Tooling: 4.80× faster DisjointTensor.frame_data with identical frame receipts

certificates/disjoint-tensor-compression.json

@@ -342,7 +342,7 @@
     "scripts/binary_phase_frames.py": "4148cfa313cf050ed25296e8db36d35871520b8318fd086c7d1facf0c6af67d7",
     "scripts/complex_compression.py": "9b6f758a5985ba5d9fdd726fe6556b3d3b4bd9d98aaf8ba06a7bb58093c4c504",
     "scripts/exclusion_circuit.py": "f9fbcd6e85e442cbd0926ac0887414e20735b2dc923d2d5822c803ceb46726d3",
-    "scripts/experiments/disjoint_tensor.py": "085600fc922ae167b5981505e1a5caba4d1c1df15482f61d83d2c086fa89eac1"
+    "scripts/experiments/disjoint_tensor.py": "23763a4b6086fb3d19f9282fff5c118f53fa52e7a7f17da1bde990bd4cf8b6ac"
   },
   "status": "IMPROVED FINITE COMPLEX NETWORK; NO NEW MULTIPLICATION KAPPA",
   "upstream_commit": "adc7f1241b42e322a6451854ab7e4b4c146bf78a"

certificates/dyadic-central-factor.json

@@ -103,7 +103,7 @@
     "scripts/complex_compression.py": "9b6f758a5985ba5d9fdd726fe6556b3d3b4bd9d98aaf8ba06a7bb58093c4c504",
     "scripts/exclusion_circuit.py": "f9fbcd6e85e442cbd0926ac0887414e20735b2dc923d2d5822c803ceb46726d3",
     "scripts/experiments/complex_centers.py": "3744396587d729253d80980f6310b56e7bf0ea21b165bd178d0d6d4c49faf2e2",
-    "scripts/experiments/disjoint_tensor.py": "085600fc922ae167b5981505e1a5caba4d1c1df15482f61d83d2c086fa89eac1",
+    "scripts/experiments/disjoint_tensor.py": "23763a4b6086fb3d19f9282fff5c118f53fa52e7a7f17da1bde990bd4cf8b6ac",
     "scripts/general_network_guard.py": "790a2064449afdbffd6dbd4adbcf4a28eaf41931ce7134e4a4e8766f3764270c"
   },
   "status": "COMPLEX INTERFACE HEADROOM; RETAINED KAPPA 2^-31",

certificates/parity-side.json

@@ -120,7 +120,7 @@
     "docs/research/parity-side.md": "75de20f8dcd3d1a2e2d19bc087eda50a6cc77027651f48198868426a80cd12f2",
     "scripts/audit_parity_side.py": "7cbdd405ce11fa6f7ffcbef3811174f8685ad4640e9330176601d3b3d3e95390",
     "scripts/binary_phase_frames.py": "4148cfa313cf050ed25296e8db36d35871520b8318fd086c7d1facf0c6af67d7",
-    "scripts/experiments/disjoint_tensor.py": "085600fc922ae167b5981505e1a5caba4d1c1df15482f61d83d2c086fa89eac1",
+    "scripts/experiments/disjoint_tensor.py": "23763a4b6086fb3d19f9282fff5c118f53fa52e7a7f17da1bde990bd4cf8b6ac",
     "scripts/experiments/mixed_point_circuit.py": "f509362b5031976a22af351fd158ae0b4520fa4f286a5176ec37023d672a7ddc",
     "scripts/experiments/parity_phase_repair.py": "1f76f3b96ed556ed901835bd101d0e2588c997d2ed39bdfca097f0187aa86d40",
     "scripts/experiments/parity_side.py": "1363ebf82d4c39e458e7bede8253688271008374db70ed4284df84253c8d092b"

certificates/ternary-side.json

@@ -1564,7 +1564,7 @@
     "scripts/audit_ternary_side.py": "5e86566eee32031e1829d26ea17a7372e4802e2f7d76a2caa3ae16de3ab072c1",
     "scripts/certify.py": "2348d95008276c5d5764f7e028c3918e484582cd589bd4d9b82919161745e53e",
     "scripts/exclusion_circuit.py": "f9fbcd6e85e442cbd0926ac0887414e20735b2dc923d2d5822c803ceb46726d3",
-    "scripts/experiments/disjoint_tensor.py": "085600fc922ae167b5981505e1a5caba4d1c1df15482f61d83d2c086fa89eac1",
+    "scripts/experiments/disjoint_tensor.py": "23763a4b6086fb3d19f9282fff5c118f53fa52e7a7f17da1bde990bd4cf8b6ac",
     "scripts/experiments/prime_core.py": "080a89a2c91505b509068698542843c9d7af3cdec01b5c88972107dc9c688c62",
     "scripts/experiments/rank_product_core.py": "7a91c1188c5d42e864a5d2e42397b09c435b526ae4f4210c44bf615de488708e",
     "scripts/experiments/shared_core.py": "d3c59b61229956cb54ae1ef9145ed213abdf1ae743929de145a77c43656f01a8",

research/disjoint-tensor-frame-unions/NOTICE.md

@@ -0,0 +1,13 @@
+# Notice — disjoint-tensor frame unions
+
+Douglas R. Colkitt authored the disjoint-tensor compression screen, the
+`DisjointTensor` recurrence, embedding and original `frame_data` /
+`verify_frames` checkers consumed by this tooling package.
+
+Stephan Arrington’s `research/disjoint-frame-unions/` (#1044) proved the exact
+target-union propagation algebra as an opt-in adapter with identical
+historical-certificate bytes. This package promotes that algebra into the
+default production `DisjointTensor.frame_data` path, with identical
+`verify_frames` receipts and updated source pins only.
+
+Prepared by Wentao Li with substantial AI assistance, under Apache-2.0.

research/disjoint-tensor-frame-unions/README.md

@@ -0,0 +1,112 @@
+# Faster DisjointTensor.frame_data with identical frame receipts
+
+**Tooling improvement; no new kappa.** At base
+`0fdd7d82de03e43c9e5a1000dfbcd380d1b687d2`, merged
+[CrocSwap/integer-mult-bounds#2001](https://github.com/CrocSwap/integer-mult-bounds/pull/2001)
+left `DisjointTensor.verify_frames` / `frame_data` as an explicit next question
+after shipping closed-form verify support masks. Profiling on the historical
+full-size circuit (`h=26`, 60372 active nodes) shows about 1.8 of 2.2 seconds
+of `verify_frames` inside the per-node reachable-target bit enumeration in
+`frame_data`.
+
+[CrocSwap/integer-mult-bounds#1044](https://github.com/CrocSwap/integer-mult-bounds/pull/1044)
+already proved exact target-union propagation as an **opt-in** adapter under
+`research/disjoint-frame-unions/`. Production `scripts/experiments/disjoint_tensor.py`
+still used the historical bit loop. This change promotes the same algebra into
+the default path, keeps every descendant bitset, source union and rectangle
+`require`, and leaves `verify_frames` receipts fieldwise identical. Only the
+source pin for `scripts/experiments/disjoint_tensor.py` changes inside the four
+certificates that bind that file.
+
+## Reproduce
+
+Python **3.12**, standard library only, from the repository root:
+
+```sh
+python3.12 -I -B research/disjoint-tensor-frame-unions/verify.py
+python3.12 -I -O -B research/disjoint-tensor-frame-unions/verify.py
+python3.12 -B scripts/research_package.py --package research/disjoint-tensor-frame-unions
+python3.12 -I -B research/disjoint-tensor-frame-unions/benchmark.py --rounds 5
+```
+
+Expected:
+
+```text
+PASS_DISJOINT_TENSOR_FRAME_UNIONS frame_cases=6 map_cases=6 certs=4 no_new_kappa
+PASS_REGISTERED_RESEARCH_PACKAGE research/disjoint-tensor-frame-unions outcome=completed
+PASS_IDENTICAL_DISJOINT_TENSOR_FRAME_UNION_RECEIPTS
+```
+
+Default verify is read-only. Full `make verify`, selected cold replay, Lean, and
+multiplication-theorem obligations were not rerun for this isolated
+historical-checker change.
+
+## Result
+
+On Linux / CPython 3.12.3 (Linux-6.12.94+-x86_64-with-glibc2.39), five
+alternating fresh-process pairs timing only `DisjointTensor(26).verify_frames`
+(construction excluded) gave median wall time **2.232 → 0.465 seconds (4.80×)**.
+Timing is host-descriptive and is not a correctness gate; all samples and
+environment pins are in [RESULT.json](RESULT.json). Every frame receipt matches
+the baseline checker fieldwise (`distinct_nontrivial_transitions`,
+`frame_types`, flags).
+
+## Equivalence
+
+For a node \(v\) let \(D(v)\) be its set of reachable output indices. If output
+\(i\) has ground-coordinate mask \(T_i\), the historical loop computes
+
+```text
+U(v) = OR_{i in D(v)} T_i
+```
+
+by walking bits of \(D(v)\). Define \(F(A)=\mathrm{OR}_{i\in A} T_i\). Then
+\(F(\emptyset)=0\) and \(F(A\cup B)=F(A)\lor F(B)\). Initializing \(U\) at the
+same output nodes as \(D\) and propagating \(U\) alongside \(D\) in the identical
+reverse topological order yields \(U(v)=F(D(v))\) at every node. An XOR-of-mask
+mutant cancels overlapping paths and is rejected by the package controls.
+Rectangle checks still use `require`, so admission stays enforced under `-O`.
+
+## Open next questions
+
+1. **Question:** Should `DisjointTensor.verify`'s `dag_sha256` `json.dumps` path
+   get a hand-rolled formatter like the mixed-point / SharedPoint digests?
+   **Why it matters:** After support masks and frame_data, `json.dumps` remains a
+   secondary share of verify.
+   **First step:** Profile the post-mask verify residual and compare a
+   hand-rolled `[[i,args,str(support)]]` encoder against the committed digest.
+   **Blocked by:** Out of this session’s stop rule.
+
+2. **Question:** Can `research/disjoint-frame-unions` drop its opt-in monkeypatch
+   now that production `frame_data` matches the accelerator?
+   **Why it matters:** The adapter is redundant on main, but its
+   byte-for-byte historical certificate claim is a separate provenance object.
+   **First step:** Diff accelerator vs production and decide whether that
+   package becomes a pure historical replay fixture.
+   **Blocked by:** Do not reopen #1044’s certificate fixture in this PR.
+
+## Overlap and credit
+
+Closest priors (different objects or delivery):
+[CrocSwap/integer-mult-bounds#756](https://github.com/CrocSwap/integer-mult-bounds/pull/756),
+[CrocSwap/integer-mult-bounds#1044](https://github.com/CrocSwap/integer-mult-bounds/pull/1044)
+(opt-in exact union propagation; this package promotes it to production),
+[CrocSwap/integer-mult-bounds#1793](https://github.com/CrocSwap/integer-mult-bounds/pull/1793),
+[CrocSwap/integer-mult-bounds#1861](https://github.com/CrocSwap/integer-mult-bounds/pull/1861),
+[CrocSwap/integer-mult-bounds#1922](https://github.com/CrocSwap/integer-mult-bounds/pull/1922),
+[CrocSwap/integer-mult-bounds#1984](https://github.com/CrocSwap/integer-mult-bounds/pull/1984),
+[CrocSwap/integer-mult-bounds#1994](https://github.com/CrocSwap/integer-mult-bounds/pull/1994),
+[CrocSwap/integer-mult-bounds#2001](https://github.com/CrocSwap/integer-mult-bounds/pull/2001)
+(support masks + open `verify_frames` / `frame_data` question this answers).
+Open [CrocSwap/integer-mult-bounds#2100](https://github.com/CrocSwap/integer-mult-bounds/pull/2100)
+updates `ternary_side.py` inside `certificates/ternary-side.json`; this PR only
+rewrites the `disjoint_tensor.py` pin in that shared JSON.
+Open [CrocSwap/integer-mult-bounds#1861](https://github.com/CrocSwap/integer-mult-bounds/pull/1861)
+updates `binary_phase_frames.py` inside `certificates/parity-side.json`; shared
+JSON should carry both pins on merge.
+
+Douglas R. Colkitt authored the disjoint-tensor checkers. Stephan Arrington’s
+#1044 supplied the opt-in union algebra. Prepared by Wentao Li
+([VictorLiwentao](https://github.com/VictorLiwentao),
+[@Is__Victor](https://x.com/Is__Victor)) with substantial AI assistance, under
+Apache-2.0.

research/disjoint-tensor-frame-unions/RESULT.json

@@ -0,0 +1,50 @@
+{
+  "environment": {
+    "executable": "/usr/bin/python3.12",
+    "platform": "Linux-6.12.94+-x86_64-with-glibc2.39",
+    "python": "3.12.3 (main, Aug 31 2026, 10:18:26) [GCC 13.3.0]"
+  },
+  "fixture": "DisjointTensor(26).verify_frames only (construction excluded)",
+  "median_baseline_seconds": 2.231777176000037,
+  "median_optimized_seconds": 0.4646116810000649,
+  "median_ratio": 4.8035322125267985,
+  "no_new_kappa": true,
+  "rounds": 5,
+  "samples": [
+    {
+      "baseline_seconds": 2.132552615999998,
+      "optimized_seconds": 0.4646116810000649,
+      "ratio": 4.589967715425778,
+      "round": 0
+    },
+    {
+      "baseline_seconds": 2.1409860709999293,
+      "optimized_seconds": 0.44430533499996727,
+      "ratio": 4.818726903200671,
+      "round": 1
+    },
+    {
+      "baseline_seconds": 2.231777176000037,
+      "optimized_seconds": 0.44960231200002454,
+      "ratio": 4.9638916803433055,
+      "round": 2
+    },
+    {
+      "baseline_seconds": 2.450227195000025,
+      "optimized_seconds": 0.6052943949999872,
+      "ratio": 4.047992539233866,
+      "round": 3
+    },
+    {
+      "baseline_seconds": 2.325545840000018,
+      "optimized_seconds": 0.49189761000002363,
+      "ratio": 4.727703068124089,
+      "round": 4
+    }
+  ],
+  "source_sha256": {
+    "baseline": "085600fc922ae167b5981505e1a5caba4d1c1df15482f61d83d2c086fa89eac1",
+    "production": "23763a4b6086fb3d19f9282fff5c118f53fa52e7a7f17da1bde990bd4cf8b6ac"
+  },
+  "status": "PASS_IDENTICAL_DISJOINT_TENSOR_FRAME_UNION_RECEIPTS"
+}

research/disjoint-tensor-frame-unions/SOURCES.json

@@ -0,0 +1,84 @@
+{
+  "schema_version": 1,
+  "base_commit": "0fdd7d82de03e43c9e5a1000dfbcd380d1b687d2",
+  "files": [
+    {
+      "path": "docs/research/agent-task-catalog.json",
+      "usage": "provenance",
+      "git_blob": "e8870e5917057d59b730eb14721a778639eca050",
+      "sha256": "d46eb6d5b3e279428753d770653663fbdf1063e55c255ea073d768f344927ef7"
+    },
+    {
+      "path": "certificates/selected-result.json",
+      "usage": "provenance",
+      "git_blob": "ae6a99f28aa468649eb66a361e764d769363bf11",
+      "sha256": "10b9e340fd8e354a37a0c4813c90e5de1dd42044b5f6aca4a3fc5a42b685fa32"
+    },
+    {
+      "path": "certificates/disjoint-tensor-compression.json",
+      "usage": "provenance",
+      "git_blob": "a24b0c9af473414fa87efa5a8d2b643d93e6fd01",
+      "sha256": "899950c322069ebf5a561a321c987906f8c11a592ca229143840901e4e370dd6"
+    },
+    {
+      "path": "certificates/ternary-side.json",
+      "usage": "provenance",
+      "git_blob": "b2c1b793e7cb7e4d358b66bfd021d01c85371e19",
+      "sha256": "2ec667c52babb2a25820e8305538ef16399ab2ea12441d2e8130cc8e3fa13614"
+    },
+    {
+      "path": "certificates/dyadic-central-factor.json",
+      "usage": "provenance",
+      "git_blob": "5bd4e583cd61b0e5183d74799e886afb57690960",
+      "sha256": "3f46000e645c338a583e0f2ab669a7c681980743d03335548eeb838072f1d458"
+    },
+    {
+      "path": "certificates/parity-side.json",
+      "usage": "provenance",
+      "git_blob": "bcc1a78412d297424db1727f49636598206b9392",
+      "sha256": "44ab9d7b781a28228498aa0e73229c700eac4a3a3cf93ffe8f82f52f8c52226c"
+    },
+    {
+      "path": "scripts/experiments/disjoint_tensor.py",
+      "usage": "provenance",
+      "git_blob": "b842d0a8d4f0f782a1f0bef171f5f2cd96878e58",
+      "sha256": "085600fc922ae167b5981505e1a5caba4d1c1df15482f61d83d2c086fa89eac1"
+    },
+    {
+      "path": "scripts/audit_disjoint_tensor.py",
+      "usage": "provenance",
+      "git_blob": "08cb8184abcb7f81434395871152e4902df8cf84",
+      "sha256": "477db540e972b82751fac6a3a6d6e38fb4141e967261b1f9c260e2c686479a78"
+    },
+    {
+      "path": "scripts/exclusion_circuit.py",
+      "usage": "provenance",
+      "git_blob": "b0e2f3e03d7de791ca936dfd92cee2134c7dd4f5",
+      "sha256": "f9fbcd6e85e442cbd0926ac0887414e20735b2dc923d2d5822c803ceb46726d3"
+    },
+    {
+      "path": "scripts/binary_phase_frames.py",
+      "usage": "provenance",
+      "git_blob": "c8e4fa71fe60550324f9e43c1dd5ad6d9bf4e2f4",
+      "sha256": "4148cfa313cf050ed25296e8db36d35871520b8318fd086c7d1facf0c6af67d7"
+    },
+    {
+      "path": "scripts/certify.py",
+      "usage": "provenance",
+      "git_blob": "a773311b726fd1ef98a056f6f76010e872d7908a",
+      "sha256": "2348d95008276c5d5764f7e028c3918e484582cd589bd4d9b82919161745e53e"
+    },
+    {
+      "path": "research/disjoint-frame-unions/accelerator.py",
+      "usage": "provenance",
+      "git_blob": "e4bdd7e3aeb11b86e627b1227249188e4a1157ef",
+      "sha256": "4b90cdf10e864d6a477c43ded3b3025c38cdf82f6c819b0a055795c31ddd9a02"
+    },
+    {
+      "path": "research/disjoint-tensor-support-masks/TASK.json",
+      "usage": "provenance",
+      "git_blob": "4c6d696123cbc21e08595d0b93ef4c97260d4cae",
+      "sha256": "5fe0c79417e7ac42b903db01774f788affff6f2f4b8f58dcbc55d1cda454665f"
+    }
+  ]
+}

research/disjoint-tensor-frame-unions/TASK.json

@@ -0,0 +1,70 @@
+{
+  "schema_version": 1,
+  "task_id": "replay-runtime",
+  "priority_revision": 3,
+  "base_commit": "0fdd7d82de03e43c9e5a1000dfbcd380d1b687d2",
+  "chooser_seed": "a4f19c82e6b0357d9148e2c0b5d67a31",
+  "original_card": {
+    "id": "replay-runtime",
+    "question": "Can one measured replay bottleneck be sped up without dropping coverage?",
+    "starting_points": [
+      "scripts/run_isolated_tests.py",
+      "scripts/verify_replay_coverage.py",
+      "certificates/selected-result.json"
+    ],
+    "methods": [
+      "Profile one isolated replay and optimize the measured bottleneck.",
+      "Design source-bound reuse with explicit dependency invalidation and negative controls."
+    ],
+    "acceptance_gate": "Report before/after wall time on the same fixture, retained checks and invalidation behavior.",
+    "first_step": {
+      "command": [
+        "python3",
+        "-B",
+        "scripts/research_baseline.py"
+      ],
+      "expected": "PASS_RESEARCH_BASELINE_BINDINGS_ONLY",
+      "inspect": "Read the selected reproduction command and choose a single replay stage for profiling.",
+      "scope": "Positive control / source-bound orientation only; this is not a new construction or full cold replay."
+    },
+    "first_experiment": "Measure one unchanged fixture, preserve check coverage, and test one optimization with invalidation controls.",
+    "bottleneck_link": "Faster trustworthy replays increase review throughput and let mechanism candidates be checked sooner.",
+    "allowed_variations": [
+      "Profile one stage or design one source-bound cache boundary."
+    ],
+    "scope_change_triggers": [
+      "Skipping coverage or comparing different fixtures to claim speedup."
+    ],
+    "overlap_terms": [
+      "replay runtime",
+      "cache invalidation",
+      "isolated tests"
+    ]
+  },
+  "scope_status": "refined",
+  "actual_scope": "Can scripts/experiments/disjoint_tensor.py:DisjointTensor.frame_data replace its per-node reachable-target bit enumeration with equivalent exact target-union propagation (as proved opt-in in research/disjoint-frame-unions / #1044) so verify_frames receipts, descendant bitsets, source unions and rectangle require() checks stay fieldwise identical on the historical h=26 circuit?",
+  "scope_change_reason": "The catalogue card asks for one measured replay bottleneck with before/after wall time and retained checks. Expertise override selected verifier-engineering after merged #2001 left DisjointTensor.verify_frames / frame_data as an explicit open question; profiling named the historical bit-enumeration loop in frame_data (~1.8s of ~2.2s verify_frames on h=26) as the next unclaimed hot path. The #1044 opt-in adapter is not the default production path.",
+  "bottleneck_link": "Side result on review throughput: disjoint-tensor-compression, ternary-side, dyadic-central-factor and parity-side certificates call DisjointTensor.verify_frames. A faster identical frame_data does not change kappa or the binding complex supplier.",
+  "budget_minutes": 60,
+  "stop_rule": "Ship an equivalence-preserving production frame_data change with frozen baseline, identical verify_frames receipts on the historical h=26 circuit, updated source pins only on the four binding certificates, registered replay and a measured before/after median. Do not invent kappa, do not touch ternary_side.py / audit_joint_frames.py, and do not run the selected cold frontier replay.",
+  "outcome": "completed",
+  "evidence": "PASS_DISJOINT_TENSOR_FRAME_UNIONS via research/disjoint-tensor-frame-unions/verify.py (also under -O); PASS_IDENTICAL_DISJOINT_TENSOR_FRAME_UNION_RECEIPTS via benchmark.py; four certificates differ from base only in the scripts/experiments/disjoint_tensor.py source pin.",
+  "author": "Wentao Li",
+  "github": "VictorLiwentao",
+  "email": "wentaol4@illinois.edu",
+  "ai_assistance": "Substantial AI assistance for orientation, overlap search, profiling, implementation, packaging, benchmarking and writing. Not independent human peer review.",
+  "open_questions": [
+    {
+      "question": "Should DisjointTensor.verify's dag_sha256 json.dumps path get a hand-rolled formatter like the mixed-point / SharedPoint digests?",
+      "why_it_matters": "After support masks and frame_data, json.dumps remains a secondary share of DisjointTensor.verify (~0.4s on h=26); a byte-identical encoder would be a different object.",
+      "first_step": "Profile the post-mask verify residual and compare a hand-rolled [[i,args,str(support)]] encoder against the committed dag_sha256.",
+      "blocked_by": "Out of this session's stop rule; frame_data / verify_frames was the measured bottleneck."
+    },
+    {
+      "question": "Can research/disjoint-frame-unions drop its opt-in monkeypatch now that production frame_data matches the accelerator?",
+      "why_it_matters": "The historical complete-audit package still swaps DisjointTensor.frame_data at runtime; that adapter is redundant once main carries the same algebra, but its certificate byte-for-byte claim is a separate provenance object.",
+      "first_step": "Diff accelerator.frame_data against production and decide whether the opt-in package becomes a pure historical replay fixture.",
+      "blocked_by": "Out of this session's stop rule; do not reopen #1044's certificate fixture."
+    }
+  ]
+}