Trinetra: O(N) Video Ingest, O(1) Query — A Proactive Temporal Event Graph for Long-Form Video Understanding

Current video understanding systems are fundamentally reactive: they re-process every frame at query time, incurring quadratic O(T) attention complexity that caps practical span at 30–60 minutes and costs $15–24 per query on hour-long videos. We argue this is an architectural choice, not a physical law. We introduce Trinetra (ThiriNay-thra, Tamil for “three-eyed,” evoking simultaneous perception across temporal scales) with a provably better total-cost model: O(T) + O(Q) versus reactive systems’ O(Q · T) for Q queries over a video of length T. The core of Trinetra is a Temporal Event Graph (TEG): a hierarchical representation built once during ingest and queried via dual FAISS indices without ever re-processing the video. Our Multi-Scale Temporal Attention Shift (TAS) performs a wavelet-like multiresolution decomposition of the video embedding stream — simultaneously capturing gestures (highfrequency), actions (mid-frequency), and scenes(low-frequency) — combined with a discrete firstorder temporal derivative ∆t = et − et−1 that detects causal transitions in O(1) per frame. The TEG hierarchy (Frame → Event → Chapter → Video) constitutes a semantic compression ladder: information-theoretic entropy decreases monotonically up the hierarchy, retaining only causally relevant structure. Eight temporal memory tokens provide O(1)-space low-rank compression of unbounded video history. Evaluated on a Raspberry Pi 5 assembly tutorial (1,236 s) on a consumer RTX 3050 (4 GB VRAM): 60% category success rate without audio (estimated 80%+ with audio enabled), 25.8 s ingest, 15.2 s per query, 317,500× fewer query-time FLOPs than commercial VLMs, at under $0.01 per 100 queries versus $1,500–2,430 for GPT-4o / Gemini / ClaudeCopyright - @S KhavinIs supplement to - https://doi.org/10.5281/zenodo.18850768 Contact - skhavin.res@gmail.com I am currently looking for research internships. I am a independent researcher with no proper guidance. My aim is to publish this to cvpr. Paper may have some formatting issues. If u have any questions or can guide me on getting research internships please feel free to contact me on my email. I would be happy to help