RPCBench - Domain - MovieLens-1M - Conditional Localization: leaderboard
RPCBench tests proactive premise critique in recommendation requests. This series measures conditional localization on the MovieLens-1M subset (462 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: Estimated already saturated. 11 models tracked.
Top models
| # | Model | Score | Overall rank |
|---|---|---|---|
| 1 | Qwen 3.5 Plus | 94.98 | #136 |
| 2 | Qwen 3.5 122B A10B | 94.87 | #186 |
| 3 | DeepSeek V4 Pro | 93.68 | #113 |
| 4 | Qwen 3.5 397B A17B | 93.38 | #155 |
| 5 | GPT-5.5 | 91.96 | #34 |
| 6 | Qwen 3.5 35B A3B | 91.28 | #270 |
| 7 | DeepSeek V4 Flash | 89.18 | #156 |
| 8 | Gemini 3.1 Pro (Preview) | 88.82 | #66 |
| 9 | Claude Sonnet 4.6 | 88.59 | #97 |
| 10 | Llama 3.1 70B Instruct | 44.87 | #570 |
| 11 | Llama 3.1 8B Instruct | 42.31 | #1021 |
No result here: #3 Claude Opus 5.5, #5 GPT-6 Astra, #6 GPT-6.1 Sol.
Interactive version: theaggregate.ai/benchmark?slug=rpcbench-domain-movielens-1m-conditional-localization · How It Works · Data refreshed daily, snapshot 2026-11-07.