Clue Matters: Empower Video Reasoning with Brain-Inspired Latent Clue Learning

Multi-modal Large Language Models (MLLMs) have signifi-cantly advanced video reasoning, yet Video Question Answering (VideoQA)remains challenging due to its demand for temporal causal reasoningand evidence-grounded answer generation. Prevailing end-to-end MLLMframeworks lack explicit structured reasoning between visual percep-tion and answer derivation, causing severe hallucinations and poor in-terpretability. Existing methods also fail to address three core gaps:faithful visual clue extraction, utility-aware clue filtering, and end-to-end clue-answer alignment. Inspired by hierarchical human visual cogni-tion, we propose ClueNet, a clue-aware video reasoning framework with atwo-stage supervised fine-tuning paradigm without extensive base modelmodifications. Decoupled supervision aligns clue extraction and chain-based reasoning, while inference supervision with an adaptive clue filterrefines high-order reasoning, alongside lightweight modules for efficientinference. Experiments on NExT-QA, STAR, and MVBench show thatClueNet outperforms state-of-the-art methods by ≥ 1.1%, with strongbenchmark generalization, hallucination mitigation, and improved infer-ence efficiency. This work bridges the perception-to-generation gap inMLLM video understanding, providing an interpretable, faithful reason-ing paradigm for real-world VideoQA applications.