Customer-obsessed science

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

Understanding the behavior and logical structure of complex algorithms is a fundamental challenge in industrial systems. Recent advancements in large language models (LLMs) have demonstrated remarkable code understanding capabilities. However, their potential for reverse engineering algorithms into interpretable causal structures remains unexplored. In this work, we develop a multi-agent framework, RECoRD