We propose Step-by-Step Coding (SBSC): a multi-turn math reasoning framework that enables Large Language Models (LLMs) to generate sequence of programs for solving Olympiad level math problems. After each turn/step, by leveraging the code execution outputs and programs of previous steps, the model generates the next sub-task and the corresponding program to complete it. This way, SBSC, sequentially navigates to reach the final answer. SBSC allows more granular, flexible and precise approach to problem-solving compared to existing methods. Extensive experiments highlight the effectiveness of SBSC in tackling competition and Olympiad-level math problems. For Claude-3.5-Sonnet, we observe SBSC (greedy decoding) surpasses existing state-of-the-art (SOTA) program generation based reasoning strategies by absolute 10.7% on AMC12, 8% on AIME and 12.6% on MathOdyssey. Given SBSC is multi-turn in nature, we also benchmark SBSC’s greedy decoding against self- consistency decoding results of existing SOTA math reasoning strategies and observe performance gain by absolute 6.2% on AMC, 6.7% on AIME and 7.4% on MathOdyssey.

Video Object Detection (VOD) is one of the fundamental problems in video understanding with applications ranging from surveillance to autonomous driving. But many such real-world applications are unable to leverage the existing VOD models owing to their higher computational complexity which reduces inference speed. Single-stage still-image object detection models are naively used without any use of video information. In this paper, we present YOLOX based VOD model, YOLO-MaxVOD, which provides a better trade-off between accuracy and inference time than the current real-time VOD solutions. Specifically, we propose a temporal fusion module that integrates within the YOLOX architecture to take advantage of the high speed that the YOLOX model offers. In our experimentation on the Imagenet-VID dataset, we show that YOLO-MaxVOD shows 4.4-5.6% AP50 improvement over the baseline YOLOX, across different versions, with just a 1-2 ms increase in latency on NVIDIA 1080Ti GPU.