[Paper] StreamReady: Learning What to Answer and When in Long Streaming Videos

Source: arXiv - 2603.08620v1

Overview

The paper StreamReady tackles a subtle but critical challenge in streaming‑video AI: not only must a model answer a question correctly, it must also answer at the right moment—exactly when the visual evidence appears. By introducing an Answer Readiness Score (ARS) that penalizes early speculation and late responses, the authors propose a new “readiness‑aware” formulation that aligns model behavior with real‑time, time‑sensitive applications such as live sports analytics, surveillance, and interactive assistants.

Key Contributions

• Answer Readiness Score (ARS): A timing‑aware metric that combines correctness with asymmetric penalties for answering too early or too late.
• StreamReady framework: A lightweight, plug‑and‑play module that decides when enough visual evidence has been observed before emitting an answer, integrating temporal reasoning directly into the inference loop.
• ProReady‑QA benchmark: A newly curated dataset of long streaming videos with meticulously annotated evidence windows and proactive multi‑turn questions covering both local (short‑term) and global (long‑term) contexts.
• Broad empirical validation: State‑of‑the‑art performance on ProReady‑QA and consistent gains across eight additional streaming and offline long‑video benchmarks, demonstrating the generality of the approach.

Methodology

1. Readiness‑aware objective:

   • For each question, the ground‑truth evidence window ([t_s, t_e]) is known.
   • The ARS loss adds an early penalty that grows the farther a prediction precedes (t_s) and a late penalty that grows after (t_e).
   • This asymmetric design reflects real‑world costs: premature answers can mislead, while delayed answers miss the opportunity to act.

2. Readiness module (StreamReady):

   • Operates on top of any video encoder (e.g., a transformer or 3‑D CNN).
   • At each time step, it computes a readiness confidence based on accumulated visual features and the question embedding.
   • When confidence crosses a learned threshold, the model “locks in” an answer; otherwise it continues watching.
   • The module is lightweight (≈ 2 M parameters) and can be trained end‑to‑end with the ARS loss.

3. Training & evaluation pipeline:

   • Models are trained on ProReady‑QA using the ARS loss, while standard QA loss is also retained to preserve answer accuracy.
   • Evaluation reports both traditional QA accuracy and ARS‑adjusted accuracy, the latter reflecting on‑time performance.

Results & Findings

• On‑time answering: StreamReady reduces early answers by 38 % and late answers by 45 % compared to baselines.
• Generalization: Even when evaluated on offline (non‑streaming) long‑video QA datasets, the readiness module improves performance, indicating that temporal awareness benefits static video understanding as well.

Practical Implications

• Live analytics & alerts: Systems such as sports commentary bots, security monitoring, or autonomous vehicle perception can now trigger alerts exactly when the relevant event unfolds, minimizing false alarms and missed detections.
• Interactive assistants: Voice‑controlled agents that answer “What just happened?” in a live video feed can provide concise, timely responses without waiting for the entire clip to finish.
• Resource efficiency: By stopping inference once readiness is reached, StreamReady can cut unnecessary frame processing, saving compute and bandwidth in edge deployments.
• Plug‑and‑play adoption: Because the readiness module sits atop existing encoders, developers can retrofit it onto current video‑QA pipelines with minimal code changes and training overhead.

Limitations & Future Work

• Evidence window granularity: The current ARS assumes a single contiguous evidence interval; complex queries that require multiple disjoint evidence segments may need a more flexible formulation.
• Threshold sensitivity: The learned readiness threshold can be dataset‑specific; adapting it on‑the‑fly for unseen domains (e.g., different frame rates or latency constraints) remains an open challenge.
• Scalability to ultra‑long streams: While StreamReady handles videos up to several minutes, truly continuous streams (hours‑long) may require hierarchical or memory‑efficient extensions.

Authors

• Shehreen Azad
• Vibhav Vineet
• Yogesh Singh Rawat

Paper Information

• arXiv ID: 2603.08620v1
• Categories: cs.CV
• Published: March 9, 2026
• PDF: Download PDF