2 articles tagged with #active-perception. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.
Researchers introduce MOV-Bench, a benchmark for evaluating multi-hop audio-visual reasoning in large language models, and propose AOP-Agent, an agentic framework that enables open-source multimodal LLMs to perform active perception across temporally dispersed audio and visual evidence without additional training.
Researchers introduce Sea² (See, Act, Adapt), a novel approach that improves AI perception models in new environments by using an intelligent pose-control agent rather than retraining the models themselves. The method keeps perception modules frozen and uses a vision-language model as a controller, achieving significant performance improvements of 13-27% across visual tasks without requiring additional training data.
