让 AI 在漫长对话中,
依然记得真正重要的事。
What matters should survive
the longest conversations.
MemoryBear 在两个长期记忆基准上接受全量评测:标准化长期记忆能力与复杂多会话压力测试。
MemoryBear was evaluated end-to-end on two long-term memory benchmarks: standardized abilities and complex multi-session stress tests.
双重验证长期记忆能力
Two benchmarks. One durable memory layer.
LongMemEval 检验标准能力,LoCoMo 检验更长、更复杂、更接近持续交互的记忆表现。
LongMemEval tests standardized abilities. LoCoMo tests longer, noisier and more complex conversational memory.
公开结果对比
Public result comparison
MemoryBear 结果来自本地全量评测;竞品数字为公开资料快照,仅作水位参考。
MemoryBear uses our full local run; competitor figures are public reference snapshots only.
LoCoMo overall comparison
* 除 MIRIX 外,所有回答均使用短答案提示词生成。
* All responses are generated using a short-answer prompt except MIRIX.
LongMemEval category comparison
* 不同底座模型、回答模型、Judge、检索预算与数据版本会显著影响分数。本表不代表统一环境下的严格复现排名。
* Base models, answerers, judges, retrieval budgets and dataset versions materially affect scores. This is not a controlled same-environment ranking.
先公开方法,再讨论领先
Disclose the method before claiming the lead.
我们把可复核性放在榜单之前。以下是本轮 MemoryBear 结果所对应的评测边界。
Reproducibility comes before the leaderboard. These are the evaluation boundaries behind the results.
看看它是怎么想的
See how it thinks
每个回答背后,MemoryBear 都会召回相关记忆、整理证据,再作答。下面是真实样例。
Behind every answer, MemoryBear recalls relevant memories, assembles the evidence, then responds. These are real traces.
召回的记忆(按相关度)
Recalled memories (by relevance)
召回的记忆(按相关度)
Recalled memories (by relevance)
我们也公开它答错的地方
Where it gets it wrong
透明地展示典型失败样例,是可信 benchmark 的一部分。
Publishing typical failure cases is part of a trustworthy benchmark.
召回的记忆(按相关度)
Recalled memories (by relevance)
召回的记忆(按相关度)
Recalled memories (by relevance)
访问 MemoryBear
Explore MemoryBear
通过官方渠道了解产品、查看项目源码,并获取完整的评测数据与结果。
Learn more about the product, browse the source code, and access the complete evaluation data and results through our official channels.
Hugging Face
查看完整评测数据、逐题结果、检索记忆与配置文件。
Browse complete evaluation data, per-question results, retrieved memories and configuration files.
GitHub
查看 MemoryBear 开源项目与最新代码。
Browse the MemoryBear open-source project and latest code.
RedBear AI
访问红熊AI官网,了解产品与团队信息。
Visit the RedBear AI website to learn about the product and team.
如何阅读这些数字
How to read the numbers
- LongMemEval 与 LoCoMo 的规模、难度和上下文长度不同,两项总分不应相互比较。
- 竞品结果来自公开页面或历史快照,不代表统一环境下的严格复现排名。
- 自动 Judge 存在边界误判可能;正式技术报告应同时提供人工抽检与逐题证据。
- LongMemEval and LoCoMo differ in scale, difficulty and context length; their headline scores are not directly comparable.
- Competitor figures come from public pages or historical snapshots, not controlled reruns.
- Automated judges can misclassify edge cases; formal reports should include human audits and per-question evidence.