Researchers proposed a self-adaptive anomaly detection system for connected vehicles that uses reinforcement learning and human feedback to adjust to system updates and changing workloads.
Major Chinese stock indexes fell sharply in afternoon trading, with computing hardware and semiconductor chip sectors leading the decline.
Amazon Web Services has published a guide detailing practical context engineering approaches to optimize Model Context Protocol tool design.
Tencent has released Hunyuan Hy3, a Mixture of Experts reasoning model featuring 295 billion parameters and a 256K context window, which has been integrated across several of its products.
BYD's premium brand Denza has officially unveiled the Denza N8, a luxury electric SUV featuring its second-generation blade battery and fast-charging technology.
Hugging Face has launched a one-click integration with Amazon SageMaker Studio to streamline the deployment and training of machine learning models.
Researchers have developed CodeTracer, a forensic framework designed to detect and trace malicious backdoor attacks in large language model code completion systems.
This study analyzes practitioner perspectives to build a causal theory regarding how AI coding agents affect the process and outcomes of code reviews.
Researchers have introduced Shift & Drift, a new benchmark designed to evaluate the robustness and generalization of autonomous driving motion planners under distribution shifts.
Track2Map is a new real-time 3D Gaussian Splatting system designed to reconstruct deformable anatomy during robotic surgery without relying on precise camera trajectory priors.
The proposed LEEVLA architecture improves vision-language-action models for robotics by focusing on critical environmental changes rather than treating all visual data equally.
This survey paper categorizes and reviews recent system-level optimizations for key-value caches to improve the efficiency and throughput of large language model serving.
A new graph-based framework evaluates the logical consistency and uncertainty of large language model reasoning steps rather than just checking the final output.
A large-scale study analyzes the usage patterns of an AI-based learning assistant among over 77,000 higher education students in distance learning programs.
A new evaluation suite called Blind-Spots-Bench has been developed to identify simple tasks that humans easily perform but multimodal AI models struggle to complete.
The AutoPersonas framework addresses the issue of behavioral collapse in long-term persona agents by enabling open-ended evolution across multiple timescales.
The authors present Diffusion-GR2, a generative reasoning re-ranker that utilizes block-diffusion language models to accelerate inference speeds in recommendation systems.
Researchers developed CatDT, a self-evolving multi-agent digital twin system designed to autonomously simulate and discover heterogeneous catalysts.
MasFACT is a continual learning framework designed to optimize and adapt communication topologies among multi-agent systems across evolving tasks.
A prompting framework called Concretized Proposition Prompting has been developed to improve reasoning and knowledge retrieval in large language models, particularly for medical tasks.
A new behavioral benchmark evaluates how closely the emergent object segmentation properties of self-supervised vision transformers align with human visual perception.
This paper introduces an online, reference-free evaluation framework to monitor the quality of flowchart image-to-code generation by vision-language models during inference.
The MultiFair framework addresses modality bias and demographic unfairness in multimodal medical classification models using dual-level gradient modulation.
Researchers have introduced InvestPhilBench, a multi-layer benchmark designed to evaluate how well large language models can reconstruct and apply expert investment decision frameworks.