diff --git a/crates/ruvector-rulake/src/cache.rs b/crates/ruvector-rulake/src/cache.rs
index a46adaa8..900767ce 100644
--- a/crates/ruvector-rulake/src/cache.rs
+++ b/crates/ruvector-rulake/src/cache.rs
@@ -420,6 +420,63 @@ impl VectorCache {
             .collect())
     }
 
+    /// Batch form of [`search_cached_with_rerank`]: all `queries` hit
+    /// the same cached entry, so we take the mutex once and look up
+    /// the witness + pos_to_id mapping once. This is the surface a
+    /// GPU / SIMD kernel needs to amortize per-query setup; today's
+    /// CPU impl already saves the repeated mutex acquisition and
+    /// eliminates per-query `ensure_fresh` calls from the caller.
+    ///
+    /// Preserves the query order: `result[i]` is the top-k for
+    /// `queries[i]`.
+    pub fn search_cached_batch(
+        &self,
+        key: &CacheKey,
+        queries: &[Vec<f32>],
+        k: usize,
+        rerank_factor_override: Option<usize>,
+    ) -> crate::Result<Vec<Vec<(u64, f32)>>> {
+        let mut inner = self.inner.lock().unwrap();
+        let witness = inner
+            .pointers
+            .get(key)
+            .ok_or_else(|| crate::RuLakeError::UnknownCollection {
+                backend: key.0.clone(),
+                collection: key.1.clone(),
+            })?
+            .clone();
+        let entry = inner.entries.get_mut(&witness).ok_or_else(|| {
+            crate::RuLakeError::UnknownCollection {
+                backend: key.0.clone(),
+                collection: key.1.clone(),
+            }
+        })?;
+        let dim = entry.dim;
+        for q in queries {
+            if q.len() != dim {
+                return Err(crate::RuLakeError::DimensionMismatch {
+                    expected: dim,
+                    actual: q.len(),
+                });
+            }
+        }
+        entry.last_used = Instant::now();
+        let mut raw: Vec<Vec<ruvector_rabitq::SearchResult>> = Vec::with_capacity(queries.len());
+        for q in queries {
+            let r = match rerank_factor_override {
+                None => entry.index.search(q, k)?,
+                Some(rf) => entry.index.search_with_rerank(q, k, rf)?,
+            };
+            raw.push(r);
+        }
+        let pos_to_id = entry.pos_to_id.clone();
+        drop(inner);
+        Ok(raw
+            .into_iter()
+            .map(|v| v.into_iter().map(|r| (pos_to_id[r.id], r.score)).collect())
+            .collect())
+    }
+
     pub fn touch(&self, key: &CacheKey) {
         let mut inner = self.inner.lock().unwrap();
         inner.last_checked.insert(key.clone(), Instant::now());
diff --git a/crates/ruvector-rulake/src/lake.rs b/crates/ruvector-rulake/src/lake.rs
index 49b293f1..5289a7f8 100644
--- a/crates/ruvector-rulake/src/lake.rs
+++ b/crates/ruvector-rulake/src/lake.rs
@@ -298,6 +298,42 @@ impl RuLake {
             .collect())
     }
 
+    /// Batched single-collection search. All `queries` run against the
+    /// same `(backend, collection)` key, so coherence is checked once
+    /// and the cache mutex is held for a single round of N scans
+    /// instead of N separate acquires. Preserves query order:
+    /// `result[i]` is the top-k for `queries[i]`.
+    ///
+    /// This is also the plug-point for the future `VectorKernel` trait
+    /// (ADR-157): GPU / SIMD kernels cross over CPU only at batch
+    /// sizes above their `min_batch`, so a per-query API would never
+    /// let dispatch pick them. The batch API makes the dispatch
+    /// decision tractable.
+    pub fn search_batch(
+        &self,
+        backend: &str,
+        collection: &str,
+        queries: &[Vec<f32>],
+        k: usize,
+    ) -> Result<Vec<Vec<SearchResult>>> {
+        let key: CacheKey = (backend.to_string(), collection.to_string());
+        self.ensure_fresh(&key)?;
+        let raw = self.cache.search_cached_batch(&key, queries, k, None)?;
+        Ok(raw
+            .into_iter()
+            .map(|v| {
+                v.into_iter()
+                    .map(|(id, score)| SearchResult {
+                        backend: backend.to_string(),
+                        collection: collection.to_string(),
+                        id,
+                        score,
+                    })
+                    .collect()
+            })
+            .collect())
+    }
+
     /// Coherence check: ask the backend for its current bundle and
     /// compare its witness with whatever the cache currently points at.
     ///
diff --git a/crates/ruvector-rulake/tests/federation_smoke.rs b/crates/ruvector-rulake/tests/federation_smoke.rs
index 6746673b..9aa623f5 100644
--- a/crates/ruvector-rulake/tests/federation_smoke.rs
+++ b/crates/ruvector-rulake/tests/federation_smoke.rs
@@ -762,6 +762,75 @@ fn refresh_from_bundle_dir_reports_all_three_states() {
     let _ = std::fs::remove_dir_all(&tmp);
 }
 
+#[test]
+fn search_batch_matches_per_query_results() {
+    // search_batch must return the same top-k as calling search_one
+    // for each query individually. Byte-exact required: same seed,
+    // same rerank factor, same cache entry — the only difference is
+    // the API.
+    let d = 32;
+    let n = 500;
+    let seed = 61;
+    let data = clustered(n, d, 8, seed);
+    let backend = Arc::new(LocalBackend::new("batch"));
+    backend
+        .put_collection("c", d, (0..n as u64).collect(), data)
+        .unwrap();
+    let lake = RuLake::new(20, seed).with_consistency(Consistency::Eventual { ttl_ms: 60_000 });
+    lake.register_backend(backend).unwrap();
+
+    let queries = clustered(16, d, 8, seed ^ 0xa5a5);
+
+    // Prime.
+    lake.search_one("batch", "c", &queries[0], 5).unwrap();
+
+    let single: Vec<Vec<_>> = queries
+        .iter()
+        .map(|q| lake.search_one("batch", "c", q, 5).unwrap())
+        .collect();
+    let batch = lake.search_batch("batch", "c", &queries, 5).unwrap();
+    assert_eq!(single.len(), batch.len());
+    for (i, (a, b)) in single.iter().zip(batch.iter()).enumerate() {
+        assert_eq!(
+            a, b,
+            "batch and single diverged at query {i}: single={:?} batch={:?}",
+            a, b
+        );
+    }
+}
+
+#[test]
+fn search_batch_acquires_cache_lock_once() {
+    // A single search_batch(N=32) should register as one coherence-skip
+    // hit (Eventual) or one miss+prime on first call, NOT N individual
+    // hits. This is how operators can see the lock-amortization working.
+    let d = 16;
+    let backend = Arc::new(LocalBackend::new("amort"));
+    backend
+        .put_collection("c", d, (0..50).collect(), vec![vec![0.0; d]; 50])
+        .unwrap();
+    let lake = RuLake::new(20, 42).with_consistency(Consistency::Eventual { ttl_ms: 60_000 });
+    lake.register_backend(backend).unwrap();
+
+    // Prime.
+    lake.search_one("amort", "c", &vec![0.0f32; d], 1).unwrap();
+    let before = lake.cache_stats();
+
+    let queries = vec![vec![0.0f32; d]; 32];
+    let _ = lake.search_batch("amort", "c", &queries, 1).unwrap();
+    let after = lake.cache_stats();
+
+    // Before this change, per-query ensure_fresh bumped hits by N=32.
+    // The batch path bumps it by exactly 1.
+    let delta_hits = after.hits - before.hits;
+    assert_eq!(
+        delta_hits, 1,
+        "batch of 32 should register as 1 coherence check, got {delta_hits}"
+    );
+    // No new primes.
+    assert_eq!(after.primes, before.primes);
+}
+
 #[test]
 fn frozen_consistency_never_rechecks_after_prime() {
     // Frozen asserts immutability. After the first miss-prime, backend