Customer-obsessed science

Large language model (LLM)-based agents increasingly rely on tool use to complete real-world tasks. While existing works evaluate the LLMs' tool use capability, they largely focus on the final answers yet overlook the detailed tool usage trajectory, i.e., whether tools are selected, parameterized, and ordered correctly. We introduce TRAJECT-Bench, a trajectory-aware benchmark to comprehensively evaluate

Training-free verbal reinforcement learning enables LLM agents to learn from world feedback—objective signals such as dynamic task outcomes, market returns, or demand forecasts—by extracting verbal rules from experience and injecting them as context, updating the agent's behavior without parameter changes. However, in non-stationary environments these agents face a retention-forgetting dilemma: retaining

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