diff --git a/aborist/qa/query.py b/aborist/qa/query.py
index 19ffdc8..3c7f49f 100644
--- a/aborist/qa/query.py
+++ b/aborist/qa/query.py
@@ -280,12 +280,18 @@ def _docs_with_core_keyword_match(
 def _body_density_passes(
     conn, document_root: str, qtokens: set[str], min_mentions: int = 3
 ) -> bool:
-    """Body-token relevance: docs that mention a query token >= N times pass.
+    """Body-token CO-OCCURRENCE relevance.
 
-    Cheap proxy for "this doc is actually about the topic" when title
-    doesn't help. Counts case-insensitive substring matches in concatenated
-    chunk content. min_mentions=3 skips docs that mention the term in
-    passing without being topical.
+    Pre-2026-04-27 this counted any token's mentions in the body (so
+    Intel_8086, with 30 "Intel" mentions, would pass for a query of
+    {intel, fastest, cpu} despite never mentioning "fastest"). The
+    revised gate requires at least HALF the query tokens to each appear
+    at least once, AND the total mention count to clear `min_mentions`.
+
+    For a 1-token query (e.g. {anarchism}) this simplifies to "the body
+    must mention the token at least min_mentions times" — same as before.
+    For multi-token queries, both signals must hold: breadth (most
+    tokens present) and depth (enough total mentions).
     """
     if not qtokens:
         return False
@@ -296,12 +302,11 @@ def _body_density_passes(
     if not rows:
         return False
     body = " ".join(unpack_chunk(r["content"]) or "" for r in rows).lower()
-    total_mentions = 0
-    for t in qtokens:
-        total_mentions += body.count(t.lower())
-        if total_mentions >= min_mentions:
-            return True
-    return False
+    counts = {t: body.count(t.lower()) for t in qtokens}
+    distinct_present = sum(1 for n in counts.values() if n > 0)
+    total_mentions = sum(counts.values())
+    breadth_threshold = max(1, (len(qtokens) + 1) // 2)
+    return distinct_present >= breadth_threshold and total_mentions >= min_mentions
 
 
 def _search_corpus(
@@ -357,12 +362,23 @@ def _search_corpus(
                 )
                 root_to_shard[h.document_root] = str(p.resolve())
             # Parallel title search using synonym-expanded tokens.
+            # The score starts deliberately low so this signal can't drown
+            # FTS5 BM25 + body relevance. _rerank_by_title later adds
+            # `overlap*10` to every hit (FTS5 and title-search alike) that
+            # has title-token overlap, so a doc whose title genuinely IS
+            # the topic ends up rewarded twice (once here, once in rerank).
+            # Single-token title matches against generic terms ("intel",
+            # "cpu") used to score 60+ standalone, dominating top-K with
+            # legacy 80486-era articles for queries like "fastest intel
+            # CPU?". Now that contribution is the same scale as FTS5 body
+            # BM25, which lets Pentium_4 (high body relevance, no title
+            # overlap) win on its actual topical fit.
             for r in _search_titles(conn, list(accept_tokens), over_fetch):
                 title_lower = (r["title"] or "").lower().replace("_", " ")
                 overlap = sum(1 for t in accept_tokens if t in title_lower)
                 if overlap == 0:
                     continue
-                title_score = 50.0 + overlap * 10.0
+                title_score = overlap * 10.0
                 raw.append(
                     (
                         title_score,