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docs(readme): update LongMemEval benchmark to 94.8 and add Temporal Reasoning (#5131)

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Agam Pandey
2026-05-13 14:31:15 +05:30
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@@ -47,7 +47,7 @@
| Benchmark | Old | New | Tokens | Latency p50 |
| --- | --- | --- | --- | --- |
| **LoCoMo** | 71.4 | **91.6** | 7.0K | 0.88s |
| **LongMemEval** | 67.8 | **93.4** | 6.8K | 1.09s |
| **LongMemEval** | 67.8 | **94.8** | 6.8K | 1.09s |
| **BEAM (1M)** | — | **64.1** | 6.7K | 1.00s |
| **BEAM (10M)** | — | **48.6** | 6.9K | 1.05s |
@@ -58,12 +58,13 @@ All benchmarks run on the same production-representative model stack. Single-pas
- **Agent-generated facts are first-class** -- when an agent confirms an action, that information is now stored with equal weight.
- **Entity linking** -- entities are extracted, embedded, and linked across memories for retrieval boosting.
- **Multi-signal retrieval** -- semantic, BM25 keyword, and entity matching scored in parallel and fused.
- **Temporal Reasoning** -- time-aware retrieval that ranks the right dated instance for queries about current state, past events, and upcoming plans.
See the [migration guide](https://docs.mem0.ai/migration/oss-v2-to-v3) for upgrade instructions. The [evaluation framework](https://github.com/mem0ai/memory-benchmarks) is open-sourced so anyone can reproduce the numbers.
## Research Highlights
- **91.6 on LoCoMo** -- +20 points over the previous algorithm
- **93.4 on LongMemEval** -- +26 points, with +53.6 on assistant memory recall
- **94.8 on LongMemEval** -- +27 points, with +53.6 on assistant memory recall
- **64.1 on BEAM (1M)** -- production-scale memory evaluation at 1M tokens
- [Read the full paper](https://mem0.ai/research)