# Streaming Video Understanding

Streaming video understanding must retain evidence before anyone knows which details later queries will need, yet existing methods flatten event transitions when compressing history and cannot recover details once a frame has passed. DeltaStreamer explicitly records visual changes between adjacent frames in a hierarchical textual memory, and after a query arrives a query-driven planner decides whether to retrieve, keep observing, or replay historical frames. It scores 66.07% overall on OVO-Bench and 59.53% on StreamingBench, above the representative streaming methods it is compared with.

Streaming video understanding must retain evidence before anyone knows which details later queries will need, yet existing methods flatten event transitions when compressing history and cannot recover details once a frame has passed. DeltaStreamer explicitly records visual changes between adjacent frames in a hierarchical textual memory, and after a query arrives a query-driven planner decides whether to retrieve, keep observing, or replay historical frames. It scores 66.07% overall on OVO-Bench and 59.53% on StreamingBench, above the representative streaming methods it is compared with.

The work introduces APM-Bench, which reformulates egocentric streaming interaction as multi-session life trajectories (549 sessions, 104 trajectories, 2,719 candidates, averaging 69 minutes of video per trajectory) and evaluates general video models and eight specialized memory systems on cross-session understanding, real-time perception, and adaptive response, plus an evidence-availability-aware test; results reveal a clear utility-latency-storage trade-off: raw video memory gives the strongest cross-session performance but needs GiB-scale storage and high latency, text summaries cut storage to the KiB scale at the cost of cross-session performance, event-structured memory shows the strongest utility among specialized systems, and adaptive response remains difficult even with rich history

The work introduces APM-Bench, which reformulates egocentric streaming interaction as multi-session life trajectories (549 sessions, 104 trajectories, 2,719 candidates, averaging 69 minutes of video per trajectory) and evaluates general video models and eight specialized memory systems on cross-session understanding, real-time perception, and adaptive response, plus an evidence-availability-aware test; results reveal a clear utility-latency-storage trade-off: raw video memory gives the strongest cross-session performance but needs GiB-scale storage and high latency, text summaries cut storage to the KiB scale at the cost of cross-session performance, event-structured memory shows the strongest utility among specialized systems, and adaptive response remains difficult even with rich history

The work introduces AsyncLLM, a general framework built on the Python asyncio async/await programming model that lets pretrained LLMs observe, reason and act as concurrent coroutines, sharing memory in real time through CacheBlocks (attention KV caches and Gated DeltaNet recurrent states) and cache views, and demonstrates asynchronous operation with the same family of Qwen 3.x vision-language models on streaming video understanding, ViZDoom games and DevOps-Gym system monitoring without task-specific training.

The work introduces AsyncLLM, a general framework built on the Python asyncio async/await programming model that lets pretrained LLMs observe, reason and act as concurrent coroutines, sharing memory in real time through CacheBlocks (attention KV caches and Gated DeltaNet recurrent states) and cache views, and demonstrates asynchronous operation with the same family of Qwen 3.x vision-language models on streaming video understanding, ViZDoom games and DevOps-Gym system monitoring without task-specific training.

TRACE introduces a condition-aware benchmark and evaluation framework for streaming video understanding that makes temporal validity, execution conditions, and operational outcomes explicit, evaluating eight publicly available models or systems in eight configurations on 1,240 records from 517 videos, and finds that nearly identical QA accuracy can mask substantial differences in completion, answer validity, and generation workload, while proactive performance separates into response quality, response delay, false alarms, and missed target windows.

TRACE introduces a condition-aware benchmark and evaluation framework for streaming video understanding that makes temporal validity, execution conditions, and operational outcomes explicit, evaluating eight publicly available models or systems in eight configurations on 1,240 records from 517 videos, and finds that nearly identical QA accuracy can mask substantial differences in completion, answer validity, and generation workload, while proactive performance separates into response quality, response delay, false alarms, and missed target windows.