Customer-obsessed science

Existing deepfake detection techniques struggle to keep up with the ever-evolving novel, unseen forgeries methods. This limitation stems from their reliance on statistical artifacts learned during training, which are often tied to specific generation processes that may not be representative of samples from new, unseen deepfake generation methods encountered at test time. We propose that incorporating language

LLM-based multi-agent systems (MAS) show promise on complex tasks but remain prone to coordination failures such as goal drift, error cascades, and misaligned behaviors. We propose Explicit Trait Inference (ETI), a psychologically grounded method for improving coordination. ETI enables agents to infer and track partner characteristics along two established psychological dimensions—warmth (e.g., trust) and

While many existing grasping models can be highly reliable in picking objects in most cases, challenging scenarios persist in industrial automation where objects are difficult to grasp—such as when positioned in corners, occluded by other items, or tightly clustered. These challenges are prevalent in smart manufacturing and logistics systems, where current robotic systems often require costly human intervention

Multi-task model merging combines separately trained expert models into a single model that handles all tasks without co-training. Standard practice merges experts at their optimal validation loss. We challenge this convention by systematically studying how training duration of domain experts affects the quality of the merged model. We fine-tune experts on five domains (Math, Code, Instruction Following

Optimizing Large Language Models (LLMs) for production AI agent deployment demands substantial computational resources and specialized human expertise (e.g., prompt engineering). Self-evolution offers a promising solution by enabling agents to autonomously enhance capabilities through structured feedback, improving performance without expensive manual optimization. However, most existing self-evolving agents