fix(wiki): token-boundary grounding match + sharper ref-list filter (C-6, R-2)

- C-6: the content-5-gram check used 'gram in src' on space-joined strings, so the
  first/last gram token could match a prefix/suffix of a longer chunk word
  ('set' inside 'subset'). Both gram and chunk are now space-padded, so a match
  requires whole-token alignment. Regression test added.
- R-2: the reference-list filter's author pattern ('Surname, I.') also matched
  'Theorem A.'/'Lemma B.', so theorem-dense chunks could be dropped as a
  bibliography. The pattern now excludes common math-structure words.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

codex/wiki.py

@@ -186,8 +186,15 @@ def _retrieve_chunks(
         ).fetchall()
 
     # Filter reference-list chunks: skip chunks whose content looks like a bibliography
-    # (heuristic: > 60 % of lines match "^\[\d+\]" or "^[A-Z][a-z]+,?\s+[A-Z]\.").
-    ref_pattern = re.compile(r"^\s*(\[\d+\]|[A-Z][a-z]+,?\s+[A-Z]\.)", re.MULTILINE)
+    # (heuristic: > 60 % of lines look like "[12]" refs or "Surname, I." authors).
+    # The author alternative excludes common math-structure words ("Theorem A.",
+    # "Lemma B.") so theorem-dense chunks aren't mistaken for a reference list (R-2).
+    ref_pattern = re.compile(
+        r"^\s*(\[\d+\]|(?!(?:Theorem|Lemma|Proposition|Corollary|Definition|Proof|"
+        r"Section|Figure|Fig|Table|Equation|Eq|Remark|Example|Chapter)\b)"
+        r"[A-Z][a-z]+,?\s+[A-Z]\.)",
+        re.MULTILINE,
+    )
     filtered: list[dict[str, Any]] = []
     for row in rows:
         content: str = row["content"]
@@ -336,8 +343,9 @@ def _run_grounding_guard(
     for chunk in chunks:
         bk = str(chunk.get("bibkey") or "")
         if bk:
-            # Content words of the chunk (stopwords removed, lowercased)
-            cw = " ".join(_content_words(chunk["content"]))
+            # Content words of the chunk, space-padded so a 5-gram match is bounded
+            # by whole tokens, not a substring bleeding across words (audit C-6).
+            cw = " " + " ".join(_content_words(chunk["content"])) + " "
             bib_index.setdefault(bk, []).append(cw)
 
     for claim in claims:
@@ -359,7 +367,7 @@ def _run_grounding_guard(
 
         found = False
         for i in range(len(content) - 4):  # content-5-grams
-            gram = " ".join(content[i : i + 5])
+            gram = " " + " ".join(content[i : i + 5]) + " "  # padded: token-boundary match
             if any(gram in src for src in sources):
                 found = True
                 break