Researchers present woma, a foundation model trained without labels on roughly one million gastrointestinal endoscopy frames. Task-specific models are then fine-tuned from this base, and the authors describe a systematic design intended for production deployment, including requirements and performance targets. The work targets real-time use in clinical endoscopy workflows.
A new arXiv paper proposes RelayS2S, a dual-path speculative generation method for real-time spoken dialogue systems. It addresses the trade-off between latency and response quality, since end-to-end speech-to-speech models can respond instantly and manage turn-taking, backchanneling, and interruptions, but tend to produce semantically weaker replies. The method aims to combine immediate responsiveness with improved response content.
A newly updated arXiv paper introduces MAVEN-T, a method combining reinforcement learning with heterogeneous knowledge distillation to forecast the future paths of multiple agents simultaneously. The work targets real-time deployment in autonomous vehicles, where anticipating surrounding traffic informs collision checking, planning, and control. The approach is designed to remain dependable in dense scenarios with diverse and multimodal agent behaviors.
Deep spatially selective filters deliver high-quality, real-time speech enhancement for stationary speakers whose directions are known, but they struggle when sources move. A new arXiv paper introduces an autoregressive guidance scheme built on Bayesian speaker tracking that lets these filters follow moving speakers from only their initial positions. The approach preserves the efficiency and enhancement quality of the underlying architecture in dynamic scenes.
A new arXiv preprint presents PeriodicCALM, a framework for detecting anomalies on the fly in cyclostationary data streams, whose statistical properties vary periodically over time. Rather than relying on classical stationary assumptions, the algorithm adapts to these recurring temporal patterns as it monitors live data for deviations. The work was posted to arXiv's machine learning listing as a cross-listed submission.