MR-NIAH (Multi-Round Needles-In-A-Haystack)
Models must repeat one earlier answer from a long multi-turn dialogue history of 2K to 1M tokens. Scored by adjusted recall of key parts.
MiniMax built it as an open alternative to MRCR, in English and Chinese.
- Made by
- MiniMax
- Released
- January 2025
- Kind
- Finding facts in long inputs
- Level
- Everyday
- Scored
- Graded by a model
- Answers
- Open-ended
- Tasks
- All public
- Last in a system card
- January 2025
- Last model added
- January 2025
1
System card reports it, from 1 lab
No scores here yet
No lab, leaderboard or Epoch AI run has reported a score for MR-NIAH (Multi-Round Needles-In-A-Haystack) yet.
Labs report it only in system cards
We read the headline results of 522 launch posts from 52 labs, and none of them list this benchmark. Some system cards do, below.
One system card reports it
Besides launch posts, we read the system cards, model cards and technical reports labs publish with a release. They report far more, including safety and frontier-risk tests.
| Date | Document | Score | As the document writes it | Section |
|---|---|---|---|---|
| Jan 14, 2025 |  MiniMax-Text-01 technical report | MR-NIAH English and Chinese, adjusted recall curves without printed values (Figure 15) | Capabilities |
More in Long context
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- OpenAI MRCR v2 In use Models read a long synthetic chat that hides 2, 4 or 8 identical writing requests and must reproduce the response to the i-th one. Scored by mean string match ratio per context bin.
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- AA-LCR 1.1 In use Models answer 100 questions that need reasoning across sets of real documents of about 100K tokens. An LLM equality checker with a fixed system prompt compares each answer with the key.
Step 5 Preview 88.3%