": 4, "ranges": [[0, 1966], [1966, 3932], [3932, 5898], [178913, 180879]], "channels": 7864, "channel_fraction": 0.039998372395833336, "chunk_names": ["sector_0_chunk_0", "sector_0_chunk_1", "sector_0_chunk_2", "sector_0_chunk_91"], "mask_path": "/root/runs/issue42_percentile_proxy_8b46e31/selection/masks/percentile_top_4.full.npz", "selected_chunks": [{"rank": 1, "name": "sector_0_chunk_0", "score": 0.3821094371378422, "mean_sentence_kl": 0.18972662463784218, "tail90_sentence_kl": 0.384765625, "channels": 1966, "ranges": [[0, 1966]]}, {"rank": 2, "name": "sector_0_chunk_1", "score": 0.36368532478809357, "mean_sentence_kl": 0.17325563728809357, "tail90_sentence_kl": 0.380859375, "channels": 1966, "ranges": [[1966, 3932]]}, {"rank": 3, "name": "sector_0_chunk_2", "score": 0.32993651553988457, "mean_sentence_kl": 0.15220214053988457, "tail90_sentence_kl": 0.35546875, "channels": 1966, "ranges": [[3932, 5898]]}, {"rank": 4, "name": "sector_0_chunk_91", "score": 0.19272078573703766, "mean_sentence_kl": 0.08676375448703766, "tail90_sentence_kl": 0.2119140625, "channels": 1966, "ranges": [[178913, 180879]]}]}
+{"name": "percentile_top_5", "top_n": 5, "ranges": [[0, 1966], [1966, 3932], [3932, 5898], [178913, 180879], [7864, 9830]], "channels": 9830, "channel_fraction": 0.049997965494791664, "chunk_names": ["sector_0_chunk_0", "sector_0_chunk_1", "sector_0_chunk_2", "sector_0_chunk_91", "sector_0_chunk_4"], "mask_path": "/root/runs/issue42_percentile_proxy_8b46e31/selection/masks/percentile_top_5.full.npz", "selected_chunks": [{"rank": 1, "name": "sector_0_chunk_0", "score": 0.3821094371378422, "mean_sentence_kl": 0.18972662463784218, "tail90_sentence_kl": 0.384765625, "channels": 1966, "ranges": [[0, 1966]]}, {"rank": 2, "name": "sector_0_chunk_1", "score": 0.36368532478809357, "mean_sentence_kl": 0.17325563728809357, "tail90_sentence_kl": 0.380859375, "channels": 1966, "ranges": [[1966, 3932]]}, {"rank": 3, "name": "sector_0_chunk_2", "score": 0.32993651553988457, "mean_sentence_kl": 0.15220214053988457, "tail90_sentence_kl": 0.35546875, "channels": 1966, "ranges": [[3932, 5898]]}, {"rank": 4, "name": "sector_0_chunk_91", "score": 0.19272078573703766, "mean_sentence_kl": 0.08676375448703766, "tail90_sentence_kl": 0.2119140625, "channels": 1966, "ranges": [[178913, 180879]]}, {"rank": 5, "name": "sector_0_chunk_4", "score": 0.1307773245498538, "mean_sentence_kl": 0.0599765432998538, "tail90_sentence_kl": 0.1416015625, "channels": 1966, "ranges": [[7864, 9830]]}]}
+{"name": "percentile_top_6", "top_n": 6, "ranges": [[0, 1966], [1966, 3932], [3932, 5898], [178913, 180879], [7864, 9830], [5898, 7864]], "channels": 11796, "channel_fraction": 0.05999755859375, "chunk_names": ["sector_0_chunk_0", "sector_0_chunk_1", "sector_0_chunk_2", "sector_0_chunk_91", "sector_0_chunk_4", "sector_0_chunk_3"], "mask_path": "/root/runs/issue42_percentile_proxy_8b46e31/selection/masks/percentile_top_6.full.npz", "selected_chunks": [{"rank": 1, "name": "sector_0_chunk_0", "score": 0.3821094371378422, "mean_sentence_kl": 0.18972662463784218, "tail90_sentence_kl": 0.384765625, "channels": 1966, "ranges": [[0, 1966]]}, {"rank": 2, "name": "sector_0_chunk_1", "score": 0.36368532478809357, "mean_sentence_kl": 0.17325563728809357, "tail90_sentence_kl": 0.380859375, "channels": 1966, "ranges": [[1966, 3932]]}, {"rank": 3, "name": "sector_0_chunk_2", "score": 0.32993651553988457, "mean_sentence_kl": 0.15220214053988457, "tail90_sentence_kl": 0.35546875, "channels": 1966, "ranges": [[3932, 5898]]}, {"rank": 4, "name": "sector_0_chunk_91", "score": 0.19272078573703766, "mean_sentence_kl": 0.08676375448703766, "tail90_sentence_kl": 0.2119140625, "channels": 1966, "ranges": [[178913, 180879]]}, {"rank": 5, "name": "sector_0_chunk_4", "score": 0.1307773245498538, "mean_sentence_kl": 0.0599765432998538, "tail90_sentence_kl": 0.1416015625, "channels": 1966, "ranges": [[7864, 9830]]}, {"rank": 6, "name": "sector_0_chunk_3", "score": 0.12398696690797806, "mean_sentence_kl": 0.05855727940797806, "tail90_sentence_kl": 0.130859375, "channels": 1966, "ranges": [[5898, 7864]]}]}
+{"name": "percentile_top_7", "top_n": 7, "ranges": [[0, 1966], [1966, 3932], [3932, 5898], [178913, 180879], [7864, 9830], [5898, 7864], [45219, 47185]], "channels": 13762, "channel_fraction": 0.06999715169270833, "chunk_names": ["sector_0_chunk_0", "sector_0_chunk_1", "sector_0_chunk_2", "sector_0_chunk_91", "sector_0_chunk_4", "sector_0_chunk_3", "sector_0_chunk_23"], "mask_path": "/root/runs/issue42_percentile_proxy_8b46e31/selection/masks/percentile_top_7.full.npz", "selected_chunks": [{"rank": 1, "name": "sector_0_chunk_0", "score": 0.3821094371378422, "mean_sentence_kl": 0.18972662463784218, "tail90_sentence_kl": 0.384765625, "channels": 1966, "ranges": [[0, 1966]]}, {"rank": 2, "name": "sector_0_chunk_1", "score": 0.36368532478809357, "mean_sentence_kl": 0.17325563728809357, "tail90_sentence_kl": 0.380859375, "channels": 1966, "ranges": [[1966, 3932]]}, {"rank": 3, "name": "sector_0_chunk_2", "score": 0.32993651553988457, "mean_sentence_kl": 0.15220214053988457, "tail90_sentence_kl": 0.35546875, "channels": 1966, "ranges": [[3932, 5898]]}, {"rank": 4, "name": "sector_0_chunk_91", "score": 0.19272078573703766, "mean_sentence_kl": 0.08676375448703766, "tail90_sentence_kl": 0.2119140625, "channels": 1966, "ranges": [[178913, 180879]]}, {"rank": 5, "name": "sector_0_chunk_4", "score": 0.1307773245498538, "mean_sentence_kl": 0.0599765432998538, "tail90_sentence_kl": 0.1416015625, "channels": 1966, "ranges": [[7864, 9830]]}, {"rank": 6, "name": "sector_0_chunk_3", "score": 0.12398696690797806, "mean_sentence_kl": 0.05855727940797806, "tail90_sentence_kl": 0.130859375, "channels": 1966, "ranges": [[5898, 7864]]}, {"rank": 7, "name": "sector_0_chunk_23", "score": 0.1231808140873909, "mean_sentence_kl": 0.0592159703373909, "tail90_sentence_kl": 0.1279296875, "channels": 1966, "ranges": [[45219, 47185]]}]}
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diff --git a/circuit-shotting/artifacts/percentile_proxy/issue42_percentile_proxy_20260518T110628Z/selection/selection_summary.json b/circuit-shotting/artifacts/percentile_proxy/issue42_percentile_proxy_20260518T110628Z/selection/selection_summary.json
new file mode 100644
index 0000000000000000000000000000000000000000..906c013ecb44173fb870ec0fa3249612a13a92cf
--- /dev/null
+++ b/circuit-shotting/artifacts/percentile_proxy/issue42_percentile_proxy_20260518T110628Z/selection/selection_summary.json
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diff --git a/circuit-shotting/artifacts/percentile_proxy/issue42_percentile_proxy_20260518T110628Z/spec/issue42_percentile_proxy.json b/circuit-shotting/artifacts/percentile_proxy/issue42_percentile_proxy_20260518T110628Z/spec/issue42_percentile_proxy.json
new file mode 100644
index 0000000000000000000000000000000000000000..0a0f3d40d1d8835c4dedd4943af20f876189c18c
--- /dev/null
+++ b/circuit-shotting/artifacts/percentile_proxy/issue42_percentile_proxy_20260518T110628Z/spec/issue42_percentile_proxy.json
@@ -0,0 +1,45 @@
+{
+  "issue": 42,
+  "title": "Proxy search: top-20 destructive MLP 1%iles and substrate-only recovery",
+  "model": "tencent/HY-MT1.5-1.8B",
+  "task": "EN->PT translation",
+  "hook": "MLP down_proj input",
+  "eval_type": "teacher-forced proxy only",
+  "judge": "none",
+  "mlp_universe": {
+    "layers": 32,
+    "intermediate_size": 6144,
+    "total_channels": 196608
+  },
+  "percentile_chunks": {
+    "definition": "100 contiguous chunks over flattened (layer, channel) coordinates",
+    "chunks": 100,
+    "selection_budget": 20,
+    "expected_fraction": 0.2
+  },
+  "damage_proxy": {
+    "script": "actdelta_mlp_damage_scan.py",
+    "metric": "mean_sentence_kl + 0.5 * tail90_sentence_kl",
+    "intervention": "zero selected chunk channels from full model"
+  },
+  "substrate_proxy": {
+    "script": "evaluate_mlp_proxy_translation.py",
+    "systems": [
+      "all_mlp_zero",
+      "nuked_percentile_top_20",
+      "native_percentile_top_20_substrate_only"
+    ],
+    "metrics": [
+      "mean_token_kl",
+      "mean_sentence_kl",
+      "tail90_sentence_kl",
+      "teacher_top1_agreement",
+      "teacher_top5_contains",
+      "teacher_argmax_nll",
+      "kl_gap_recovery_vs_zero_all"
+    ]
+  },
+  "runner": "issue42_percentile_proxy_runner.sh",
+  "summary_script": "summarize_issue42_percentile_proxy.py",
+  "artifact_packager": "scripts/package_issue42_hf_upload.sh"
+}
diff --git a/circuit-shotting/artifacts/percentile_proxy/issue42_percentile_proxy_20260518T110628Z/summaries/issue42_summary.json b/circuit-shotting/artifacts/percentile_proxy/issue42_percentile_proxy_20260518T110628Z/summaries/issue42_summary.json
new file mode 100644
index 0000000000000000000000000000000000000000..1abe6159a1d515ac799c3f630cda044dd3d33621
--- /dev/null
+++ b/circuit-shotting/artifacts/percentile_proxy/issue42_percentile_proxy_20260518T110628Z/summaries/issue42_summary.json
@@ -0,0 +1,11295 @@
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+      "n_layers": 32,
+      "hidden_size": 2048,
+      "d_ffn": 6144,
+      "mode": "chunks",
+      "shard_index": 0
+    }
+  ]
+}
diff --git a/circuit-shotting/artifacts/percentile_proxy/issue42_percentile_proxy_20260518T110628Z/summaries/issue42_summary.md b/circuit-shotting/artifacts/percentile_proxy/issue42_percentile_proxy_20260518T110628Z/summaries/issue42_summary.md
new file mode 100644
index 0000000000000000000000000000000000000000..50c2a255e982fe7f037ca1d74fa91551b55d3f53
--- /dev/null
+++ b/circuit-shotting/artifacts/percentile_proxy/issue42_percentile_proxy_20260518T110628Z/summaries/issue42_summary.md
@@ -0,0 +1,39 @@
+# Issue 42 Percentile Proxy Summary
+
+- Final mix: `percentile_top_20`
+- 1%ile chunks: `20`
+- Channels: `39321`
+- Channel fraction: `0.1999969482421875`
+
+## Proxy Metrics
+
+| system | intervention | channels | mean token KL | mean sentence KL | tail90 sentence KL | top1 agree | top5 contains | KL recovery vs all-zero |
+|---|---|---:|---:|---:|---:|---:|---:|---:|
+| `all_mlp_zero` | `keep-only` | `0` | `12.821661838961273` | `12.828857421875` | `13.3125` | `0.00018033452053559354` | `0.00040575267120508544` | `0.0` |
+| `nuked_percentile_top_20` | `nuke-zero` | `39321` | `13.415645710292592` | `13.41259765625` | `14.0` | `0.0` | `9.016726026779677e-05` | `-0.046326590015529634` |
+| `native_percentile_top_20_substrate_only` | `keep-only` | `39321` | `10.892991802528064` | `10.8990478515625` | `11.5` | `0.001172174383481358` | `0.0051846174653983135` | `0.15042278143481724` |
+
+## Top 20 Destructive 1%iles
+
+| rank | chunk | score | mean sentence KL | tail90 sentence KL | channels |
+|---:|---|---:|---:|---:|---:|
+| 1 | `sector_0_chunk_0` | `0.3821094371378422` | `0.18972662463784218` | `0.384765625` | `1966` |
+| 2 | `sector_0_chunk_1` | `0.36368532478809357` | `0.17325563728809357` | `0.380859375` | `1966` |
+| 3 | `sector_0_chunk_2` | `0.32993651553988457` | `0.15220214053988457` | `0.35546875` | `1966` |
+| 4 | `sector_0_chunk_91` | `0.19272078573703766` | `0.08676375448703766` | `0.2119140625` | `1966` |
+| 5 | `sector_0_chunk_4` | `0.1307773245498538` | `0.0599765432998538` | `0.1416015625` | `1966` |
+| 6 | `sector_0_chunk_3` | `0.12398696690797806` | `0.05855727940797806` | `0.130859375` | `1966` |
+| 7 | `sector_0_chunk_23` | `0.1231808140873909` | `0.0592159703373909` | `0.1279296875` | `1966` |
+| 8 | `sector_0_chunk_5` | `0.11249938979744911` | `0.05122009292244911` | `0.12255859375` | `1966` |
+| 9 | `sector_0_chunk_20` | `0.10601149499416351` | `0.05474196374416351` | `0.1025390625` | `1966` |
+| 10 | `sector_0_chunk_65` | `0.0985150970518589` | `0.0460248626768589` | `0.10498046875` | `1966` |
+| 11 | `sector_0_chunk_56` | `0.09739024937152863` | `0.049538686871528625` | `0.095703125` | `1966` |
+| 12 | `sector_0_chunk_7` | `0.09699758514761925` | `0.04548391327261925` | `0.10302734375` | `1966` |
+| 13 | `sector_0_chunk_58` | `0.09512045234441757` | `0.04800131171941757` | `0.09423828125` | `1966` |
+| 14 | `sector_0_chunk_6` | `0.09350507520139217` | `0.041991403326392174` | `0.10302734375` | `1966` |
+| 15 | `sector_0_chunk_62` | `0.08989111706614494` | `0.04472510144114494` | `0.09033203125` | `1967` |
+| 16 | `sector_0_chunk_9` | `0.08937827311456203` | `0.041526710614562035` | `0.095703125` | `1966` |
+| 17 | `sector_0_chunk_57` | `0.08864056691527367` | `0.044695254415273666` | `0.087890625` | `1966` |
+| 18 | `sector_0_chunk_59` | `0.08843556046485901` | `0.04473438858985901` | `0.08740234375` | `1966` |
+| 19 | `sector_0_chunk_18` | `0.08735546190291643` | `0.04170116502791643` | `0.09130859375` | `1966` |
+| 20 | `sector_0_chunk_60` | `0.08616224676370621` | `0.04392591863870621` | `0.08447265625` | `1966` |