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

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

Machine Heart

Mar 30, 2026 · Artificial Intelligence

Proactive Interaction for Video Multimodal Models: MMDuet2 & ProactiveVideoQA

This article surveys the ICLR 2026 papers ProactiveVideoQA and MMDuet2, detailing how video multimodal large models can decide when to reply autonomously, the PAUC benchmark for evaluating timeliness and accuracy, a reinforcement‑learning training pipeline that requires no precise timestamps, and experimental findings on data construction, frame‑sampling density, and SOTA performance.

MMDuet2PAUCProactive Interaction

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Proactive Interaction for Video Multimodal Models: MMDuet2 & ProactiveVideoQA

Alibaba Terminal Technology

Oct 30, 2020 · Frontend Development

Why “Happy Work” Is the Core Assumption Behind Ant Design 4.0

In this talk, Lin Wai, senior experience design expert at Ant Group, explains how Ant Design 4.0 is built on the fundamental assumption that every person seeks happy work, explores the challenges of mismatched skill‑challenge balance in the digital age, and introduces the ETCG 2.0 methodology that makes design more natural, certain, and growth‑oriented.

Ant DesignFrontendProactive Interaction

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Why “Happy Work” Is the Core Assumption Behind Ant Design 4.0