4 accepted papers
As large language models (LLMs) are increasingly deployed in high-stakes domains such as education, healthcare, and law, accurately evaluating their nuanced reasoning process becomes essential to ensure their safety, reliability, and trustworthiness. However, most existing benchmarks evaluate LLMs a
Collecting demonstrations through human teleoperation is an effective approach for learning complex manipulation skills. However, challenges such as morphology gaps, control latency, and limited feedback make high-quality data collection costly and inefficient. In this paper, we introduce Neural Tel…
Generating large-scale demonstrations for dexterous manipulation remains a challenging problem, and various approaches have been proposed in recent years to address it. Among these, generative models have emerged as a promising paradigm, enabling the efficient generation of diverse and plausible dem…
Large language models (LLMs) fine-tuned on multimodal financial data have demonstrated impressive reasoning capabilities in various financial tasks. However, they often struggle with multi-step, goal-oriented scenarios in interactive financial markets, such as trading, where complex agentic approach…