diff --git a/logicalplan/distribute.go b/logicalplan/distribute.go
index 446ce5d9..cfe27191 100644
--- a/logicalplan/distribute.go
+++ b/logicalplan/distribute.go
@@ -177,79 +177,125 @@ func (m DistributedExecutionOptimizer) Optimize(plan Node, opts *query.Options)
 		}
 	}
 
-	var warns = annotations.New()
+	warns := annotations.New()
+
+	parents := computeParents(&plan)
+	distributionPoints := m.computeDistributionPoints(&plan, parents, engineLabels, warns)
 
-	// TODO(fpetkovski): Consider changing TraverseBottomUp to pass in a list of parents in the transform function.
-	parents := make(map[*Node]*Node)
-	TraverseBottomUp(nil, &plan, func(parent, current *Node) (stop bool) {
-		parents[current] = parent
-		return false
-	})
 	TraverseBottomUp(nil, &plan, func(parent, current *Node) (stop bool) {
-		// Handle avg() specially - it's not distributive but can be distributed as sum/count.
+		if _, distributeNow := distributionPoints[current]; !distributeNow {
+			return false
+		}
+
 		if isAvgAggregation(current) {
 			*current = m.distributeAvg(*current, engines, m.subqueryOpts(parents, current, opts), labelRanges)
 			return true
 		}
 
-		// If the current operation is not distributive, stop the traversal.
-		if !isDistributive(current, m.SkipBinaryPushdown, engineLabels, warns) {
+		if isAbsent(current) {
+			*current = m.distributeAbsent(*current, engines, calculateStartOffset(current, opts.LookbackDelta), m.subqueryOpts(parents, current, opts))
 			return true
 		}
 
-		// Handle absent functions specially
-		if isAbsent(current) {
-			*current = m.distributeAbsent(*current, engines, calculateStartOffset(current, opts.LookbackDelta), m.subqueryOpts(parents, current, opts))
+		if isAggregation(current) {
+			*current = m.distributeAggregation((*current).(*Aggregation), engines, m.subqueryOpts(parents, current, opts), labelRanges)
 			return true
 		}
 
-		// If the current node is an aggregation, check if we should distribute here
-		// or continue traversing up.
-		if aggr, ok := (*current).(*Aggregation); ok {
-			// If this aggregation preserves partition labels and there's a
-			// distributive aggregation ancestor, continue up to let it handle distribution.
-			// This enables patterns like:
-			//   - topk(10, sum by (P, instance) (X))
-			//   - sum(metric_a * group by (P) (metric_b))
-			//   - max(sum by (P, instance) (X))
-			// where P is a partition label - we can push the entire expression
-			// to remote engines.
-			//
-			// We need to check ancestors (not just immediate parent) because the
-			// aggregation might be nested inside a binary expression that is itself
-			// inside another aggregation: sum(A * group by (P) (B))
+		*current = m.distributeQuery(current, engines, m.subqueryOpts(parents, current, opts), labelRanges)
+		return true
+	})
+	return plan, *warns
+}
+
+func (m DistributedExecutionOptimizer) distributeAggregation(aggr *Aggregation, engines []api.RemoteEngine, opts *query.Options, labelRanges labelSetRanges) Node {
+	localAggregation := aggr.Op
+	if aggr.Op == parser.COUNT {
+		localAggregation = parser.SUM
+	}
+	remoteAggregation := newRemoteAggregation(aggr, engines)
+	subQueries := m.distributeQuery(&remoteAggregation, engines, opts, labelRanges)
+	return &Aggregation{
+		Op:       localAggregation,
+		Expr:     subQueries,
+		Param:    aggr.Param,
+		Grouping: aggr.Grouping,
+		Without:  aggr.Without,
+	}
+}
+
+func computeParents(plan *Node) map[*Node]*Node {
+	parents := make(map[*Node]*Node)
+	TraverseBottomUp(nil, plan, func(parent, current *Node) (stop bool) {
+		parents[current] = parent
+		return false
+	})
+	return parents
+}
+
+func (m DistributedExecutionOptimizer) computeDistributionPoints(plan *Node, parents map[*Node]*Node, engineLabels map[string]struct{}, warns *annotations.Annotations) map[*Node]struct{} {
+	marks := make(map[*Node]struct{})
+
+	// First pass: mark all "forced" distribution points (aggregations that don't
+	// preserve partition labels, avg aggregations, absent functions).
+	Traverse(plan, func(current *Node) {
+		if isAvgAggregation(current) {
+			marks[current] = struct{}{}
+			return
+		}
+		if isAbsent(current) {
+			if m.isDistributive(current, engineLabels, warns) {
+				marks[current] = struct{}{}
+			}
+			return
+		}
+		if isAggregation(current) {
+			if !m.isDistributive(current, engineLabels, warns) {
+				return
+			}
 			if preservesPartitionLabels(*current, engineLabels) {
-				if hasDistributiveAncestor(parents, current, m.SkipBinaryPushdown, engineLabels, warns) {
-					return false
+				if m.hasDistributiveAncestor(parents, current, engineLabels, warns) {
+					return
 				}
 			}
-			localAggregation := aggr.Op
-			if aggr.Op == parser.COUNT {
-				localAggregation = parser.SUM
-			}
+			marks[current] = struct{}{}
+		}
+	})
 
-			remoteAggregation := newRemoteAggregation(aggr, engines)
-			subQueries := m.distributeQuery(&remoteAggregation, engines, m.subqueryOpts(parents, current, opts), labelRanges)
-			*current = &Aggregation{
-				Op:       localAggregation,
-				Expr:     subQueries,
-				Param:    aggr.Param,
-				Grouping: aggr.Grouping,
-				Without:  aggr.Without,
+	// Second pass: for nodes whose siblings have marks, mark them too so both
+	// sides of a binary expression get distributed.
+	Traverse(plan, func(current *Node) {
+		if _, ok := marks[current]; ok {
+			return
+		}
+		if subtreeHasMark(current, marks) {
+			return
+		}
+		if !m.isDistributive(current, engineLabels, warns) {
+			return
+		}
+		parent := parents[current]
+		if parent != nil && (m.isDistributive(parent, engineLabels, warns) || isAvgAggregation(parent)) {
+			if !subtreeHasMark(parent, marks) {
+				return
 			}
-			return true
 		}
+		marks[current] = struct{}{}
+	})
 
-		// If the parent operation is distributive or is an avg (which we handle specially),
-		// continue the traversal.
-		if isDistributive(parent, m.SkipBinaryPushdown, engineLabels, warns) || isAvgAggregation(parent) {
-			return false
-		}
+	return marks
+}
 
-		*current = m.distributeQuery(current, engines, m.subqueryOpts(parents, current, opts), labelRanges)
-		return true
-	})
-	return plan, *warns
+func subtreeHasMark(node *Node, marks map[*Node]struct{}) bool {
+	for _, child := range (*node).Children() {
+		if _, ok := marks[child]; ok {
+			return true
+		}
+		if subtreeHasMark(child, marks) {
+			return true
+		}
+	}
+	return false
 }
 
 func (m DistributedExecutionOptimizer) subqueryOpts(parents map[*Node]*Node, current *Node, opts *query.Options) *query.Options {
@@ -633,7 +679,7 @@ func preservesPartitionLabels(expr Node, partitionLabels map[string]struct{}) bo
 	}
 }
 
-func isDistributive(expr *Node, skipBinaryPushdown bool, engineLabels map[string]struct{}, warns *annotations.Annotations) bool {
+func (m DistributedExecutionOptimizer) isDistributive(expr *Node, engineLabels map[string]struct{}, warns *annotations.Annotations) bool {
 	if expr == nil {
 		return false
 	}
@@ -645,10 +691,10 @@ func isDistributive(expr *Node, skipBinaryPushdown bool, engineLabels map[string
 		if isBinaryExpressionWithOneScalarSide(e) {
 			return true
 		}
-		return !skipBinaryPushdown &&
+		return !m.SkipBinaryPushdown &&
 			isBinaryExpressionWithDistributableMatching(e, engineLabels) &&
-			isDistributive(&e.LHS, skipBinaryPushdown, engineLabels, warns) &&
-			isDistributive(&e.RHS, skipBinaryPushdown, engineLabels, warns)
+			m.isDistributive(&e.LHS, engineLabels, warns) &&
+			m.isDistributive(&e.RHS, engineLabels, warns)
 	case *Aggregation:
 		// Certain aggregations are currently not supported.
 		if _, ok := distributiveAggregations[e.Op]; !ok {
@@ -832,9 +878,9 @@ func matchesExternalLabels(ms []*labels.Matcher, externalLabels labels.Labels) b
 // parent chain from the current node that can handle distribution.
 // We must have an unbroken chain of distributive nodes to the ancestor for it to
 // be able to handle distribution on our behalf.
-func hasDistributiveAncestor(parents map[*Node]*Node, current *Node, skipBinaryPushdown bool, engineLabels map[string]struct{}, warns *annotations.Annotations) bool {
+func (m DistributedExecutionOptimizer) hasDistributiveAncestor(parents map[*Node]*Node, current *Node, engineLabels map[string]struct{}, warns *annotations.Annotations) bool {
 	for p := parents[current]; p != nil; p = parents[p] {
-		if !isDistributive(p, skipBinaryPushdown, engineLabels, warns) {
+		if !m.isDistributive(p, engineLabels, warns) {
 			// We hit a non-distributive node, so we can't push through it.
 			// No ancestor can help us distribute.
 			return false
diff --git a/logicalplan/distribute_test.go b/logicalplan/distribute_test.go
index f193f110..0068a63c 100644
--- a/logicalplan/distribute_test.go
+++ b/logicalplan/distribute_test.go
@@ -522,6 +522,13 @@ count by (cluster) (
 			expected:          `max(dedup(remote(metric_a), remote(metric_a)) + sum by (region, pod) (dedup(remote(sum by (pod, region) (metric_b)), remote(sum by (pod, region) (metric_b)))))`,
 			skipBinopPushdown: true,
 		},
+		{
+			// When the RHS of unless has an aggregation that drops the partition label,
+			// both sides should still be distributed independently.
+			name:     "unless with aggregation that drops partition label distributes both sides",
+			expr:     `group by (region, instance) (metric_a unless on (region, instance) max by (instance) (metric_b))`,
+			expected: `group by (region, instance) (dedup(remote(metric_a), remote(metric_a)) unless on (region, instance) max by (instance) (dedup(remote(max by (instance, region) (metric_b)), remote(max by (instance, region) (metric_b)))))`,
+		},
 		{
 			// group_left/group_right with partition label cannot be distributed because
 			// match cardinality changes when each partition only sees one value for that label.
diff --git a/logicalplan/logical_nodes.go b/logicalplan/logical_nodes.go
index 19be0e06..181d80ac 100644
--- a/logicalplan/logical_nodes.go
+++ b/logicalplan/logical_nodes.go
@@ -572,12 +572,18 @@ func shallowCloneSlice[T any](s []T) []T {
 	return clone
 }
 
-func isAvgAggregation(expr *Node) bool {
+func isAggregation(expr *Node) bool {
 	if expr == nil {
 		return false
 	}
-	if aggr, ok := (*expr).(*Aggregation); ok {
-		return aggr.Op == parser.AVG
+	_, ok := (*expr).(*Aggregation)
+	return ok
+}
+
+func isAvgAggregation(expr *Node) bool {
+	if expr == nil {
+		return false
 	}
-	return false
+	aggr, ok := (*expr).(*Aggregation)
+	return ok && aggr.Op == parser.AVG
 }