Large Language Models (LLMs) have revolutionized various domains, including natural language processing, data analysis, and software development, by enabling automation.In software engineering, LLM-powered coding agents have garnered significant attention due to their potential to automate complex development tasks, assist in debugging, and enhance productivity.However, existing approaches often struggle with sub-optimal decision-making, requiring either extensive manual intervention or inefficient compute scaling strategies.To improve coding agent performance, we present Dynamic Action Re-Sampling (DARS), a novel inference time compute scaling approach for coding agents, that is faster and more effective at recovering from sub-optimal decisions compared to baselines.While traditional agents either follow linear trajectories or rely on random sampling for scaling compute, our approach DARS works by branching out a trajectory at certain key decision points by taking an alternative action given the history of the trajectory and execution feedback of the previous attempt from that point.We evaluate our approach on SWE-Bench Lite benchmark, demonstrating that this scaling strategy achieves a pass@k score of 55% with Claude 3.5 Sonnet V2.Our framework achieves a pass@1 rate of 47%, outperforming state-of-the-art (SOTA) opensource frameworks. 1

Recent years have seen the rapid development of large generative models for text; however, much less research has explored the connection between text and another "language" of communication -music.Music, much like text, can convey emotions, stories, and ideas, and has its own unique structure and syntax.In our work, we bridge text and music via a textto-music generation model that is highly efficient, expressive, and can handle long-term structure.Specifically, we develop Moûsai, a cascading two-stage latent diffusion model that can generate multiple minutes of high-quality stereo music at 48kHz from textual descriptions.Moreover, our model features high efficiency, which enables real-time inference on a single consumer GPU with a reasonable speed.Through experiments and property analyses, we show our model's competence over a variety of criteria compared with existing music generation models.Lastly, to promote the opensource culture, we provide a collection of opensource libraries with the hope of facilitating future work in the field. 1

AI systems are becoming increasingly intertwined with human life. In order to effectively collaborate with humans and ensure safety, AI systems need to be able to understand, interpret and predict human moral judgments and decisions. Human moral judgments are often guided by rules, but not always. A central challenge for AI safety is capturing the flexibility of the human moral mind — the ability to determine when a rule should be broken, especially in novel or unusual situations. In this paper, we present a novel challenge set consisting of moral exception question answering (MoralExceptQA) of cases that involve potentially permissible moral exceptions – inspired by recent moral psychology studies. Using a state-of-the-art large language model (LLM) as a basis, we propose a novel moral chain of thought (MoralCoT) prompting strategy that combines the strengths of LLMs with theories of moral reasoning developed in cognitive science to predict human moral judgments. MoralCoT outperforms seven existing LLMs by 6.2% F1, suggesting that modeling human reasoning might be necessary to capture the flexibility of the human moral mind. We also conduct a detailed error analysis to suggest directions for future work to improve AI safety using MoralExceptQA. Our data is open-sourced at https://huggingface.co/datasets/feradauto/MoralExceptQA and code at https://github.com/feradauto/MoralCoT.