#active-perception News & Analysis

5 articles tagged with #active-perception. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.

5 articles

AIBullisharXiv – CS AI · Jun 97/10

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ACTIVE-o3: Empowering MLLMs with Active Perception via Pure Reinforcement Learning

Researchers introduce ACTIVE-o3, a reinforcement learning framework that enables Multimodal Large Language Models (MLLMs) to actively perceive and intelligently select regions of interest for visual analysis. The system outperforms GPT-o3's zoom strategy while maintaining general understanding capabilities, with applications spanning robotics, autonomous driving, and remote sensing.

AIBullisharXiv – CS AI · Jun 57/10

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Active Video Perception: Iterative Evidence Seeking for Agentic Long Video Understanding

Researchers introduce Active Video Perception (AVP), an AI framework that enables agents to actively seek relevant evidence in long videos rather than passively processing entire content. The system uses an iterative plan-observe-reflect process to achieve superior accuracy on five benchmarks while reducing inference time by 82% and token usage by 88% compared to existing agentic methods.

AIBullisharXiv – CS AI · Jun 106/10

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Co-GLANCE: Uncertainty-Aware Active Perception for Heterogeneous Robot Teaming

Researchers introduce Co-GLANCE, an onboard AI system for multi-robot teams that detects and resolves perceptual uncertainty in unstructured environments without cloud computing. By distilling vision-language model capabilities into an efficient local model with statistical uncertainty guarantees, the system achieves 25-36% accuracy improvements over cloud-based approaches while reducing inference latency by 350x.

AIBullisharXiv – CS AI · May 286/10

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Agentic Active Omni-Modal Perception for Multi-Hop Audio-Visual Reasoning

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.

AIBullisharXiv – CS AI · Mar 26/1012

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See, Act, Adapt: Active Perception for Unsupervised Cross-Domain Visual Adaptation via Personalized VLM-Guided Agent

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.