DGP - LLaVA Wild: leaderboard
Dynamic visual-layer routing systems on original multimodal evaluation tables. DGP v1 trains CLIP ViT-L/14-336 plus Vicuna-7B or Vicuna-13B on LLaVA-558K pretraining and LLaVA-mixed-665K supervised data, one epoch per stage. The language model and DGP modules train jointly while the vision encoder stays frozen. Native scores cover GQA, ScienceQA Image, VQAv2, VizWiz, SEED All/Image, LLaVA-Wild, MM-Vet, MMBench English/Chinese and POPE. Each DGP row is a separately trained system, never bare Vicuna. Cited historical baseline rows remain source evidence and are not emitted again. Question access: Primary result reviewed; exact scored release availability not confirmed.
Metric: Native benchmark score (%). Source: arxiv.org. Saturation forecast: Rough model projection: around 2026. 2 models tracked.
Top models
| # | Model | Score | Overall rank |
|---|---|---|---|
| 1 | DGP Vicuna-13B (Benchmark Sweep Run: source=DGP; CLIP-L 336; trained LLaVA 558K + 665K) | 70 | |
| 2 | DGP Vicuna-7B (Benchmark Sweep Run: source=DGP; CLIP-L 336; trained LLaVA 558K + 665K) | 66.5 |
Interactive version: theaggregate.ai/benchmark?slug=dgp-llava-wild · How It Works · Data refreshed daily, snapshot 2026-11-06.