StreamAhead: Real-Time Streaming Video Question Answering via Adaptive Precomputation
Real-time streaming video question answering with vision-language models must answer queries that arrive at any time with low latency while sustaining the video-processing rate required by the application.
ProofPaper ↗
Key points
- This highlights a fundamental trade-off: performing more computation as video arrives reduces query-time computation but can increase processing lag, whereas deferring computation can increase the video-processing rate but requires additional query-time computation for retrieved content.
- We present StreamAhead, an algorithm-system co-design that exploits this trade-off through adaptive precomputation: dynamically controlling how much computation to complete for each video segment before a query arrives.
- StreamAhead profiles offline the cost of advancing a segment to each representation level.
- To our knowledge, StreamAhead is the first real-time streaming video QA system with competitive accuracy, setting a new Pareto frontier between video-processing rate and TTFT.
Sources (1)
- [1]StreamAhead: Real-Time Streaming Video Question Answering via Adaptive PrecomputationarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 18, 09:48 PM
Real-time streaming video question answering with vision-language models must answer queries that arrive at any time with low latency while sustaining the video-processing rate required by the application.
This highlights a fundamental trade-off: performing more computation as video arrives reduces query-time computation but can increase processing lag, whereas deferring computation can increase the video-processing rate but requires additional query-time computation for retrieved content.
Extractive summary: sentences quoted from the sources.
Before this
- Oct 18, 2026Bud ContextCompact 2.0: Context Compaction for Agent Success and Accuracy with Small Language Models and Constrained Context Windows
- Oct 18, 2026Meet Voxel51 3.0: A Unified Multimodal Data Platform for Physical AI
- Oct 16, 2026Torch-DPhys: A Torch-Native, MOOSE-Compatible Framework for Differentiable Physics Simulation and Hybrid Machine Learning Integration
- Oct 16, 2026[Research] 2,788 tokens/s on a single RTX 5090 with a diffusion LLM — Introducing ORL (fully open source)
- Oct 16, 2026Peak-Memory Schedules You Can Trust: Exact Realization, Certified Optimality, and the Limits of Recomputation for Inference
- Oct 16, 2026One Communicator, Three Vendors: Extending the MoA Predictive Theory to a Real NVIDIA+AMD+CPU MPI Ensemble