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SOTA2 Research · benchmarks
Compare state-of-the-art methods across the tasks and datasets used to measure AI progress.
| Task | Dataset | Domain | Trend | Results | Last Update |
|---|---|---|---|---|---|
| Conformal Prediction | VentricularVolume (OUT) | 35 | Jul 7, 2026 | ||
| Salient Object Detection | ORSI-4199 2022 (test) | 35 | Jul 7, 2026 | ||
| Lexical Retrieval | HotpotQA | 35 | Jul 3, 2026 | ||
| Lexical Retrieval | MS MARCO | 35 | Jul 3, 2026 | ||
| Lexical Retrieval | Natural Questions | 35 | Jul 3, 2026 | ||
| Generalization Evaluation |
| AM-Thinking Distilled-math&code v1 (Unseen) |
| 35 |
| Jul 2, 2026 |
| Language Model Evaluation | Tulu-3 (dev) | 35 | Jul 2, 2026 |
|---|
| Robust Pose Graph Optimization | 2D SLAM datasets (Intel, Csail, FR079, FRH) (outlier ratio sets) | 35 | Jun 30, 2026 |
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| Provenance Tracing | Infinity | 35 | Jun 30, 2026 |
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| Provenance Tracing | VAR | 35 | Jun 30, 2026 |
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| Provenance Tracing | Taming | 35 | Jun 30, 2026 |
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| Provenance Tracing | LlamaGen | 35 | Jun 30, 2026 |
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| Image Classification | MURA Fracture | 35 | Jun 30, 2026 |
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| QDA classification | Neural data | 35 | Jun 29, 2026 |
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| KNN classification | Zand-Kohn | 35 | Jun 29, 2026 |
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| Multiframe Infrared Small Target Detection | NUDT-MIRSDT Hard subset | 35 | Jun 29, 2026 |
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| Multiframe Infrared Small Target Detection | NUDT-MIRSDT (All) | 35 | Jun 29, 2026 |
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| Semantic Uncertainty Stability Prediction | BBEH | 35 | Jun 29, 2026 |
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| Causality | BBEH | 35 | Jun 29, 2026 |
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| Minimality Evaluation | BBEH (test) | 35 | Jun 29, 2026 |
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| Same-task discrimination accuracy | BBEH | 35 | Jun 29, 2026 |
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| Text-to-Image Generation | FLUX.1 DrawBench dev 1.0 (test) | 35 | Jun 26, 2026 |
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| Commonsense Reasoning | Commonsense Reasoning Suite (PIQA, ARC-e, ARC-c, HellaSwag, WinoGrande) | 35 | Jun 30, 2026 |
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| AUC of the ET inspector | US Income CA14 | 35 | Jun 26, 2026 |
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| Object Detection | CARPK (test) | 35 | Jun 25, 2026 |
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