MRCR v2
MRCR v2 (Multi-Round Coreference Resolution version 2) is an enhanced version of the synthetic long-context reasoning task. It extends the original MRCR framework with improved evaluation criteria and additional complexity for testing models' ability to maintain attention and reasoning across extended contexts.
Gemma 4 31B from Google currently leads the MRCR v2 leaderboard with a score of 0.664 across 7 evaluated AI models.
What MRCR v2 measures
MRCR v2 is a text benchmark that evaluates large language models on reasoning, general, and long context tasks. LLM Stats tracks 7 models on this benchmark, with a maximum possible score of 1. Current average across reported models is 0.4, with the leader reaching 0.7.
Compare leaders on the best AI for reasoning, best AI for general and best AI for long context 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.
Gemma 4 31B leads with 66.4%, followed by Gemma 4 26B-A4B at 44.1% and Gemma 4 12B at 43.4%.
Progress Over Time
Interactive timeline showing model performance evolution on MRCR v2
MRCR v2 Leaderboard
| Context | Cost | License | ||||
|---|---|---|---|---|---|---|
| 1 | Google | 31B | 262K | $0.14 / $0.40 | ||
| 2 | Google | 25B | 262K | $0.13 / $0.40 | ||
| 3 | Google | 12B | — | — | ||
| 4 | Google | 25B | — | — | ||
| 5 | Google | 8B | — | — | ||
| 6 | Google | 5B | — | — | ||
| 7 | Google | — | — | — |
FAQ
Common questions about MRCR v2.
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