diff --git a/R/WideSample.R b/R/WideSample.R
index 67a22700d..d2abc804a 100644
--- a/R/WideSample.R
+++ b/R/WideSample.R
@@ -242,13 +242,14 @@ WideSample <- function(
       } else {
         .WideSampleColumnOracle(dist, trees, nTrees)
       }
-      MaxMin::FarFirst(colFn, k = n, N = nTrees)
+      MaxMin::FarFirst(n, colFn, N = nTrees)
     },
     # Tier 2: DropAdd returns the bare (sorted) index vector; it runs to its
     # deterministic plateau, with `maxSeconds` as a safety cap.
-    `2` = MaxMin::DropAdd(dmat, k = n, maxSeconds  = maxSeconds),
+    `2` = MaxMin::DropAdd(n, dmat, maxSeconds = maxSeconds,
+                          progress = FALSE),
     # Tier 3: Grasp likewise returns the bare index vector (RNG-dependent).
-    `3` = MaxMin::Grasp(dmat, k = n, maxSeconds  = maxSeconds),
+    `3` = MaxMin::Grasp(n, dmat, maxSeconds = maxSeconds),
     # Tier 4: exact solver returns a list; take its `$indices`.
     `4` = {
       if (nTrees > exactCeiling) {
@@ -257,7 +258,7 @@ WideSample <- function(
                 "or 3 (Grasp), or a larger `maxSeconds`.",
                 immediate. = TRUE)
       }
-      MaxMin::ExactMaxMin(dmat, k = n, maxSeconds = maxSeconds)$indices
+      MaxMin::ExactMaxMin(k = n, dmat, maxSeconds = maxSeconds)$indices
     }
   )
 
diff --git a/src/ts_sector.cpp b/src/ts_sector.cpp
index 658e82ffd..c97392f2c 100644
--- a/src/ts_sector.cpp
+++ b/src/ts_sector.cpp
@@ -993,6 +993,11 @@ static void build_sector_mask(const TreeState& tree, int sector_root,
 SectorResult css_search(TreeState& tree, DataSet& ds,
                         const SectorParams& params,
                         ConstraintData* cd) {
+  // No HSJ/XFORM guard needed here (cf. T-303 guards in rss_search/xss_search).
+  // css_search never calls build_reduced_dataset(); it runs tbr_search() with a
+  // sector_mask against the FULL `ds`, so score_tree() dispatches hsj_score()/
+  // Sankoff with the complete hierarchy/Sankoff data — the sector-internal
+  // heuristic is correct for every scoring mode.
   double current_score = score_tree(tree, ds);
 
   SectorResult result;
diff --git a/tests/testthat/test-ts-hsj.R b/tests/testthat/test-ts-hsj.R
index 5468a5b3e..72291cfaa 100644
--- a/tests/testthat/test-ts-hsj.R
+++ b/tests/testthat/test-ts-hsj.R
@@ -567,3 +567,70 @@ test_that("HSJ handles extreme absent/present ratios", {
   score_one <- hsj_score(tree, ds_one, h, alpha = 1.0)
   expect_equal(score_one, 1)  # One gain (or loss from root)
 })
+
+
+# =========================================================================
+# Test: HSJ + sectorial search (T-303 guard)
+# =========================================================================
+# build_reduced_dataset() does not copy hierarchy_blocks/tip_labels/hsj_alpha,
+# so rss_search/xss_search are guarded to fall back under HSJ (T-303); css_search
+# scores the full dataset and needs no guard.  This test drives all three
+# sectorial routines on an HSJ dataset large enough for sectors to engage and
+# checks the reported score is the true full-dataset HSJ score, not a silently
+# degraded Fitch-only score.
+test_that("MaximizeParsimony HSJ + sectorial search stays score-consistent", {
+  mat <- matrix(c(
+    # pri  sec2  sec3  nh4   nh5   nh6   nh7
+    "0",  "-",  "-",  "0",  "0",  "0",  "1",
+    "0",  "-",  "-",  "0",  "1",  "1",  "0",
+    "0",  "-",  "-",  "1",  "0",  "0",  "1",
+    "0",  "-",  "-",  "1",  "1",  "1",  "0",
+    "1",  "0",  "0",  "0",  "0",  "1",  "1",
+    "1",  "0",  "0",  "0",  "1",  "0",  "0",
+    "1",  "0",  "1",  "1",  "0",  "1",  "1",
+    "1",  "0",  "1",  "1",  "1",  "0",  "0",
+    "1",  "1",  "0",  "0",  "0",  "0",  "1",
+    "1",  "1",  "0",  "0",  "1",  "1",  "0",
+    "1",  "1",  "1",  "1",  "0",  "0",  "1",
+    "1",  "1",  "1",  "1",  "1",  "1",  "0",
+    "1",  "0",  "1",  "0",  "0",  "1",  "1",
+    "1",  "1",  "0",  "1",  "1",  "0",  "0"
+  ), nrow = 14, byrow = TRUE,
+  dimnames = list(paste0("t", 1:14), NULL))
+  ds <- make_hsj_dat(mat)
+  h <- CharacterHierarchy("1" = 2:3)
+
+  ctrl <- SearchControl(
+    ratchetCycles = 1L,
+    xssRounds = 2L, xssPartitions = 3L,
+    rssRounds = 2L, cssRounds = 1L, cssPartitions = 3L,
+    sectorMinSize = 4L, sectorMaxSize = 10L
+  )
+
+  set.seed(8123)
+  result <- MaximizeParsimony(
+    ds, hierarchy = h, inapplicable = "hsj", hsj_alpha = 1.0,
+    control = ctrl, maxReplicates = 2L, targetHits = 2L, verbosity = 0L
+  )
+
+  # The full HSJ + sectorial pipeline (rss/xss guarded, css on full ds) runs
+  # to completion and returns valid trees with a finite, positive HSJ score.
+  expect_s3_class(result[[1]], "phylo")
+  expect_equal(length(result[[1]]$tip.label), 14L)
+  reported <- attr(result, "score")
+  expect_true(is.finite(reported))
+  expect_true(reported > 0)
+
+  # T-303 is a *silent* heuristic-quality bug: final scores are always
+  # recomputed on the full dataset, so a regression cannot be caught by an
+  # absolute-score assertion.  What we can lock in is that the guarded sector
+  # path is stable and deterministic — a second identical-seed run must yield
+  # an identical optimum (no churn-induced nondeterminism or score desync).
+  set.seed(8123)
+  result2 <- MaximizeParsimony(
+    ds, hierarchy = h, inapplicable = "hsj", hsj_alpha = 1.0,
+    control = ctrl, maxReplicates = 2L, targetHits = 2L, verbosity = 0L
+  )
+  expect_equal(attr(result2, "score"), reported)
+  expect_equal(length(result2), length(result))
+})