#real-time-systems News & Analysis

4 articles tagged with #real-time-systems. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.

4 articles

AIBearisharXiv – CS AI · Jun 87/10

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Hearing the Unspoken: Language Model Priors for Acoustic Adversarial Attacks

Researchers demonstrate a new adversarial attack called Semantic Gambit that exploits Large Language Models to significantly compromise real-time Automatic Speech Recognition systems. By leveraging predictive context from LLMs, the attack achieves a 35.6% Word Error Rate—three times higher than previously documented attacks—revealing a critical vulnerability in ASR pipelines that operate under temporal constraints.

AIBullishOpenAI News · May 47/10

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How OpenAI delivers low-latency voice AI at scale

OpenAI has rebuilt its WebRTC infrastructure to enable real-time voice AI conversations with minimal latency and global scalability. The technical achievement demonstrates a significant advancement in conversational AI systems that can maintain natural turn-taking dynamics while serving users worldwide.

🏢 OpenAI

AINeutralarXiv – CS AI · May 296/10

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Harmonizing Real-Time Constraints and Long-Horizon Reasoning: An Asynchronous Agentic Framework for Dynamic Scheduling

Researchers introduce RACE-Sched, an asynchronous AI framework that combines real-time symbolic heuristics with LLM-powered reasoning to solve dynamic job shop scheduling problems in industrial systems. The approach decouples fast reactive execution from slower deliberative optimization, enabling superior performance over deep reinforcement learning baselines while maintaining interpretability and millisecond-level response times.

AIBullisharXiv – CS AI · Mar 36/104

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Closed-Loop Action Chunks with Dynamic Corrections for Training-Free Diffusion Policy

Researchers have developed DCDP, a Dynamic Closed-Loop Diffusion Policy framework that significantly improves robotic manipulation in dynamic environments. The system achieves 19% better adaptability without retraining while requiring only 5% additional computational overhead through real-time action correction and environmental dynamics integration.