docs(website): align mempalaceofficial.com with honest benchmarks

Part of #875. Bring the VitePress site into line with the new README
and the reproducibility scorecard: drop category-error comparisons,
drop retracted claims, retain only metrics and caveats that survive
audit.

website/index.md
 - New tagline matches README (local-first, verbatim, pluggable backend,
   96.6% R@5 raw, zero API calls).
 - Replace the "MemPalace hybrid 100% / Supermemory ~99% / Mastra
   94.87% / Mem0 ~85%" comparison table with a single honest table
   showing MemPalace's own retrieval-recall numbers (raw 96.6%,
   hybrid v4 held-out 98.4%). Add an explicit sentence explaining why
   we no longer publish a cross-system table on the landing page
   (retrieval recall vs QA accuracy are different metrics).
 - Soften the "ChromaDB-powered vector search" feature blurb to be
   backend-agnostic, since the retrieval layer is pluggable.

website/reference/benchmarks.md
 - Full rewrite of the retrieval-recall tables. No more "100%"
   headline; honest held-out 98.4% R@5 replaces it. Added the
   model-agnostic rerank result (99.2% R@5 / 100% R@10 with
   minimax-m2.7 via Ollama) to show the pipeline is not Haiku-specific.
 - Drop the LoCoMo "Hybrid v5 + Sonnet rerank (top-50) 100%" row.
   With per-conversation session counts of 19-32 and top_k=50, the
   retrieval stage returns every session by construction — the number
   measures an LLM's reading comprehension, not retrieval.
 - Drop the cross-system comparison tables. Link out to each project's
   own research page (Mastra, Mem0, Supermemory) for their published
   numbers and metric definitions.
 - Rewrite reproduction commands to use the correct repository and
   demonstrate the new --llm-backend ollama flag.

website/concepts/the-palace.md
 - Remove the "+34%" row / paragraph. Wing/room filtering is standard
   metadata filtering in the vector store, not a novel retrieval
   mechanism — the April-7 note already retracted that framing; this
   finishes the retraction on the website where it had remained.

website/guide/searching.md
 - Same treatment for "34% retrieval improvement". Reframe as
   operational scoping, not a novel boost.

website/reference/contributing.md
 - Update the "palace structure matters" bullet to reflect the same
   framing: scoping-not-magic.

website/concepts/knowledge-graph.md
 - Replace the MemPalace-vs-Zep feature matrix with a short "related
   work" note that links to Zep's own documentation for authoritative
   details on their deployment model. Avoids claims we cannot verify
   at source.

website/concepts/knowledge-graph.md

@@ -80,12 +80,11 @@ The knowledge graph uses SQLite with two tables:
 
 Database location: `~/.mempalace/knowledge_graph.sqlite3`
 
-## Comparison
+## Related Work
 
-| Feature | MemPalace | Zep (Graphiti) |
-|---------|-----------|----------------|
-| Storage | SQLite (local) | Neo4j (cloud) |
-| Cost | Free | $25/mo+ |
-| Temporal validity | Yes | Yes |
-| Self-hosted | Always | Enterprise only |
-| Privacy | Everything local | SOC 2, HIPAA |
+Temporal entity-relationship graphs are a familiar pattern — Zep's
+Graphiti, for example, also exposes a bi-temporal model. MemPalace's
+knowledge graph is local-first (SQLite, everything on disk) and free;
+Zep is a managed service backed by Neo4j with its own pricing, SLAs,
+and compliance surface. See Zep's own [documentation](https://www.getzep.com/)
+for authoritative details on their deployment model.

website/concepts/the-palace.md

@@ -92,16 +92,9 @@ The original stored text chunks. This is the primary retrieval layer used by the
 
 ## Why Structure Matters
 
-Tested on 22,000+ real conversation memories:
+Wing and room identifiers become metadata filters at query time. Narrowing a search to a specific wing (or wing + room) means the vector store only scores candidates inside that scope, which is useful when you have many unrelated projects or people filed in the same palace.
 
-| Search scope | R@10 | Improvement |
-|-------------|------|-------------|
-| All closets | 60.9% | baseline |
-| Within wing | 73.1% | +12% |
-| Wing + hall | 84.8% | +24% |
-| Wing + room | 94.8% | +34% |
-
-The practical point is that structure improves retrieval. In the project benchmarks, narrowing the search scope by wing and room outperformed searching the entire corpus at once.
+This is standard metadata filtering in the underlying vector store, not a novel retrieval mechanism. The useful property here is operational — clear scoping rules that a human or an agent can apply predictably — not a magic retrieval boost.
 
 ## Navigation