When Should a Streaming Video LLM Speak? Evidence‑Condition Alignment via Explicit Scene Graphs (Response‑G1)

The ACL 2026 paper introduces Response‑G1, a proactive streaming video‑LLM framework that aligns visual evidence with response conditions using explicit scene‑graph modeling, memory‑augmented retrieval, and trigger‑based decision making, achieving 12.8 % and 15.1 % improvements on active tasks of OVO‑Bench and StreamingBench while also benefiting passive settings.

Proactive InteractionResponse-G1Scene Graph

0 likes · 9 min read

When Should a Streaming Video LLM Speak? Evidence‑Condition Alignment via Explicit Scene Graphs (Response‑G1)

AntTech

Jul 2, 2020 · Operations

Innovative Design and Implementation of the Barad‑Dur Custom Monitoring Dashboard

This article introduces the Barad‑Dur custom monitoring dashboard of Ant Monitoring, detailing its WYSIWYG editor, advanced interaction features, controller concept, extensible data‑source architecture, unified time‑series format, scene‑graph inspired layout engine, and future roadmap for cloud‑native observability.

DataSourceOperationsScene Graph

0 likes · 12 min read

Innovative Design and Implementation of the Barad‑Dur Custom Monitoring Dashboard

Alibaba Cloud Developer

Nov 19, 2019 · Artificial Intelligence

Can AI Imagine Visually? Seq‑SG2SL for Scene‑to‑Semantic Layout

This article introduces the Seq‑SG2SL framework, which tackles the challenge of granting AI visual imagination by converting scene graphs into semantic layouts, discusses the limitations of existing text‑to‑image methods, proposes the SLEU metric for automatic evaluation, and presents experimental results demonstrating its effectiveness.

AISLEUScene Graph

0 likes · 16 min read

Can AI Imagine Visually? Seq‑SG2SL for Scene‑to‑Semantic Layout