Firehose

This paper develops a system called RoboArchitect that automates the design of robotic agents by learning to optimize their harnesses, which determine when the agent interacts with the environment. A practitioner might care about this because it could significantly improve the performance of robotic agents without requiring significant updates to their underlying models.

This paper creates a system that turns users' histories into labels to help improve AI agents' memory systems, making them more adaptable to different tasks and users. Practitioners might care about this because it can help improve the performance of AI agents in real-world applications.

This paper explores whether achieving artificial general intelligence (AGI) depends on improving a lightweight predictor (System-1) rather than just scaling a resource-intensive executive (System-2). Practitioners might care because understanding how to design and optimize these systems could lead to more efficient and effective AI.

This paper introduces CaPtain, a post-training pipeline that enables small language models to become capable robot agents that can write code against robot APIs and read back the results. A practitioner might care about this work because it demonstrates a novel approach to training robot agents using language models, which could be applied to various robotics tasks.

This paper investigates the problem of training-inference mismatch in reinforcement learning for large language models, where the training and inference processes can produce different results, leading to suboptimal training. A practitioner might care about this issue because it can lead to poor performance and instability in training, especially when using off-policy methods.

This paper develops a new method to evaluate proactive agents, which are systems that decide when and how to assist without being asked. Practitioners in AI and robotics might care because it helps them understand how well these systems work and make them more effective.

This paper develops a framework called CodeSpecWeave to help coding agents create more realistic and comprehensive task specifications for long-horizon software development. Practitioners may care about this because it could improve the performance and reliability of coding agents in real-world development scenarios.

This paper develops an online reinforcement learning framework that trains agents to navigate UIs on live iOS apps, addressing limitations of existing models. Practitioners might care about how this approach can improve the performance and generalizability of UI navigation models in real-world applications.

This paper presents a unified AI model that enables aerial robots to understand environments, navigate, and control themselves using a shared vision-language representation. Practitioners might care about how to build AI systems that can perform multiple tasks, such as perception and control, without relying on external resources.

This paper develops a single AI agent that can edit long-form videos from narration, improving the efficiency and quality of video production. Practitioners in the video production industry may care about this research as it has the potential to automate video editing tasks and reduce production costs.

This paper develops a robot policy that allows for real-time control over quality attributes like speed and smoothness, and shows that this approach can improve both task success and quality in real-world deployment scenarios.