SENRI: Understanding the Space-Event Nexus in Real-Life Videos
Thus we introduce SENRI, a task that studies this space--event connection in long, continuous real-life egocentric video.
ProofPaper ↗
Key points
- Human memories are organized in a way that connects events with their corresponding spatial contexts.
- An AI assistant should also be able to memorize and recall the nexus of events and spaces.
- Also, we evaluate three typical long video understanding paradigms: direct perception, evidence seeking, and persistent memory.
- Then we show two representative failure modes of existing memory methods and address these evidence gaps with SENRI-Agent, a controller that uses Target-Guided Visual Check (TGVC) to propose local visual check tasks for parallel verifiers and integrate the reports into a working memory to help with reasoning.
Sources (1)
- [1]SENRI: Understanding the Space-Event Nexus in Real-Life VideosarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 18, 02:41 PM
Thus we introduce SENRI, a task that studies this space--event connection in long, continuous real-life egocentric video.
Human memories are organized in a way that connects events with their corresponding spatial contexts.
Extractive summary: sentences quoted from the sources.
Before this
- Oct 18, 2026Google Open-Sources Harness2, a Recursive Agent Harnessing Framework
- Oct 18, 2026Auto-MD-bench: Evaluating Autoresearch Agents for Real-World Medical Tasks
- Oct 17, 2026Are We Evaluating VLM Token Pruning Right? Looking Beyond Accuracy and Token Count
- Oct 16, 2026SteerSearch: Interactive Search Reasoning with Mid-Response Steering
- Oct 16, 2026Persistent Memory and Iterative Improvement in an LLM Agent for Chinese-Character Drawing
- Oct 16, 2026PreciseEdit: Strong Yet Stable Semantic Transport for One-Step Image Editing