Firehose

This paper proposes a runtime system called REA that enables the coordination of fast, reactive policies (like those used in vision-language models) with slower, deliberative brain models, allowing them to work together effectively. Practitioners might care about REA because it can improve the performance and efficiency of embodied systems like robots.

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 develops a new framework called Latent Reasoning in Continuous space (LARC) that helps Unified Multimodal Models better use language to generate images, leading to improved image quality and controllability. Practitioners might care about this research if they want to create models that can generate images that are more coherent and flexible.

This paper creates a voice agent tool called VoxLock that can automatically identify and transcribe the speaker's voice without needing to enroll them beforehand, which is useful for applications like customer service. Practitioners might care about VoxLock because it can improve the accuracy and efficiency of voice-based interactions.

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 develops a new framework called HiFOR that helps vision-language-action models predict and plan for future events in a more accurate way, which can improve their performance in tasks like robotic manipulation. Practitioners can care about this research because it could lead to more reliable and effective robots that can adapt to changing situations.

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 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 introduces EditAgent, a new approach to agentic image editing that allows for more flexible and reliable editing by combining general-purpose and specialized methods, and verifying candidate edits with off-the-shelf judges. Practitioners may care because it can improve the efficiency and quality of image editing tasks.

This paper proposes a method to improve text-to-image generation models by matching the generated distribution with a reference distribution, allowing for more accurate rendering of details specified in the prompt. Practitioners may care about this approach as it can lead to better performance on image generation tasks.

This paper introduces Glance, a model that combines generation and reranking into a single model, which can improve performance and reduce memory usage. Practitioners might care about this because it could lead to more efficient and effective AI-powered question-answering systems.

This paper introduces Youtu-Parsing-Omni, a system that can parse documents, images, audio, and video into structured records, allowing agents to access and cite evidence across multiple modalities. Practitioners might care about this research because it provides a new approach to multimodal parsing that can improve the performance of agents in various applications.

This paper proves the Platonic Representation Hypothesis by showing that models trained on different modalities (e.g. vision and language) converge to the same representation of the world as they scale, but with a specific condition (good views). Practitioners might care because this has implications for building more efficient and accurate multimodal AI models.

This paper shows that foundation models can be trained with a single algorithm, which can improve the accuracy-compute tradeoff and task coverage. Practitioners might care because this approach can simplify and streamline the training process for foundation models.

This paper shows how to make existing autoregressive image models generate images in any order, without having to retrain them from scratch, which could improve their flexibility and usefulness for tasks like inpainting and editing. Practitioners might care about this because it could make their models more versatile and efficient.

This paper introduces a new dataset and benchmark for multimodal interleaved generation, which is the process of jointly generating text and images that are naturally intertwined like in real-world content. Practitioners might care about this because it could lead to better AI models that can create more realistic and engaging multimodal content.

This paper introduces A-RSI, a method that helps AI models decide when to stop learning from their current experience and introduce new tasks, without relying on external validation or human feedback. By monitoring the model's learning signal and accuracy, A-RSI can automatically refresh the task pool, leading to improved performance.

This paper improves a common technique in language model training called Multi-Token Prediction (MTP) by removing a noisy component that interferes with learning, resulting in faster and more accurate predictions. Practitioners may care because this improvement can lead to better performance in applications such as language translation and text generation.

This paper introduces SciGame, a controlled benchmark for evaluating language model agents in scientific discovery, and finds that familiar priors can hinder performance, adaptive experimentation can be beneficial but also unfair, and that agents struggle to induce exact symbolic laws in unfamiliar settings.

This paper introduces a new AI model called Venus-DefakerOne2 that can detect a wide range of fake images, videos, and documents, and is useful for developers who need to build robust anti-forgery systems.

This paper presents a new approach to building large language models by reducing the parameter count while maintaining performance, and analyzes what went wrong with the approach, providing valuable insights for improving future models. A practitioner might care about this paper if they're interested in building efficient and effective language models.

This paper helps prevent large language models from becoming unsafe by identifying training data that causes them to behave badly, even if the data itself looks harmless. Practitioners can use this technique to build safer language models that are more reliable and trustworthy.

This paper introduces WorkFactory, a framework for creating executable tasks for training agents, which can help improve the reliability of expert demonstrations and scale agent training. Practitioners might care about how to create coherent and executable tasks for their agents, especially in complex environments.

This paper introduces WebAnything, a framework that automatically generates reusable website environments from real URLs, allowing for reproducible training and evaluation of web agents. Practitioners might care about this because it simplifies the creation of diverse and realistic environments for training AI models.

This paper proposes a new method to process images only when necessary in diffusion vision-language models, reducing unnecessary computations and maintaining high accuracy. Practitioners caring about efficient model performance might be interested in this approach.