CAGE - Intervention (Chain-Text Probe) - Chain Score: leaderboard
CAGE, the Causal Abstraction Gap Evaluation (Hoang and Hasan, 2026), asks vision-language models causal questions about 500 COCO images. In its Chain-Text Probe the model first writes a causal chain in arrow notation and then a textual answer; this board scores the causal chain in arrow notation for the level-2 intervention questions, from 0 to 10 as the mean of GPT-4o and Claude 3.5 Sonnet judges, with blank responses scored 0; higher is better.
Metric: Judge score (0-10) for the causal chain in arrow notation, mean of GPT-4o and Claude 3.5 Sonnet ratings against GPT-4o-generated reference answers and chains; Chain-Text Probe, level-2 intervention questions (what happens if something in the image changes), where the chain needs at least one causal link; 500 held-out COCO images, three questions each; blank responses score 0. Source: arxiv.org. Saturation forecast: Rough model projection: around 2026. 8 models tracked.
Top models
| # | Model | Score | Overall rank |
|---|---|---|---|
| 1 | LLaVA-NeXT-13B | 7.83 | |
| 2 | InternVL2-76B | 3.9 | #1480 |
| 3 | Qwen-VL-Chat | 3.77 | #8018 |
| 4 | MiniGPT-4-Vicuna-13B | 2.09 | |
| 5 | LLaVA-CoT | 0.97 | #3074 |
| 6 | LLaVA-RLHF-13B | 0.81 | |
| 7 | CogVLM2-19B | 0.54 | |
| 8 | mPLUG-Owl2 | 0.42 | #8280 |
Interactive version: theaggregate.ai/benchmark?slug=cage-intervention-chain-text-probe-chain-score · How It Works · Data refreshed daily, snapshot 2026-11-13.