Long-Form Video Question Answering (LVQA) poses challenges beyond traditional visual question answering (VQA), which is often limited to static images or short video clips. While current vision-language models (VLMs) perform well in those settings, they struggle with answering complex queries in LVQA over long videos involving multi-step temporal reasoning and causality. Vanilla approaches, which simply sample frames uniformly and feed them to a VLM along with the question, incur significant token overhead, forcing severe downsampling of long videos. As a result, the model often misses fine-grained visual structure, subtle event transitions, or key temporal cues—ultimately leading to incorrect answers. To address these limitations, recent works have explored query-adaptive frame sampling, hierarchical keyframe selection, and agent-based iterative querying. However, these methods remain fundamentally heuristic: they lack explicit temporal representations and cannot enforce or verify logical event relationships (e.g., "before X," "after Y"). As a result, there are no formal guarantees that the sampled context actually encodes the compositional or causal logic demanded by the question. To address these foundational gaps, we introduce NeuS-QA, a training-free, plug-and-play neuro-symbolic pipeline for LVQA. NeuS-QA translates a natural language question into a formal temporal logic expression, constructs a video automaton from frame-level semantic propositions, and applies model checking to rigorously identify video segments that satisfy the question's logical requirements. Only these logic-verified segments are submitted to the VLM, thus improving interpretability, reducing hallucinations, and enabling compositional reasoning without modifying or fine-tuning the model. Experiments on the LongVideoBench and CinePile long-form VQA benchmarks show that NeuS-QA significantly improves performance by over 10%, particularly on questions involving event ordering, causality, and multi-step compositional reasoning.