diff --git a/lib/ocaml/baseline/expected.json b/lib/ocaml/baseline/expected.json
index 2c9d525b..a3a88858 100644
--- a/lib/ocaml/baseline/expected.json
+++ b/lib/ocaml/baseline/expected.json
@@ -82,6 +82,7 @@
   "kadane.ml": 6,
   "kmp.ml": 5,
   "lambda_calc.ml": 7,
+  "lcs.ml": 4,
   "majority_vote.ml": 4,
   "levenshtein.ml": 11,
   "memo_fib.ml": 75025,
diff --git a/lib/ocaml/baseline/lcs.ml b/lib/ocaml/baseline/lcs.ml
new file mode 100644
index 00000000..bc3a53fa
--- /dev/null
+++ b/lib/ocaml/baseline/lcs.ml
@@ -0,0 +1,23 @@
+let lcs s1 s2 =
+  let m = String.length s1 in
+  let n = String.length s2 in
+  let prev = Array.make (n + 1) 0 in
+  let curr = Array.make (n + 1) 0 in
+  for i = 1 to m do
+    for j = 1 to n do
+      if s1.[i - 1] = s2.[j - 1] then
+        curr.(j) <- prev.(j - 1) + 1
+      else if prev.(j) >= curr.(j - 1) then
+        curr.(j) <- prev.(j)
+      else
+        curr.(j) <- curr.(j - 1)
+    done;
+    for j = 0 to n do
+      prev.(j) <- curr.(j)
+    done
+  done;
+  prev.(n)
+
+;;
+
+lcs "ABCBDAB" "BDCAB"
diff --git a/plans/ocaml-on-sx.md b/plans/ocaml-on-sx.md
index 6d09d730..2463a2e2 100644
--- a/plans/ocaml-on-sx.md
+++ b/plans/ocaml-on-sx.md
@@ -407,6 +407,13 @@ _Newest first._
   binary search tree (`type 'a tree = Leaf | Node of 'a * 'a tree *
   'a tree`) with insert + in-order traversal. Tests parametric ADT,
   recursive match, List.append, List.fold_left.
+- 2026-05-10 Phase 5.1 — lcs.ml baseline (longest common subsequence
+  of "ABCBDAB" and "BDCAB" = 4). Rolling-array DP in O(mn) time and
+  O(min(m,n)) space: keep `prev` and `curr` rows, copy after each
+  outer step. Two LCS witnesses: "BCAB" and "BDAB". Avoids needing
+  `Array.make_matrix` (not in our runtime) by manual rolling. Tests
+  string indexing, double-nested for-loops, sibling for to copy
+  rows, integer compare. 142 baseline programs total.
 - 2026-05-10 Phase 5.1 — dijkstra.ml baseline (single-source shortest
   path on a 5-node weighted graph, dist 0→4 = 7). O(n²) array-based
   Dijkstra: at each step, scan unvisited vertices for the minimum