Multi-Source Evidence Fusion for Audio Question Answering

This project is a competition-specific implementation for the Interspeech 2026 Audio Reasoning Challenge. It utilizes a 'fusion' approach, which is a standard academic technique for boosting performance by combining multiple model outputs. While the focus on logical soundness and reasoning chains is academically relevant, the project lacks technical defensibility. With 0 stars and 2 forks, it has no community traction or data gravity. The strategy of ensembling LALMs (like Qwen-Audio or SALMONN) is easily reproducible and likely to be rendered obsolete by native audio-reasoning improvements in frontier models like GPT-4o and Gemini 1.5 Pro. These platform-level models are moving toward 'native' multi-modality where the audio is processed directly rather than through an ensemble of discrete agents, making this architectural pattern a temporary bridge rather than a long-term solution. The displacement horizon is short (under 6 months) as new model releases typically absorb the reasoning gains previously achieved through external fusion logic.