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/index.md

@@ -4,7 +4,7 @@ layout: home
 hero:
   name: MemPalace
   text: Give your AI a memory.
-  tagline: "96.6% recall on LongMemEval in raw mode. Local-first, open source, and usable without an API key."
+  tagline: "Local-first AI memory. Verbatim storage, pluggable backend, 96.6% R@5 raw on LongMemEval — zero API calls."
   image:
     src: /mempalace_logo.png
     alt: MemPalace
@@ -34,7 +34,7 @@ features:
       src: /icons/search.svg
       alt: Semantic Search
     title: Semantic Search
-    details: ChromaDB-powered vector search lets the model retrieve past discussions by topic, project, or room.
+    details: Vector search over verbatim content lets the model retrieve past discussions by topic, project, or room. Backend is pluggable.
   - icon:
       src: /icons/git-merge.svg
       alt: Knowledge Graph
@@ -49,7 +49,7 @@ features:
       src: /icons/shield-check.svg
       alt: Zero Cloud
     title: Zero Cloud
-    details: Core storage and retrieval run locally on ChromaDB and SQLite. Optional reranking features can add an API dependency.
+    details: Core storage and retrieval run locally. Optional reranking features can add an API dependency but are not required for the benchmark path.
 ---
 
 <style>
@@ -68,20 +68,21 @@ features:
 
 ## Verbatim Retrieval First
 
-MemPalace starts from a simple premise: **store the source text and retrieve it well**. The benchmarked raw mode does not require an LLM extraction step.
+MemPalace stores source text and retrieves it with semantic search. The benchmarked raw mode does not require an LLM at any stage — no extraction, no rerank, no summarisation.
 
-| System | LongMemEval R@5 | API Required | Cost |
-|--------|----------------|--------------|------|
-| **MemPalace (hybrid)** | **100%** | Optional | Free |
-| Supermemory ASMR | ~99% | Yes | — |
-| **MemPalace (raw)** | **96.6%** | **None** | **Free** |
-| Mastra | 94.87% | Yes | API costs |
-| Mem0 | ~85% | Yes | $19–249/mo |
+**LongMemEval retrieval recall (500 questions):**
 
-The raw 96.6% LongMemEval result is the baseline story: strong recall without requiring an API key or an LLM in the retrieval pipeline.
+| Mode | R@5 | LLM required |
+|---|---|---|
+| Raw (semantic search over verbatim text) | **96.6%** | None |
+| Hybrid v4, held-out 450q | **98.4%** | None |
+
+The raw 96.6% reproduces on any machine with the committed dataset: result JSONLs, the `seed=42` train/held-out split, and the `--mode raw` / `--held-out` runners are all in the `benchmarks/` directory of the repo.
+
+We deliberately do not publish a side-by-side comparison against other memory systems on this page. Retrieval recall (R@5) and end-to-end QA accuracy are different metrics and are not comparable; where MemPalace can be fairly compared on the same metric, we link to the other project's published source.
 
 <div style="text-align: center; padding-top: 16px;">
-  <a href="./reference/benchmarks" style="color: var(--vp-c-brand-1); font-weight: 500;">Full benchmark results →</a>
+  <a href="./reference/benchmarks" style="color: var(--vp-c-brand-1); font-weight: 500;">Full benchmark methodology →</a>
 </div>
 
 </div>