eval result

README.md

@@ -54,22 +54,20 @@
 
 ## 📈 Performance Benchmark
 
-MemOS demonstrates significant improvements over baseline memory solutions in multiple reasoning tasks.
-
-| Model       | Avg. Score | Multi-Hop | Open Domain | Single-Hop | Temporal Reasoning |
-|-------------|------------|-----------|-------------|------------|---------------------|
-| **OpenAI**  | 0.5275     | 0.6028    | 0.3299      | 0.6183     | 0.2825              |
-| **MemOS**   | **0.7331** | **0.6430** | **0.5521**   | **0.7844** | **0.7321**          |
-| **Improvement** | **+38.98%** | **+6.67%** | **+67.35%** | **+26.86%** | **+159.15%**       |
-
-> 💡 **Temporal reasoning accuracy improved by 159% compared to the OpenAI baseline.**
-
-### Details of End-to-End Evaluation on LOCOMO
-
-> [!NOTE]
-> Comparison of LLM Judge Scores across five major tasks in the LOCOMO benchmark. Each bar shows the mean evaluation score judged by LLMs for a given method-task pair, with standard deviation as error bars. MemOS-0630 consistently outperforms baseline methods (LangMem, Zep, OpenAI, Mem0) across all task types, especially in multi-hop and temporal reasoning scenarios.
-
-<img src="https://statics.memtensor.com.cn/memos/score_all_end2end.jpg" alt="END2END SCORE">
+MemOS demonstrates significant improvements over baseline memory solutions in multiple memory tasks,
+showcasing its capabilities in **information extraction**, **temporal and cross-session reasoning**, and **personalized preference responses**.
+
+| Model           | LOCOMO      | LongMemEval | PrefEval-10 | PersonaMem  | 
+|-----------------|-------------|-------------|-------------|-------------|
+| **GPT-4o-mini** | 52.75       | 55.4        | 2.8         | 43.46       |
+| **MemOS**       | **75.80**   | **77.80**   | **71.90**   | **61.17**   |
+| **Improvement** | **+43.70%** | **+40.43%** | **+2568%**  | **+40.75%** |
+
+### Detailed Evaluation Results
+- We use gpt-4o-mini as the processing and judging LLM and bge-m3 as embedding model in MemOS evaluation.
+- The evaluation was conducted under conditions that align various settings as closely as possible. Reproduce the results with our scripts at [`evaluation`](./evaluation).
+- Check the full search and response details at huggingface https://huggingface.co/datasets/MemTensor/MemOS_eval_result.
+> 💡 **MemOS outperforms all other methods (Mem0, Zep, Memobase, SuperMemory et al.) across all benchmarks!**
 
 ## ✨ Key Features
 

evaluation/scripts/locomo/locomo_ingestion.py

@@ -88,7 +88,7 @@ def ingest_session(client, session, frame, version, metadata):
     return elapsed_time
 
 
-def process_user(conv_idx, frame, locomo_df, version):
+def process_user(conv_idx, frame, locomo_df, version, success_records, f):
     conversation = locomo_df["conversation"].iloc[conv_idx]
     max_session_count = 35
     start_time = time.time()
@@ -149,11 +149,15 @@ def process_user(conv_idx, frame, locomo_df, version):
 
     print(f"Processing {valid_sessions} sessions for user {conv_idx}")
 
-    for session, metadata in sessions_to_process:
-        session_time = ingest_session(client, session, frame, version, metadata)
-        total_session_time += session_time
-        print(f"User {conv_idx}, {metadata['session_key']} processed in {session_time} seconds")
-
+    for session_idx, (session, metadata) in enumerate(sessions_to_process):
+        if f"{conv_idx}_{session_idx}" not in success_records:
+            session_time = ingest_session(client, session, frame, version, metadata)
+            total_session_time += session_time
+            print(f"User {conv_idx}, {metadata['session_key']} processed in {session_time} seconds")
+            f.write(f"{conv_idx}_{session_idx}\n")
+            f.flush()
+        else:
+            print(f"Session {conv_idx}_{session_idx} already ingested")
     end_time = time.time()
     elapsed_time = round(end_time - start_time, 2)
     print(f"User {conv_idx} processed successfully in {elapsed_time} seconds")
@@ -170,9 +174,20 @@ def main(frame, version="default", num_workers=4):
     print(
         f"Starting processing for {num_users} users in serial mode, each user using {num_workers} workers for sessions..."
     )
-    with concurrent.futures.ThreadPoolExecutor(max_workers=num_workers) as executor:
+    os.makedirs(f"results/locomo/{frame}-{version}/", exist_ok=True)
+    success_records = []
+    record_file = f"results/locomo/{frame}-{version}/success_records.txt"
+    if os.path.exists(record_file):
+        with open(record_file) as f:
+            for i in f.readlines():
+                success_records.append(i.strip())
+
+    with (
+        concurrent.futures.ThreadPoolExecutor(max_workers=num_workers) as executor,
+        open(record_file, "a+") as f,
+    ):
         futures = [
-            executor.submit(process_user, user_id, frame, locomo_df, version)
+            executor.submit(process_user, user_id, frame, locomo_df, version, success_records, f)
             for user_id in range(num_users)
         ]
         for future in concurrent.futures.as_completed(futures):
@@ -216,7 +231,7 @@ def main(frame, version="default", num_workers=4):
         help="Version identifier for saving results (e.g., 1010)",
     )
     parser.add_argument(
-        "--workers", type=int, default=3, help="Number of parallel workers to process users"
+        "--workers", type=int, default=10, help="Number of parallel workers to process users"
     )
     args = parser.parse_args()
     lib = args.lib