6 accepted papers
Large language models (LLMs) exhibit remarkable mathematical reasoning abilities through supervised fine-tuning (SFT) or reinforcement learning with verifiable rewards (RLVR). However, adapting them to scientific domains like molecular optimization is challenging: its datasets provide only reference…
Prefill-Decode (PD) disaggregation has become the standard architecture for modern LLM inference engines, which alleviates the interference of two distinctive workloads. With the growing demand for multi-turn interactions in chatbots and agentic systems, we re-examined PD in this case and found two …
Image denoising poses a significant challenge in image processing, aiming to remove noise and artifacts from input images. However, current denoising algorithms implemented on electronic chips frequently encounter latency issues and demand substantial computational resources. In this paper, we intro…
Magnetic levitation control provides a promising solution for wireless capsule endoscopy by minimizing tissue pressure and reducing patient discomfort and risks. Compared to electromagnetic actuation systems, using permanent magnets as the actuation source provides stronger magnetic fields at a lowe
Locomotion is a fundamental capability that all mobile robots strive to achieve. A large variety of magnetic miniature robots have been proposed to leverage the magnetic forces and torques between internal magnetic agents and external magnetic fields to enable various locomotion on different terrain
One of the key problems in model-free deep reinforcement learning is how to obtain more accurate value estimations. Current most widely-used off-policy algorithms suffer from over- or underestimation bias which may lead to unstable policy. In this paper, we propose a novel method, Adaptive Estimatio…