4 accepted papers
Despite the advanced capabilities of large language models (LLMs), their temporal reasoning ability remains underdeveloped. Prior works have highlighted this limitation, particularly in maintaining temporal consistency when understanding event relations. For example, models often confuse mutually ex…
In this study, we investigate the potential of language models (LMs) in aiding patients experiencing anomia, a difficulty identifying the names of items. Identifying the intended target item from patient’s circumlocution involves the two challenges of term failure and error. (1) The terms relevant t…
Policy gradient methods are among the most effective methods for large-scale reinforcement learning, and their empirical success has prompted several works that develop the foundation of their global convergence theory. However, prior works have either required exact gradients or state-action visita…
Transmission between high speed and high force motions is a classic, but challenging problem for most engineering disciplines as well as robotics. This study optimizes the performances (i.e., both velocity and force) of the distributed actuation mechanism (DAM) based on the novel concept of continuo