diff --git a/src/pipeline/chunk_embedding.rs b/src/pipeline/chunk_embedding.rs
index 17a8555..f721d86 100644
--- a/src/pipeline/chunk_embedding.rs
+++ b/src/pipeline/chunk_embedding.rs
@@ -408,6 +408,7 @@ pub(super) fn try_batch_chunk_embedding(
                     hard_clusters: ChunkSpeakerClusters(Array2::zeros((0, 0))),
                     discrete_diarization: DiscreteDiarization(Array2::zeros((0, 0))),
                     segments: Vec::new(),
+                    exclusive_segments: Vec::new(),
                 })
             })
             .collect())
diff --git a/src/pipeline/post_inference.rs b/src/pipeline/post_inference.rs
index b869ea5..755f54d 100644
--- a/src/pipeline/post_inference.rs
+++ b/src/pipeline/post_inference.rs
@@ -3,7 +3,7 @@ use tracing::debug;
 
 use crate::binarize::binarize;
 use crate::clustering::plda::PldaTransform;
-use crate::reconstruct::Reconstructor;
+use crate::reconstruct::{Reconstructor, exclusive_from, resolve_exclusive_conflicts};
 use crate::segment::merge_segments;
 
 use super::config::{PipelineConfig, ReconstructMethod};
@@ -36,6 +36,7 @@ pub fn post_inference(
             hard_clusters: ChunkSpeakerClusters(Array2::zeros((0, 0))),
             discrete_diarization: DiscreteDiarization(Array2::zeros((0, 0))),
             segments: Vec::new(),
+            exclusive_segments: Vec::new(),
         });
     }
 
@@ -44,24 +45,38 @@ pub fn post_inference(
 
     let reconstructor =
         Reconstructor::with_clusters(&segmentations, &hard_clusters, &layout.start_frames, 0);
+    // One activation pass feeds both reconstructions; the exclusive variant needs the
+    // continuous scores, which a reconstruction has already flattened to 1.0.
+    let activations = reconstructor.frame_activations(&speaker_count);
     let discrete_diarization = match config.reconstruct_method {
         ReconstructMethod::Smoothed { epsilon } => {
-            reconstructor.reconstruct_smoothed(&speaker_count, epsilon)
+            reconstructor.reconstruct_smoothed_with(&activations, &speaker_count, epsilon)
+        }
+        ReconstructMethod::Standard => {
+            reconstructor.reconstruct_with(&activations, &speaker_count)
         }
-        ReconstructMethod::Standard => reconstructor.reconstruct(&speaker_count),
     };
+    let exclusive_diarization = exclusive_from(&discrete_diarization, &activations);
 
     // apply min-duration filtering to remove single-frame speaker flickers
     let has_duration_filter =
         config.binarize.min_duration_on > 0 || config.binarize.min_duration_off > 0;
-    let discrete_diarization = if has_duration_filter {
-        DiscreteDiarization(binarize(&discrete_diarization, &config.binarize))
+    let (discrete_diarization, exclusive_diarization) = if has_duration_filter {
+        (
+            DiscreteDiarization(binarize(&discrete_diarization, &config.binarize)),
+            DiscreteDiarization(binarize(&exclusive_diarization, &config.binarize)),
+        )
     } else {
-        discrete_diarization
+        (discrete_diarization, exclusive_diarization)
     };
+    // binarize runs per-speaker independently and can pad/extend two speakers' regions into the
+    // same frame, undoing the exclusivity exclusive_from established — re-resolve any conflicts.
+    let exclusive_diarization = resolve_exclusive_conflicts(&exclusive_diarization, &activations);
 
     let segments = discrete_diarization.to_segments();
     let segments = merge_segments(&segments, config.merge_gap);
+    let exclusive_segments = exclusive_diarization.to_segments();
+    let exclusive_segments = merge_segments(&exclusive_segments, config.merge_gap);
 
     debug!(
         post_inference_ms = post_start.elapsed().as_millis(),
@@ -75,5 +90,6 @@ pub fn post_inference(
         hard_clusters,
         discrete_diarization,
         segments,
+        exclusive_segments,
     })
 }
diff --git a/src/pipeline/types/data.rs b/src/pipeline/types/data.rs
index 6ca4c04..e9e427f 100644
--- a/src/pipeline/types/data.rs
+++ b/src/pipeline/types/data.rs
@@ -172,6 +172,10 @@ pub struct DiarizationResult {
     pub discrete_diarization: DiscreteDiarization,
     /// Merged speaker segments (time-stamped speaker turns)
     pub segments: Vec<crate::segment::Segment>,
+    /// Merged speaker segments with at most one speaker at any instant — the
+    /// `exclusive_speaker_diarization` equivalent. Overlapped frames go to the speaker with
+    /// the highest activation score, so the speech is kept rather than split or dropped.
+    pub exclusive_segments: Vec<crate::segment::Segment>,
 }
 
 impl DiarizationResult {
diff --git a/src/reconstruct.rs b/src/reconstruct.rs
index ce401c3..d756dc9 100644
--- a/src/reconstruct.rs
+++ b/src/reconstruct.rs
@@ -134,29 +134,43 @@ impl<'a> Reconstructor<'a> {
         FrameActivations(activations)
     }
 
+    /// Convenience wrapper over [`reconstruct_with`](Self::reconstruct_with); production code
+    /// computes activations once and shares them with the exclusive pass.
+    #[cfg(test)]
     pub fn reconstruct(&self, speaker_count: &SpeakerCountTrack) -> DiscreteDiarization {
-        let activations = self.frame_activations(speaker_count);
+        self.reconstruct_with(&self.frame_activations(speaker_count), speaker_count)
+    }
+
+    /// [`reconstruct`](Self::reconstruct) over activations the caller already computed —
+    /// so a caller that also needs the exclusive variant pays for one activation pass.
+    pub(crate) fn reconstruct_with(
+        &self,
+        activations: &FrameActivations,
+        speaker_count: &SpeakerCountTrack,
+    ) -> DiscreteDiarization {
         let mut discrete = Array2::<f32>::zeros(activations.raw_dim());
         for (frame_idx, &count) in speaker_count.iter().enumerate() {
-            for speaker_idx in top_k_indices(&activations, frame_idx, count) {
+            for speaker_idx in top_k_indices(activations, frame_idx, count) {
                 discrete[[frame_idx, speaker_idx]] = 1.0;
             }
         }
         DiscreteDiarization(discrete)
     }
 
-    pub fn reconstruct_smoothed(
+    /// Smoothed reconstruction over caller-supplied activations, so a caller that also needs
+    /// the exclusive variant pays for one activation pass.
+    pub(crate) fn reconstruct_smoothed_with(
         &self,
+        activations: &FrameActivations,
         speaker_count: &SpeakerCountTrack,
         epsilon: f32,
     ) -> DiscreteDiarization {
-        let activations = self.frame_activations(speaker_count);
         let mut discrete = Array2::<f32>::zeros(activations.raw_dim());
         let mut previous_speakers: Vec<usize> = Vec::new();
 
         for (frame_idx, &count) in speaker_count.iter().enumerate() {
             let current_speakers =
-                top_k_indices_smoothed(&activations, frame_idx, count, &previous_speakers, epsilon);
+                top_k_indices_smoothed(activations, frame_idx, count, &previous_speakers, epsilon);
             for &speaker_idx in &current_speakers {
                 discrete[[frame_idx, speaker_idx]] = 1.0;
             }
@@ -167,6 +181,81 @@ impl<'a> Reconstructor<'a> {
     }
 }
 
+/// Collapse a reconstruction to one speaker per frame, keeping the speaker whose *activation
+/// score* is highest among those the reconstruction marked active.
+///
+/// This is the `exclusive_speaker_diarization` equivalent, and it has to read the continuous
+/// activations to mean anything: a reconstruction stores 1.0 for every active speaker, so an
+/// argmax taken over it is a tie that resolves to whichever cluster index the comparator
+/// happens to favour — the choice ends up made by cluster numbering rather than by acoustics.
+///
+/// Speech is never invented or lost: a frame with at least one active speaker keeps exactly
+/// one, and a frame with none stays empty.
+pub(crate) fn exclusive_from(
+    full: &DiscreteDiarization,
+    activations: &FrameActivations,
+) -> DiscreteDiarization {
+    let mut discrete = Array2::<f32>::zeros(full.raw_dim());
+    for (frame_idx, row) in full.rows().into_iter().enumerate() {
+        let mut best: Option<(usize, f32)> = None;
+        for (speaker_idx, &value) in row.iter().enumerate() {
+            if value <= 0.0 {
+                continue;
+            }
+            let score = activations
+                .get([frame_idx, speaker_idx])
+                .copied()
+                .unwrap_or(0.0);
+            if best.is_none_or(|(_, best_score)| score > best_score) {
+                best = Some((speaker_idx, score));
+            }
+        }
+        if let Some((speaker_idx, _)) = best {
+            discrete[[frame_idx, speaker_idx]] = 1.0;
+        }
+    }
+    DiscreteDiarization(discrete)
+}
+
+/// `exclusive_from` guarantees at most one active speaker per frame, but that guarantee doesn't
+/// survive `binarize`: it runs per-speaker independently, so `min_duration_on`/`pad_onset`/etc.
+/// can extend one speaker's region into a frame another speaker's region was independently
+/// extended into, reintroducing overlap in the "exclusive" output. Re-collapse any such frame to
+/// its highest-activation speaker, using the same scores `exclusive_from` used originally.
+pub(crate) fn resolve_exclusive_conflicts(
+    exclusive: &DiscreteDiarization,
+    activations: &FrameActivations,
+) -> DiscreteDiarization {
+    let mut discrete = exclusive.0.clone();
+    let num_speakers = discrete.ncols();
+    for frame_idx in 0..discrete.nrows() {
+        let mut best: Option<(usize, f32)> = None;
+        let mut active_count = 0usize;
+        for speaker_idx in 0..num_speakers {
+            if discrete[[frame_idx, speaker_idx]] > 0.0 {
+                active_count += 1;
+                let score = activations
+                    .get([frame_idx, speaker_idx])
+                    .copied()
+                    .unwrap_or(0.0);
+                if best.is_none_or(|(_, best_score)| score > best_score) {
+                    best = Some((speaker_idx, score));
+                }
+            }
+        }
+        if active_count > 1
+            && let Some((winner, _)) = best
+        {
+            for speaker_idx in 0..num_speakers {
+                if speaker_idx != winner {
+                    discrete[[frame_idx, speaker_idx]] = 0.0;
+                }
+            }
+        }
+    }
+    DiscreteDiarization(discrete)
+}
+
 /// Zero out all but the highest-scoring speaker in each frame, making activations exclusive
 pub fn make_exclusive(activations: &mut Array2<f32>) {
     for mut row in activations.rows_mut() {