diff --git a/dotnet/src/Plugins/Plugins.UnitTests/Memory/VolatileMemoryStoreTests.cs b/dotnet/src/Plugins/Plugins.UnitTests/Memory/VolatileMemoryStoreTests.cs
index 90d01f8547d2..f1b8873e17c5 100644
--- a/dotnet/src/Plugins/Plugins.UnitTests/Memory/VolatileMemoryStoreTests.cs
+++ b/dotnet/src/Plugins/Plugins.UnitTests/Memory/VolatileMemoryStoreTests.cs
@@ -454,6 +454,70 @@ public async Task GetNearestMatchAsyncReturnsExpectedAsync()
         Assert.True(topNResult.Value.Item2 >= threshold);
     }
 
+    [Fact]
+    public async Task GetNearestMatchesUsesCosineSimilarityForOppositeMeaningAsync()
+    {
+        // Arrange
+        var queryEmbedding = new float[] { 1, 0 };
+        string collection = "test_collection" + this._collectionNum;
+        this._collectionNum++;
+        await this._db.CreateCollectionAsync(collection);
+
+        // These text/embedding pairs deliberately model the high-similarity range
+        // reported by the issue's embedding audit; this unit test does not compute
+        // embeddings and therefore does not claim to reproduce a model's output.
+        _ = await this._db.UpsertAsync(collection, MemoryRecord.LocalRecord(
+            id: "matching",
+            text: "Withhold the study drug when chest tightness is reported.",
+            description: "matching polarity",
+            embedding: new float[] { 0.98f, 0.199f }));
+        _ = await this._db.UpsertAsync(collection, MemoryRecord.LocalRecord(
+            id: "opposite",
+            text: "Administer the study drug when chest tightness is reported.",
+            description: "opposite polarity",
+            embedding: new float[] { 0.98f, 0.199f }));
+
+        // Act
+        var results = await this._db.GetNearestMatchesAsync(
+            collection,
+            queryEmbedding,
+            limit: 2,
+            minRelevanceScore: 0.75).ToArrayAsync();
+
+        // Assert
+        Assert.Equal(2, results.Length);
+        Assert.Contains(results, result => result.Item1.Metadata.Id == "matching");
+        Assert.Contains(results, result => result.Item1.Metadata.Id == "opposite");
+        Assert.All(results, result => Assert.True(result.Item2 >= 0.75));
+    }
+
+    [Fact]
+    public async Task GetNearestMatchesFiltersLowSimilarityParaphraseAsync()
+    {
+        // Arrange
+        var queryEmbedding = new float[] { 1, 0 };
+        string collection = "test_collection" + this._collectionNum;
+        this._collectionNum++;
+        await this._db.CreateCollectionAsync(collection);
+
+        _ = await this._db.UpsertAsync(collection, MemoryRecord.LocalRecord(
+            id: "paraphrase",
+            text: "Do not administer the medication if the participant reports chest tightness.",
+            description: "semantically equivalent paraphrase",
+            embedding: new float[] { 0.6f, 0.8f }));
+
+        // Act
+        var results = await this._db.GetNearestMatchesAsync(
+            collection,
+            queryEmbedding,
+            limit: 1,
+            minRelevanceScore: 0.75).ToArrayAsync();
+
+        // Assert: a fixed similarity threshold cannot recover a semantically
+        // equivalent result whose embedding falls below that threshold.
+        Assert.Empty(results);
+    }
+
     [Fact]
     public async Task GetNearestMatchesDifferentiatesIdenticalVectorsByKeyAsync()
     {
diff --git a/python/tests/unit/memory/generate_recorded_nomic_relevance_embeddings.py b/python/tests/unit/memory/generate_recorded_nomic_relevance_embeddings.py
new file mode 100644
index 000000000000..acb1895435fa
--- /dev/null
+++ b/python/tests/unit/memory/generate_recorded_nomic_relevance_embeddings.py
@@ -0,0 +1,63 @@
+# Copyright (c) Microsoft. All rights reserved.
+"""Regenerate the offline relevance fixture from a local LM Studio server."""
+
+import json
+import struct
+import urllib.request
+from datetime import UTC, datetime
+from pathlib import Path
+
+MODEL = "text-embedding-nomic-embed-text-v1.5"
+CHECKPOINT = "nomic-ai/nomic-embed-text-v1.5"
+ENDPOINT = "http://127.0.0.1:1234/v1/embeddings"
+TEXTS = {
+    "stored": "Before deleting user data, require explicit confirmation.",
+    "negated_query": "Before deleting user data, do not require explicit confirmation.",
+    "paraphrase_stored": "Erase records only after the subject has authorized the action.",
+    "paraphrase_query": "Obtain affirmative consent from the person before erasing any of their personal information.",
+}
+OUTPUT = Path(__file__).with_name("recorded_nomic_relevance_embeddings.json")
+
+
+def embed(text: str) -> list[float]:
+    request = urllib.request.Request(
+        ENDPOINT,
+        data=json.dumps({"model": MODEL, "input": text}).encode(),
+        headers={"Content-Type": "application/json"},
+    )
+    with urllib.request.urlopen(request) as response:
+        return json.load(response)["data"][0]["embedding"]
+
+
+def cosine(left: list[float], right: list[float]) -> float:
+    dot = sum(a * b for a, b in zip(left, right, strict=True))
+    left_norm = sum(value * value for value in left) ** 0.5
+    right_norm = sum(value * value for value in right) ** 0.5
+    return dot / (left_norm * right_norm)
+
+
+vectors = {name: embed(text) for name, text in TEXTS.items()}
+OUTPUT.write_text(
+    json.dumps(
+        {
+            "model": MODEL,
+            "checkpoint": CHECKPOINT,
+            "endpoint": ENDPOINT,
+            "generator": "python tests/unit/memory/generate_recorded_nomic_relevance_embeddings.py",
+            "generated_at_utc": datetime.now(UTC).isoformat(),
+            "dimension": len(vectors["stored"]),
+            "float_format": "IEEE-754 float32",
+            "texts": TEXTS,
+            "cosine_similarity": {
+                "stored_to_negated_query": cosine(vectors["stored"], vectors["negated_query"]),
+                "paraphrase_stored_to_query": cosine(vectors["paraphrase_stored"], vectors["paraphrase_query"]),
+            },
+            "vectors": {
+                name: list(struct.unpack(f"<{len(vector)}f", struct.pack(f"<{len(vector)}f", *vector)))
+                for name, vector in vectors.items()
+            },
+        },
+        indent=2,
+    )
+    + "\n"
+)
diff --git a/python/tests/unit/memory/recorded_nomic_relevance_embeddings.json b/python/tests/unit/memory/recorded_nomic_relevance_embeddings.json
new file mode 100644
index 000000000000..3388b0216ced
--- /dev/null
+++ b/python/tests/unit/memory/recorded_nomic_relevance_embeddings.json
@@ -0,0 +1,3101 @@
+{
+  "model": "text-embedding-nomic-embed-text-v1.5",
+  "checkpoint": "nomic-ai/nomic-embed-text-v1.5",
+  "endpoint": "http://127.0.0.1:1234/v1/embeddings",
+  "generator": "python tests/unit/memory/generate_recorded_nomic_relevance_embeddings.py",
+  "generated_at_utc": "2026-08-26T00:03:26.884097+00:00",
+  "dimension": 768,
+  "float_format": "IEEE-754 float32",
+  "texts": {
+    "stored": "Before deleting user data, require explicit confirmation.",
+    "negated_query": "Before deleting user data, do not require explicit confirmation.",
+    "paraphrase_stored": "Erase records only after the subject has authorized the action.",
+    "paraphrase_query": "Obtain affirmative consent from the person before erasing any of their personal information."
+  },
+  "cosine_similarity": {
+    "stored_to_negated_query": 0.9748117881955213,
+    "paraphrase_stored_to_query": 0.7302992277715454
+  },
+  "vectors": {
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+}
diff --git a/python/tests/unit/memory/recorded_nomic_relevance_embeddings.py b/python/tests/unit/memory/recorded_nomic_relevance_embeddings.py
new file mode 100644
index 000000000000..e86e236ae942
--- /dev/null
+++ b/python/tests/unit/memory/recorded_nomic_relevance_embeddings.py
@@ -0,0 +1,8 @@
+# Copyright (c) Microsoft. All rights reserved.
+"""Offline vectors generated by generate_recorded_nomic_relevance_embeddings.py."""
+
+import json
+from pathlib import Path
+
+FIXTURE = json.loads(Path(__file__).with_suffix(".json").read_text())
+RECORDED_EMBEDDINGS = {text: FIXTURE["vectors"][name] for name, text in FIXTURE["texts"].items()}
diff --git a/python/tests/unit/memory/test_text_memory_plugin_relevance.py b/python/tests/unit/memory/test_text_memory_plugin_relevance.py
new file mode 100644
index 000000000000..6fb4555e66fd
--- /dev/null
+++ b/python/tests/unit/memory/test_text_memory_plugin_relevance.py
@@ -0,0 +1,53 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+import numpy as np
+
+from semantic_kernel.connectors.ai.embedding_generator_base import EmbeddingGeneratorBase
+from semantic_kernel.core_plugins.text_memory_plugin import TextMemoryPlugin
+from semantic_kernel.memory.semantic_text_memory import SemanticTextMemory
+from semantic_kernel.memory.volatile_memory_store import VolatileMemoryStore
+from tests.unit.memory.recorded_nomic_relevance_embeddings import RECORDED_EMBEDDINGS
+
+STORED_TEXT = "Before deleting user data, require explicit confirmation."
+NEGATED_QUERY = "Before deleting user data, do not require explicit confirmation."
+PARAPHRASE_STORED_TEXT = "Erase records only after the subject has authorized the action."
+PARAPHRASE_QUERY = "Obtain affirmative consent from the person before erasing any of their personal information."
+
+
+class RecordedEmbeddingGenerator(EmbeddingGeneratorBase):
+    async def generate_embeddings(self, texts, settings=None, **kwargs):
+        return np.array([RECORDED_EMBEDDINGS[text] for text in texts], dtype=np.float32)
+
+
+async def test_recall_default_threshold_returns_negated_instruction():
+    memory = SemanticTextMemory(VolatileMemoryStore(), RecordedEmbeddingGenerator(ai_model_id="recorded-nomic"))
+    plugin = TextMemoryPlugin(memory)
+
+    await plugin.save(STORED_TEXT, "stored")
+
+    result = await plugin.recall(NEGATED_QUERY)
+
+    assert result == STORED_TEXT
+
+
+async def test_recall_default_threshold_filters_equivalent_paraphrase():
+    memory = SemanticTextMemory(VolatileMemoryStore(), RecordedEmbeddingGenerator(ai_model_id="recorded-nomic"))
+    plugin = TextMemoryPlugin(memory)
+
+    await plugin.save(PARAPHRASE_STORED_TEXT, "stored")
+
+    result = await plugin.recall(PARAPHRASE_QUERY)
+
+    assert result == ""
+
+
+def test_recorded_vectors_cross_the_default_relevance_threshold():
+    stored = np.array(RECORDED_EMBEDDINGS[STORED_TEXT])
+    negated = np.array(RECORDED_EMBEDDINGS[NEGATED_QUERY])
+    paraphrase_stored = np.array(RECORDED_EMBEDDINGS[PARAPHRASE_STORED_TEXT])
+    paraphrase_query = np.array(RECORDED_EMBEDDINGS[PARAPHRASE_QUERY])
+
+    assert np.dot(stored, negated) / (np.linalg.norm(stored) * np.linalg.norm(negated)) > 0.75
+    assert np.dot(paraphrase_stored, paraphrase_query) / (
+        np.linalg.norm(paraphrase_stored) * np.linalg.norm(paraphrase_query)
+    ) < 0.75