fix: regenerate PIST predictions pipeline, fix Matrices250 kernel blowup

- generate_predictions.py: reads 278 equations from classifier receipt,
  builds 8×8 strand adjacency matrices, deduplicates to 250 by equation_id
- build_pist_matrices_250.py: emit each matrix as a separate Lean def
  (match-based findMatrix) to avoid kernel term-size blowup
- rrc_pist_predictions_250_v1.json regenerated in Research Stack shared-data/
- Shakeout finding: text-derived matrices have spectral radius < 2.0,
  so classifyExact returns none (expected — 250 non-zero matrices, weak features)

python/build_pist_matrices_250.py

@@ -7,11 +7,9 @@
 Build formal/SilverSight/PIST/Matrices250.lean from
 shared-data/rrc_pist_predictions_250_v1.json (or a local copy).
 
-Python's role: read the predictions artifact, serialize each 8×8 matrix as a
+Each 8×8 matrix is emitted as its own named `def` to avoid overwhelming
-Lean Array (Array Int) literal, emit an association list keyed by rrc_eq_<hex>.
+Lean's kernel with a single large term. `findMatrix` is a `match` on the
-
+equation_id string that delegates to the individual def.
-The generated Lean file is consumed by SilverSight.PIST.Classify
-(classifyProxy/classifyExact) to produce pistProxyLabel/pistExactLabel.
 
 Usage:
     python3 python/build_pist_matrices_250.py
@@ -20,23 +18,25 @@ Usage:
         --out-lean formal/SilverSight/PIST/Matrices250.lean
 """
 from __future__ import annotations
-import argparse, hashlib, json, sys
+import argparse, hashlib, json, re, sys
 from pathlib import Path
 
 
-def lean_str(s: str) -> str:
+def lean_matrix_def_name(eid: str) -> str:
-    s = s.replace("\\", "\\\\").replace('"', '\\"')
+    """Sanitize equation_id to a valid Lean identifier."""
-    return f'"{s}"'
+    name = re.sub(r"[^a-zA-Z0-9_]", "_", eid)
+    if name[0].isdigit():
+        name = "m_" + name
+    return name
 
 
 def lean_array_int(xs: list[int]) -> str:
-    inner = ", ".join(str(x) for x in xs)
+    return "#[" + ", ".join(str(x) for x in xs) + "]"
-    return f"#[{inner}]"
 
 
 def lean_matrix_rows(rows: list[list[int]]) -> str:
     inner = ",\n    ".join(lean_array_int(r) for r in rows)
-    return f"#[\n    {inner}\n  ]"
+    return "#[\n    " + inner + "\n  ]"
 
 
 def main() -> int:
@@ -61,24 +61,23 @@ def main() -> int:
     raw = preds.get("predictions", [])
     print(f"Loaded {len(raw)} predictions from {args.predictions}", file=sys.stderr)
 
-    # Deterministic order by equation_id for reproducible builds.
     raw_sorted = sorted(raw, key=lambda p: p.get("equation_id", ""))
 
-    entries: list[str] = []
+    defs: list[str] = []
+    find_cases: list[str] = []
     for p in raw_sorted:
         eid = p.get("equation_id", "")
         mat = p.get("matrix_8x8", [])
         if not eid or len(mat) != 8:
             print(f"WARNING: skipping malformed entry {eid}", file=sys.stderr)
             continue
-        matrix_lean = lean_matrix_rows(mat)
+        name = lean_matrix_def_name(eid)
-        entries.append(f"    ({lean_str(eid)}, {matrix_lean})")
+        defs.append(f"def {name} : Array (Array Int) :=\n  {lean_matrix_rows(mat)}")
+        find_cases.append(f"  | \"{eid}\" => some {name}")
 
-    # Include a content hash so the generated file can be validated.
     content_blob = json.dumps(
         {p.get("equation_id"): p.get("matrix_8x8") for p in raw_sorted},
-        sort_keys=True,
+        sort_keys=True, separators=(",", ":"),
-        separators=(",", ":"),
     ).encode("utf-8")
     content_hash = hashlib.sha256(content_blob).hexdigest()
 
@@ -90,26 +89,20 @@ def main() -> int:
         "-- Source: rrc_pist_predictions_250_v1.json",
         "-- Content hash (SHA-256 canonical matrices): " + content_hash,
         "--",
-        "-- This file is consumed by SilverSight.PIST.Classify (classifyProxy/",
+        "-- This file is consumed by SilverSight.RRC.Q16_16Manifold at compile time",
-        "-- classifyExact) at compile time to produce pistProxyLabel/",
+        "-- to produce pistProxyLabel/pistExactLabel for each equation_id.",
-        "-- pistExactLabel for each invariant equation_id.",
         "",
         "namespace SilverSight.PIST.Matrices250",
         "",
-        "/-- All matrices in this list have dimension 8×8 (the canonical strand count).",
-        "    Stored as association list (key → matrix). Generated from",
-        "    rrc_pist_predictions_250_v1.json.",
-        f"    Entries: {len(entries)}. -/",
         f"def pistMatrixDim : Nat := 8",
         "",
-        "def pistMatrices250 : List (String × Array (Array Int)) :=",
+        *defs,
-        "  [",
-        ",\n".join(entries),
-        "  ]",
         "",
         "/-- Look up an 8×8 matrix by invariant equation_id. -/",
         "def findMatrix (eqId : String) : Option (Array (Array Int)) :=",
-        "  pistMatrices250.find? (fun (k, _) => k = eqId) |>.map (fun (_, v) => v)",
+        "  match eqId with",
+        *find_cases,
+        "  | _ => none",
         "",
         "end SilverSight.PIST.Matrices250",
         "",
@@ -117,7 +110,7 @@ def main() -> int:
 
     args.out_lean.parent.mkdir(parents=True, exist_ok=True)
     args.out_lean.write_text("\n".join(lines))
-    print(f"Wrote {args.out_lean} ({len(entries)} entries)", file=sys.stderr)
+    print(f"Wrote {args.out_lean} ({len(defs)} entries)", file=sys.stderr)
     return 0
 
 

python/generate_predictions.py

@@ -0,0 +1,128 @@
+#!/usr/bin/env python3
+"""Regenerate rrc_pist_predictions_250_v1.json from the classifier receipt.
+
+Reads all 278 compiled equations, builds 8×8 strand adjacency matrices,
+deduplicates by equation_id per the AGENTS.md merge rules, and writes
+the predictions artifact.
+
+Usage:
+    python3 python/generate_predictions.py
+    python3 python/generate_predictions.py \
+        --receipt /path/to/rrc_equation_classifier_receipt.json \
+        --output /path/to/rrc_pist_predictions_250_v1.json
+"""
+
+from __future__ import annotations
+
+import argparse
+import hashlib
+import json
+import re
+import sys
+from pathlib import Path
+from datetime import datetime, timezone
+from collections import OrderedDict
+
+
+def tokenize(text: str) -> list[str]:
+    text = text.lower()
+    return re.findall(r"[a-z0-9]+|[+\-*/=^(){}\[\]]", text)
+
+
+def build_matrix(tokens: list[str]) -> list[list[int]]:
+    vocab = sorted(set(tokens))
+    matrix = [[0 for _ in range(8)] for _ in range(8)]
+    for t_i, t_j in zip(tokens, tokens[1:]):
+        matrix[vocab.index(t_i) % 8][vocab.index(t_j) % 8] += 1
+    return matrix
+
+
+def canonical_json_matrix(matrix: list[list[int]]) -> str:
+    return json.dumps(matrix, separators=(",", ":"))
+
+
+def build_record(text: str, equation_id: str, name: str) -> dict:
+    tokens = tokenize(text)
+    vocab = sorted(set(tokens))
+    matrix = build_matrix(tokens)
+    matrix_json = canonical_json_matrix(matrix)
+    return {
+        "equation_id": equation_id,
+        "name": name,
+        "equation": text,
+        "schema": "rrc_pist_predictions_250_v1",
+        "claim_boundary": "matrix-only;no-classifier;no-lean-spectral",
+        "proxy_pred": None,
+        "exact_pred": None,
+        "matrix_schema": "token_strand_adjacency_8x8_v1",
+        "matrix_hash": hashlib.sha256(matrix_json.encode("utf-8")).hexdigest(),
+        "matrix_8x8": matrix,
+        "global_vocab_hash": hashlib.sha256(
+            json.dumps(vocab, separators=(",", ":")).encode("utf-8")
+        ).hexdigest(),
+        "source_records": [],
+    }
+
+
+def main():
+    parser = argparse.ArgumentParser(description="Regenerate PIST predictions")
+    parser.add_argument("--receipt", type=Path,
+                        default=Path("/home/allaun/Research Stack/archive/experimental-shim-probes/rrc_equation_classifier_receipt.json"))
+    parser.add_argument("--output", type=Path,
+                        default=Path("/home/allaun/Research Stack/shared-data/rrc_pist_predictions_250_v1.json"))
+    args = parser.parse_args()
+
+    receipt = json.loads(args.receipt.read_text())
+    equations = receipt["compiled_equations"]
+
+    print(f"Loaded {len(equations)} source equations", file=sys.stderr)
+
+    # Dedup by equation_id: deterministic representative selection per AGENTS.md
+    # representative = min(records, key = equation_record.equation_id)
+    merged: dict[str, dict] = OrderedDict()
+    for eq in equations:
+        rec = eq.get("equation_record", eq)
+        eid = rec["equation_id"]
+        name = rec.get("name", eid)
+        text = rec.get("equation", "")
+
+        if eid not in merged:
+            merged[eid] = build_record(text, eid, name)
+            merged[eid]["source_records"] = []
+
+        merged[eid]["source_records"].append({
+            "equation_record_id": eid,
+            "name": name,
+        })
+
+    predictions = list(merged.values())
+
+    # Build global vocab
+    all_tokens = set()
+    for p in predictions:
+        all_tokens.update(tokenize(p.get("equation", "")))
+    global_vocab = sorted(all_tokens)
+
+    output = {
+        "schema": "rrc_pist_predictions_250_v1",
+        "generated_at": datetime.now(timezone.utc).isoformat(),
+        "claim_boundary": "matrix-only;no-classifier;no-lean-spectral",
+        "global_vocab_hash": hashlib.sha256(
+            json.dumps(global_vocab, separators=(",", ":")).encode("utf-8")
+        ).hexdigest(),
+        "global_vocab": global_vocab,
+        "matrix_schema": "token_strand_adjacency_8x8_v1",
+        "summary": {
+            "total_source_records": len(equations),
+            "unique_equation_ids": len(predictions),
+        },
+        "predictions": predictions,
+    }
+
+    args.output.parent.mkdir(parents=True, exist_ok=True)
+    args.output.write_text(json.dumps(output, indent=2) + "\n")
+    print(f"Wrote {args.output} ({len(predictions)} unique predictions from {len(equations)} source records)", file=sys.stderr)
+
+
+if __name__ == "__main__":
+    main()