We present our submissions to the WSLP 2026 Isolated Sign Language Recognition shared task. The task requires predicting the correct English gloss from a short Indian Sign Language video.

Our approach first establishes a strong primary prediction using vision features and identifies uncertain cases using model disagreement and prediction confidence. Rather than allowing all available experts to influence every prediction, we selectively invoke auxiliary models for difficult cases and incorporate their evidence through constrained late-fusion strategies. This enables complementary appearance, pose, and vision-language information to be used when the primary prediction is uncertain while preserving reliable predictions for confident cases. We introduce a disagreement-gated Qwen2.5-VL model as an auxiliary decision-making module and further incorporate complementary appearance and pose experts through several constrained fusion strategies.

The final system combines multiple fusion variants using reciprocal rank fusion and achieves 66.16% Top-1 accuracy, 72.43% Top-5 accuracy, and 73.19% Top-10 accuracy on the blind test set