RPCBench - Error Group - Underspecified Premise - Conditional Localization: leaderboard
RPCBench tests proactive premise critique in recommendation requests. This series measures conditional localization on the Underspecified Premise subset (1,300 questions), using the same eleven-model, three-judge experiment as the headline paper. Published CLA fractions from the authors' per-model CSV are scaled once to Localization Accuracy (%). Higher is better; domains and nested premise-error groups are related slices of the same experiment.
Metric: Localization Accuracy (%). Source: github.com. Saturation forecast: Around 2029. 11 models tracked.
Top models
| # | Model | Score | Overall rank |
|---|---|---|---|
| 1 | Claude Sonnet 4.6 | 61.11 | #97 |
| 2 | Qwen 3.5 35B A3B | 58.5 | #270 |
| 3 | Gemini 3.1 Pro (Preview) | 58 | #66 |
| 4 | Qwen 3.5 397B A17B | 57.87 | #155 |
| 5 | DeepSeek V4 Pro | 56.35 | #113 |
| 6 | GPT-5.5 | 55.96 | #34 |
| 7 | Qwen 3.5 122B A10B | 55.9 | #186 |
| 8 | Qwen 3.5 Plus | 55 | #136 |
| 9 | DeepSeek V4 Flash | 54.17 | #156 |
| 10 | Llama 3.1 8B Instruct | 48.46 | #1021 |
| 11 | Llama 3.1 70B Instruct | 38 | #570 |
No result here: #3 Claude Opus 5.5, #5 GPT-6 Astra, #6 GPT-6.1 Sol.
Interactive version: theaggregate.ai/benchmark?slug=rpcbench-error-group-underspecified-premise-conditional-localization · How It Works · Data refreshed daily, snapshot 2026-11-07.