EvoRouter Training Ablation - In-Domain Accuracy: leaderboard
EvoRouter v1 compares multi-agent routing configurations with a fixed resource pool. It uses 1300 in-domain test questions across 5 domains, plus 175 LiveCodeBench and 200 BBH-LD out-of-domain questions. Results average 3 runs; the mean correct count is rounded before denominator normalization. Pool models use temperature 1 and an output budget of 1024 tokens (4096 for code). Nonvision models receive an explicit image-invisibility placeholder on multimodal tasks. Router 4B and 8B are trained Qwen3 variants; LLaMa 8B is a trained Llama3.1 variant. Systems without model selection use GLM-5. Distinct trained-model, router, and harness identities are preserved. Full EvoRouter 4B row duplicates main table and is omitted from this additional population. Metric: Accuracy (%).
Metric: Accuracy (%). Source: arxiv.org. Saturation forecast: Rough model projection: around 2026. 4 models tracked.
Top models
| # | Model | Score | Overall rank |
|---|---|---|---|
| 1 | EvoRouter-4B w/o Insights | 64.54 | |
| 2 | EvoRouter-4B w/o OPD | 63.23 | |
| 3 | EvoRouter-4B w/o RL+OPD | 59.77 | |
| 4 | EvoRouter-4B w/o SFT+RL+OPD | 53.23 |
Interactive version: theaggregate.ai/benchmark?slug=evorouter-training-ablation-in-domain-accuracy · How It Works · Data refreshed daily, snapshot 2026-10-28.