Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 2610.25551

StreamAhead: Real-Time Streaming Video Question Answering via Adaptive Precomputation

Haoyang Fan·Dhruv Parikh·Rajgopal Kannan·Viktor Prasanna
View original source ↗Read source document ↗Export metadata ↓

Abstract

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. Existing systems construct key-value (KV) caches for the entire video as it arrives, although a query typically uses only a few of the preceding frames as evidence. 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 encodes video segments independently and lets each remain at a different representation level, from inexpensive visual features to costly KV caches, completing KV caches only for the retrieved segments when a query arrives. StreamAhead profiles offline the cost of advancing a segment to each representation level. Its runtime uses these profiles to schedule segment computation concurrently with query execution, pipelining it across heterogeneous CPU and GPU resources while maintaining the required video-processing rate. StreamAhead achieves accuracy competitive with state-of-the-art methods with up to 10.4x lower time-to-first-token (TTFT), and up to 5.9x lower TTFT than state-of-the-art streaming systems. It sustains 30 FPS with P95 TTFT under 0.5 s, and up to 57 FPS with P95 TTFT under 2 s. 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.

Explore related subjects

cs.DCcs.CV

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Haoyang Fan, Dhruv Parikh, Rajgopal Kannan, Viktor Prasanna. 2026-10-18. StreamAhead: Real-Time Streaming Video Question Answering via Adaptive Precomputation. https://arxiv.org/abs/2610.25551

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

DIFFLET: A Fast Inference Engine for Diffusion Transformer Models on AWS Trainium

On AWS Trainium2, every component of a diffusion model runs as a program compiled ahead of time for fixed shapes, while pipelines differ across models, prompt lengths across requests, and caching decisions across denoising steps. We present Difflet, a serving engine in which five model pipelines share one compilation backend, joint attention is split across cores by attention head, and a host controller decides at each denoising step whether to run the denoiser or reuse its earlier output. A small probe program computes the caching signal on the device and returns one number per step, and a resident model process keeps every program loaded across requests. Across five image and video model families, fused attention accelerates denoiser steps by 1.2-4.3x over the same framework using PyTorch/XLA attention, and at 32.8k visual tokens it is the only attention that compiles. On four of the five models, resident serving is 1.5-3.4x faster than standalone runs that reload weights from a warm file cache for each generation.

cs.DC↗
arXiv2026

Constrained Federated Reinforcement Learning for Secure Offloading in Vehicular Edge Computing

Vehicular Edge Computing (VEC) reduces latency for computation-intensive vehicular applications by offloading tasks to roadside edge servers. Real vehicular workloads, however, consist of dependent subtasks with stringent reliability needs, while the cross-region collaborative learning used to adapt offloading policies must stay robust to heterogeneous data and secure over open wireless channels. We propose CRAFT, a Constrained Reinforcement-based Adaptive Federated Task Offloading framework that casts Directed Acyclic Graph (DAG)-structured offloading and resource allocation as a Constrained Markov Decision Process. CRAFT couples centralized-critic policy learning with Lagrangian relaxation to explicitly regulate the long-term task-completion constraint, and uses two-tier federated aggregation with proximal regularization to jointly mitigate non-Independent and Identically Distributed (non-IID) drift and stabilize constraint adaptation. Confidentiality and integrity for federated parameter exchange are provided by post-quantum homomorphic encryption and lattice-based signatures with modest amortized overhead. On a Virtual Machine-based testbed over a realistic Melbourne Central Business District network with real computation kernels, CRAFT cuts weighted cost by up to 42% over state-of-the-art baselines, maintains a completion rate of around 96%, and adds 6.4% security overhead.

cs.DC↗
arXiv2026

Evaluating Runtime Prediction and Conservative Walltime Policies in HPC Systems

Efficient use of large-scale supercomputing systems depends on accurate runtime estimation and walltime selection. To avoid premature termination, users often overestimate walltime, leading to poor resource utilization and reduced scheduling efficiency. This paper presents a predictor-agnostic framework for safe walltime recommendation in production HPC environments. Rather than proposing a new runtime prediction model, the primary contribution is the SAFE decision layer, which converts predicted runtimes into conservative bounded walltime recommendations suitable for deployment. Runtime prediction is therefore treated as a modular component within the proposed framework. We evaluate the approach using large-scale production job histories from the Fugaku and NREL Eagle supercomputers, comprising approximately 24M and 11M jobs, respectively. Runtime prediction performance is first evaluated using representative predictors, including linear regression, neural networks, and a neural ensemble. The proposed SAFE framework is then evaluated using the NN ensemble predictor. Experimental results indicate that similarity-aware prediction substantially improves runtime estimation for workloads similar to previous executions. When combined with the proposed conservative walltime recommendation policies, these improvements enable walltime gap reductions of approximately 20 - 26% while maintaining job termination rates below 1% across both systems.

cs.DC↗