Datasets:
license: apache-2.0
pretty_name: MemoryBear Evaluation Results
language:
- en
tags:
- evaluation
- memory
- longmemeval
- locomo
- llm
task_categories:
- question-answering
size_categories:
- n<1K
MemoryBear Evaluation Results
This dataset repository contains the evaluation results for MemoryBear, a next-generation AI memory system developed by RedBear AI.
MemoryBear's core breakthrough lies in moving beyond the limitations of traditional "static knowledge storage". Inspired by the cognitive mechanisms of biological brains, MemoryBear builds an intelligent knowledge-processing framework that spans the full lifecycle of perception → extraction → association → forgetting.
Unlike traditional memory tools that treat knowledge as static data to be retrieved, MemoryBear emulates the hippocampus's memory encoding, the neocortex's knowledge consolidation, and synaptic pruning-based forgetting — enabling knowledge to dynamically evolve with life-like properties. This shifts the relationship between AI and users from passive lookup to proactive cognitive assistance.
Benchmarks
We evaluate on two widely used long-term conversational memory benchmarks:
- LongMemEval (xiaowu0162/LongMemEval) — 500 questions probing five core long-term memory abilities of chat assistants (information extraction, multi-session reasoning, temporal reasoning, knowledge updates, and abstention) over long user–assistant interaction histories.
- LoCoMo (snap-research/locomo) — 1,986 questions over 10 very long multi-session dialogues, covering single-hop, multi-hop, temporal-reasoning, open-domain, and adversarial questions.
Repository Structure
The evaluation artifacts are organized by benchmark (lme/ for LongMemEval, locomo/ for LoCoMo), then by system. Each run directory contains the same five artifacts:
| File | Description |
|---|---|
*_metrics.json |
Aggregated metrics — accuracy, average context tokens, latency, and lexical scores (F1 / ROUGE / BLEU / METEOR), reported overall, by question category, and per question. |
*_hypotheses.json |
The answer generated by the system for each question, alongside the question, golden answer, and answer evidences. |
*_judged.json |
Per-question LLM-judge verdicts (correct / incorrect) together with the associated lexical metrics. |
*_retrieved_memories.json |
The memories retrieved by the system for each question, useful for inspecting retrieval quality. |
*_results.xlsx |
A spreadsheet summary of the run for convenient browsing. |
Baseline Reproduction
The baseline results were reproduced by us using the official reproduction repos.
For comparability, the baselines were run under the same settings as MemoryBear: retrieval returns the top 10 memories by default, and both hypothesis (answer) generation and LLM judging use qwen3.7-plus — identical to our own runs.
Evaluation Results
LongMemEval
Evaluated on the full 500-question LongMemEval set. Accuracy is determined by an LLM judge.
| System | single-session-preference | single-session-assistant | temporal-reasoning | multi-session | knowledge-update | single-session-user | overall |
|---|---|---|---|---|---|---|---|
| MemoryBear | 100% | 85.71% | 93.98% | 93.98% | 98.72% | 100% | 95.0% |
| MemOS | 86.67% | 92.86% | 81.95% | 80.45% | 94.87% | 98.57% | 87.4% |
| Memobase | 78.40% | 22.51% | 72.13% | 63.56% | 87.05% | 91.00% | 69.65% |
| Mem0 | 88.20% | 25.98% | 68.57% | 59.99% | 64.67% | 81.20% | 63.86% |
| Zep | 52.23% | 72.75% | 51.40% | 45.03% | 72.17% | 91.04% | 61.51% |
| Supermemory | 88.20% | 57.15% | 42.14% | 50.00% | 53.47% | 84.00% | 56.31% |
| MIRIX | 52.26% | 61.72% | 24.28% | 28.57% | 50.98% | 71.39% | 42.02% |
| MemU | 75.14% | 19.05% | 16.43% | 40.00% | 39.79% | 65.80% | 37.07% |
LoCoMo
Evaluated on the full LoCoMo benchmark (1,986 questions across 10 conversations). Accuracy is determined by an LLM judge. The rest of the system baselines cover the 1,540 non-adversarial questions, so their adversarial cells are empty and their overall scores are computed over the remaining four categories.
| System | single-hop | multi-hop | temporal-reasoning | open-domain | adversarial | overall | overall F1 |
|---|---|---|---|---|---|---|---|
| MemoryBear | 92.27% | 90.78% | 91.59% | 73.96% | 94.39% | 91.54% | 67.49 |
| MemOS | 89.89% | 77.30% | 81.93% | 63.54% | – | 84.29% | 38.44 |
| Mem0 | 80.98% | 84.40% | 88.16% | 73.96% | – | 82.66% | 48.74 |
| Memobase | 71.66% | 61.42% | 77.14% | 51.53% | – | 69.68% | 50.18 |
| MIRIX | 66.86% | 51.55% | 65.11% | 45.47% | – | 62.29% | 28.10 |
| Zep | 64.91% | 49.51% | 52.08% | 32.33% | – | 57.39% | 41.23 |
| MemU | 65.01% | 59.96% | 25.75% | 48.50% | – | 54.87% | 35.15 |
| Supermemory | 65.95% | 48.56% | 30.18% | 41.39% | – | 53.72% | 34.87 |
* The original LoCoMo dataset contains mislabeled golden answers. We corrected these mislabels, and all results above are reported on the corrected dataset.