MRCR
MRCR (Multi-Round Coreference Resolution) is a synthetic long-context reasoning task where models must navigate long conversations to reproduce specific model outputs. It tests the ability to distinguish between similar requests and reason about ordering while maintaining attention across extended contexts.
Gemini 2.5 Pro from Google currently leads the MRCR leaderboard with a score of 0.930 across 7 evaluated AI models.
What MRCR measures
MRCR is a text benchmark that evaluates large language models on general, long context, and reasoning tasks. LLM Stats tracks 7 models on this benchmark, with a maximum possible score of 1. Current average across reported models is 0.6, with the leader reaching 0.9.
Compare leaders on the best AI for general, best AI for long context and best AI for reasoning leaderboards.
Publication
- Paper
- Michelangelo: Long Context Evaluations Beyond Haystacks via Latent Structure Queries
- Authors
- Kiran Vodrahalli, Santiago Ontanon, Nilesh Tripuraneni, Kelvin Xu, and 20 others
- Published
- arXiv
- 2409.12640
Abstract
We introduce Michelangelo: a minimal, synthetic, and unleaked long-context reasoning evaluation for large language models which is also easy to automatically score. This evaluation is derived via a novel, unifying framework for evaluations over arbitrarily long contexts which measure the model's ability to do more than retrieve a single piece of information from its context. The central idea of the Latent Structure Queries framework (LSQ) is to construct tasks which require a model to ``chisel away'' the irrelevant information in the context, revealing a latent structure in the context. To verify a model's understanding of this latent structure, we query the model for details of the structure. Using LSQ, we produce three diagnostic long-context evaluations across code and natural-language domains intended to provide a stronger signal of long-context language model capabilities. We perform evaluations on several state-of-the-art models and demonstrate both that a) the proposed evaluations are high-signal and b) that there is significant room for improvement in synthesizing long-context information.
Gemini 2.5 Pro leads with 93.0%, followed by Gemini 1.5 Pro at 82.6% and Gemini 1.5 Flash at 71.9%.
Progress Over Time
Interactive timeline showing model performance evolution on MRCR
MRCR Leaderboard
| Context | Cost | License | ||||
|---|---|---|---|---|---|---|
| 1 | Google | — | 1.0M | $1.25 / $10.00 | ||
| 2 | Google | — | — | — | ||
| 3 | Google | — | — | — | ||
| 4 | Google | — | — | — | ||
| 5 | Google | 8B | — | — | ||
| 6 |  Xiaomi | 309B | — | — | ||
| 7 | Google | — | 1.0M | $0.30 / $2.50 |
FAQ
Common questions about MRCR.
Sub-benchmarks
MRCR 128K (2-needle)
MRCR (Multi-Round Coreference Resolution) at 128K context length with 2 needles. Models must navigate long conversations to reproduce specific model outputs, testing attention and reasoning across 128K-token contexts with 2 items to retrieve.
MRCR 128K (4-needle)
MRCR (Multi-Round Coreference Resolution) at 128K context length with 4 needles. Models must navigate long conversations to reproduce specific model outputs, testing attention and reasoning across 128K-token contexts with 4 items to retrieve.
MRCR 128K (8-needle)
MRCR (Multi-Round Coreference Resolution) at 128K context length with 8 needles. Models must navigate long conversations to reproduce specific model outputs, testing attention and reasoning across 128K-token contexts with 8 items to retrieve.
MRCR 64K (2-needle)
MRCR (Multi-Round Coreference Resolution) at 64K context length with 2 needles. Models must navigate long conversations to reproduce specific model outputs, testing attention and reasoning across 64K-token contexts with 2 items to retrieve.
MRCR 64K (4-needle)
MRCR (Multi-Round Coreference Resolution) at 64K context length with 4 needles. Models must navigate long conversations to reproduce specific model outputs, testing attention and reasoning across 64K-token contexts with 4 items to retrieve.
MRCR 64K (8-needle)
MRCR (Multi-Round Coreference Resolution) at 64K context length with 8 needles. Models must navigate long conversations to reproduce specific model outputs, testing attention and reasoning across 64K-token contexts with 8 items to retrieve.
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