๊ฐ€์ž… ํ›„ ์ดˆ๋Œ€ ๋งํฌ๋ฅผ ๊ณต์œ ํ•˜๋ฉด ๋™์˜์ƒ ์žฌ์ƒ ๋ฐ ์ดˆ๋Œ€ ๋ณด์ƒ์„ ๋ฐ›์„ ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค.

cv usk
@cv_usk
AI / Software Research Notes AI Agent, LLMOps, MLOps, Software Architecture ๆŠ•็จฟใฏๅ€‹ไบบใฎๆ„่ฆ‹ใงใ™ใ€‚
๊ฐ€์ž… May 2026
284 ํŒ”๋กœ์ž‰ ์ค‘    451 ํŒฌ
TL;DR Can an AI watch a video stream and help before you ask? This paper tackles implicit and changing user intent with a benchmark, a model and an agent harness. Title: StreamVibe: Proactive Video Agents for Evolving User Intent URL: Key points ใƒป๐ŸŽฏ Intent split into three levels: explicit, implicit, dynamic ใƒป๐Ÿ“š StreamVibe-Bench: 4,100 samples, 20,546 help points, 183.4 hours of video ใƒป๐Ÿง  StreamVibe-VL: adaptive SFT, then GRPO with trajectory-level rewards ใƒป๐Ÿ”‡ RL cuts false triggers: explicit FPR 2.02 vs. 12.75 for ViSpeak ใƒป๐Ÿ—‚ The Harness tracks active intents and progress, helping most on dynamic intents ใƒป๐Ÿ“ˆ Dynamic Pointwise F1 rises from 58.70 (SFT) to 66.34 (RL) ใƒปโš ๏ธ Long interactions remain hard: dynamic Trajectory C-F1 is only 8.91 The hard part of proactivity is knowing when to stay quiet, not just when to speak. #VideoAgents# #VLM#
๋” ๋ณด๊ธฐ