Researchers have developed a framework called auto-ibDLM to predict the evolution of public events. The framework represents events as dynamic interac...
Researchers have proposed a framework called EcoVLA for energy-efficient inference of Vision-Language-Action models in real-time. These models are use...
Researchers have developed a new model called MoCA for chart-to-code generation. This task requires a model to read visual details of a chart and writ...
Researchers have developed context-dependent argumentation frameworks (CDAFs), which allow for different attacks to succeed or fail based on specific ...
Researchers have developed a new algorithm called ATLAS that allows large language models (LLMs) to design complex algorithms from scratch without rel...
Researchers propose AdmitOR, a label-free admission mechanism for optimization modeling that uses calibrated external behavioral evidence. Unlike exis...
Researchers have developed a context-fusion framework to improve the accuracy of endoscopic polyp reporting. The framework specializes a pre-trained g...
Researchers have developed Agent Gym, a framework designed to continuously evaluate and improve Large Language Model (LLM) agents in production enviro...
Researchers have developed a new framework called ESTP (Entropy-and-Semantic Token Pooling) to improve the accuracy of predicting output lengths in la...
Researchers have developed a new defense system called TRACE to protect large language models (LLMs) from multi-turn adversarial conversations. These ...
Researchers have introduced a new benchmark called VARM-Bench to evaluate the performance of language models in moderating Chinese online content. The...
Researchers have developed a method to estimate the maximum accuracy possible for emotion recognition from text. They call this 'bias-corrected ceilin...