CASTLE2026 Team WDL Technical Report
This work addresses the challenge of integrating multi-source heterogeneous evidence—including video, ASR transcripts, auxiliary images, and spatiotemporal metadata—in long-form, multi-view first-person video question answering spanning over 600 hours. To tackle this, the authors propose an evidence-aware multimodal reasoning framework built upon the Qwen large language model. The framework employs a question-type-adaptive routing mechanism to dynamically invoke specialized prompting modules and performs iterative reasoning through keyframe sampling, ASR-based retrieval, and multimodal prompt engineering, culminating in a confidence-weighted voting ensemble. Evaluated on the CASTLE Challenge @ EgoVis 2026, the method achieves top-ranking performance. Ablation studies demonstrate that LoRA fine-tuning boosts accuracy from 0.21 to 0.50, with further improvement to 0.58 upon incorporating frame sampling, underscoring the approach’s effectiveness and novelty.