Hugging Face daily papers, ranked by community upvotes. Summaries and key terms are written by Workers AI from the abstract — click a term to filter, or search below (matches full abstracts and authors too).
This paper introduces Kimi K3, a large-scale, open-source AI model that achieves state-of-the-art performance on a range of tasks, including vision and coding, and is designed to be more efficient and scalable than previous models, making it a promising candidate for real-world applications.
This paper introduces Qwen-UI-Agent, a type of artificial intelligence system that can perform tasks on various devices, such as smartphones and computers, and improve its abilities on its own. Practitioners might care about this research because it aims to create more practical and autonomous AI systems that can be used in real-world scenarios.
This paper creates a system to help scientists and AI agents find and verify specific information in chemistry literature by converting research papers into claims, which are then linked together to form a network of evidence. Practitioners might care because this system could improve the efficiency and accuracy of literature synthesis in chemistry research.
This paper introduces a new type of AI model called memory foundation models, which allows the model to learn and retain information internally, rather than relying on external memory modules. This could be useful for practitioners who want to build more efficient and flexible AI agents.
This paper presents a video world model that can interact with a virtual environment in real-time, using a single desktop GPU. Practitioners might care about the potential applications of this technology in areas like virtual reality, gaming, and robotics.
This paper trains an AI model to improve itself in the process of building AI, with a focus on machine learning engineering, and shows promising results in various benchmarks. Practitioners may care about this research as it could lead to more efficient and autonomous AI development.
This paper introduces PhiZero, a world model that uses physical language to predict how the physical world evolves, allowing for more explicit reasoning and potentially more realistic simulations. Practitioners might care because this approach could lead to more realistic and interactive world modeling in applications like robotics, video games, and virtual reality.
This paper develops a new approach to understanding videos by predicting when specific events or evidence occur within the video. Practitioners working on video analysis and AI models might care about this research because it could lead to more accurate and robust video understanding systems.
This paper introduces AREX, a self-improving agent that recursively refines its answers by verifying intermediate results and using the partially verified state to guide subsequent refinement, aiming to improve the efficiency of deep research. Practitioners might care about AREX's approach to reducing the cost of discovery and verification in complex research tasks.
This paper develops a system that allows robots to learn manipulation policies using high-fidelity data without the need for real-robot teleoperation, which is expensive to scale. Practitioners can use this approach to train robots for tasks like precision insertion with high accuracy and success rates.
This paper develops a system to extract and represent skills from human-created resources like videos, code, and articles, allowing software agents to learn from these multimodal inputs. Practitioners might care about this work because it could enable more effective training of agents in various domains.
This paper introduces RAGU, an open-source GraphRAG engine that improves large language models with structured knowledge by separating extraction and consolidation, and trains a compact extractor that outperforms larger models on knowledge-graph construction and GraphRAG tasks. Practitioners might care because RAGU can efficiently generate more accurate and complete context for tasks like factoid-level evidence recall and multi-hop question answering.
This paper creates a platform to help large language models generate code for data pipelines, which can then be edited and used to automate data processing workflows. Practitioners might care about this because it can help reduce the time and cost of developing and maintaining these pipelines.
This paper introduces TurboVLA, a new vision-language-action model that reduces computation and memory overhead by directly exchanging information between visual observations and language instructions, allowing for faster and more efficient robotic manipulation. Practitioners might care about this approach for building more efficient and effective VLA models.
This paper introduces JarvisHub, a system that enables creative AI agents to work on long-term, multimodal projects by providing a shared workspace and context, allowing for more realistic and sustainable creative automation. Practitioners might care because it has the potential to improve the efficiency and effectiveness of creative workflows in industries such as design, advertising, and entertainment.
This paper improves autoregressive video distillation methods by aligning the initialization and distribution matching stages, focusing on matching the target distribution's mode coverage rather than just visual quality. Practitioners can benefit from this approach to generate higher-quality videos with better diversity and coverage.
This paper develops a new approach to guiding corpus interaction in agentic search, which uses relevance to improve the accuracy and efficiency of search agents in complex question answering and reasoning tasks. Practitioners may care about this research if they want to build more effective search systems that can quickly and reliably retrieve relevant information.
This paper introduces a framework called DeepSearch-Evolve, which helps train self-improving web agents by iteratively refining their performance using their own experience. Practitioners might care because this approach can lead to more efficient and effective agents that can learn from their own mistakes.
This paper introduces a framework called EvolvingWorld that allows characters and worlds to evolve together over time in interactive literary worlds, enabling more realistic and coherent simulations. Practitioners interested in developing more immersive and dynamic interactive stories might care about this approach.
This paper develops a system called CodeNib that helps coding agents navigate and retain context in evolving repositories, allowing them to work more efficiently. Practitioners in the field of artificial intelligence, software development, and human-computer interaction may care about this research because it can improve the performance and productivity of coding agents.
This paper proposes a method to improve language model training by allocating credit to individual tokens within a response, allowing for more nuanced evaluation of model performance. Practitioners may care about this method as it can lead to better language model performance, especially in tasks that require specific formatting or semantic choices.
This paper introduces a faster version of a generative world renderer that can run in real-time, allowing for interactive play and user control. A practitioner might care about this if they want to create immersive games or simulations.
This paper investigates how to improve the adaptation of diffusion models in a way that doesn't rely on a classifier, and how to address a problem where the model can't accurately learn from its teacher. Practitioners might care about this because it could lead to more effective knowledge transfer in machine learning applications.
This paper introduces a new method to improve large language models (LLMs) called RLSVR, which uses a task-transformation technique to generate self-verifiable rewards, enabling LLM self-improvement on open-ended tasks. Practitioners might care about this because it could lead to more reliable and scalable self-improvement methods for LLMs.
This paper helps us understand how text-to-image diffusion transformers work by analyzing the role of "template tokens" in generating images from text prompts. Practitioners might care because it shows how to improve the efficiency of these models without sacrificing their performance.
This paper evaluates whether vision-language models can act through a physical body and how they can make decisions about what actions to take, without being hindered by issues like balance and motor control. Practitioners in AI and robotics might care because understanding how models interact with their physical bodies can help improve their ability to navigate and interact with the world.
This paper proposes a new pruning method for coding agents that prunes tool outputs directly inside the agent, rather than relying on a separate code classifier, and shows it can save up to 39% of tokens while preserving task quality.
This paper proposes a new method to improve the performance of large language models on knowledge-intensive tasks by distilling knowledge from proprietary models and using reinforcement learning. Practitioners may care about this approach because it can help bridge the gap between proprietary and open-source models, leading to more effective and robust AI systems.
This paper introduces VideoCoCo, a system that uses executable Blender code to generate physically consistent videos from text prompts, and shows promising results in improving video generation quality. Practitioners might care about this research if they want to generate realistic videos that are also physically plausible.
This paper introduces Open-AoE, an open dataset and toolchain for egocentric manipulation learning, providing a scalable and structured platform for training embodied models. Practitioners can use Open-AoE to improve their robot learning models, especially those focused on human-robot interaction and embodied intelligence.
This paper introduces Mage-Flow, a compact model for generating and editing high-resolution images, which can be trained efficiently and deployed on a single GPU. Practitioners might care about the potential applications of this model in interactive image editing and generation tasks.
This paper introduces a vision-language-action model that can perform mobile manipulation tasks in unseen environments with minimal training data, and how it can be scaled up to achieve better performance. Practitioners might care about this model for building robots that can adapt to new tasks with minimal fine-tuning.
This paper presents a new AI approach to recovering editable design files from images, which could save time and costs in design workflows, allowing designers to work more efficiently and effectively.
This paper optimizes the training of massive neural networks on a special-purpose hardware, the Ascend SuperPOD, to improve performance and stability. Practitioners in AI/ML model training might care about the techniques and results presented here for large-scale model training on non-GPU hardware.
This paper introduces a new framework for training educational language models, specifically designed to evaluate their ability to understand curriculum knowledge and its visual presentation. Practitioners might care about this work because it aims to improve language models' performance in educational settings.
This paper provides a comprehensive survey of progress reward modeling in robotic learning, aiming to bridge the gap in the field by offering a unified framework for understanding progress rewards. Practitioners in robotics and AI can care about this paper because it helps them understand the different approaches to progress rewards and how to evaluate their effectiveness.
This paper introduces a method called DecoEvo, which helps large language models improve by co-evolving a solver skill and a rubric-generator skill in a way that's more efficient and effective. Practitioners might care about this because it could lead to better performance and more reliable optimization in open-ended tasks.
This paper develops a video personalization method that focuses on human-object interactions, aiming to improve the accuracy of video generation by better understanding human-object relationships and incorporating intra-subject references. Practitioners may care about this research as it could lead to more realistic and engaging video content.
This paper develops a new framework for world modeling that takes into account the mental state of agents, which is essential for predicting human decisions. Practitioners caring about human decision-making and planning might find this research useful.
This paper introduces a new approach to training computer-use agents by directly interacting with the underlying program state, rather than relying on visual perception. By doing so, agents can reason more effectively and make fewer mistakes, which can lead to significant improvements in performance.
This paper develops a method to scale up language models by increasing their memory capacity, allowing for better performance and more efficient use of parameters. Practitioners may care about this research as it could lead to more powerful and efficient language models for applications like language translation and text generation.
This paper proposes a new approach for embodied visual tracking that first identifies a target described in natural language and then tracks it using a single camera. Practitioners in robotics and autonomous systems might care about this work because it shows promise for robust and reliable visual tracking in real-world applications.
This paper introduces a benchmark to measure how well large language models (LLMs) can prepare training data, and how well they evaluate the quality of that data. Practitioners might care because improving data preparation can lead to better model performance.
This paper introduces a vision-tactile-language-action model that can perform fine-grained manipulation with tactile perception and control, and improve its policy offline from stored data. Practitioners may care because this model can be used to create more versatile and accurate tactile-driven manipulation policies.
This paper develops a system called AlayaWorld that can generate interactive virtual worlds from text, images, or videos, allowing for customizable and evolving environments. Practitioners in areas like game development, virtual reality, or interactive storytelling might care about this research for its potential to streamline the creation of immersive experiences.
This paper proposes a new approach to agentic visual reasoning, which helps large language models (LLMs) perform better on complex tasks by using tools more efficiently. Practitioners might care about this research because it aims to improve the performance of LLMs on challenging problems.
This paper compares the performance of different retrieval-augmented generation (RAG) paradigms at varying corpus sizes, finding that BM25 outperforms others at larger scales, but not at smaller ones, and that lexical retrieval is the strongest scalable default.
This paper introduces Visual Contrastive Self-Distillation, a method that removes the need for external teacher information and privileged answers in on-policy self-distillation, allowing for simpler and more efficient learning. Practitioners might care about this approach because it can lead to better performance in language models.
This paper introduces a new benchmark (CLBench-V) to evaluate multimodal context learning in AI models, which can learn from various types of context in real-world tasks. Practitioners can care about this research because it aims to improve the ability of AI models to understand and apply context in complex, real-world scenarios.
This paper introduces a benchmark to evaluate video generation models' ability to reason about physical laws, which is crucial for creating reliable world simulators. Practitioners caring about developing more realistic and physically intelligent AI models will find this research valuable.
This paper proposes a new method for training large language models in open-ended domains, using evolving contexts as in-training supervision to capture task preferences. Practitioners may care about this approach because it can lead to better performance on open-ended tasks.
This paper presents a fast and efficient method for generating high-quality 3D meshes with artist-style topology, which can be used for interactive asset creation in film, gaming, and interactive 3D applications. Practitioners might care about this paper because it provides a practical solution for generating meshes quickly and with high precision.
This paper investigates how Diffusion Language Models (DLMs) internally represent time and how this representation can be used to modulate the model's behavior. Practitioners might care because understanding how DLMs process time could lead to more controllable and interpretable models.
This paper introduces a benchmark to evaluate the performance of models in editing multi-person images, focusing on anatomical and geometric accuracy. Practitioners in the field of computer vision and image editing might care about this research as it aims to improve the quality of human-like images with multiple people.
This paper introduces a new dataset called ACE-Data-0, which provides a comprehensive and synchronized record of human behavior in real-world environments, capturing various aspects of embodied intelligence such as perception, action, and interaction. Practitioners in the field of embodied AI and machine learning can use this dataset to develop more sophisticated models that can learn from human demonstrations and perform tasks that involve complex manipulation, locomotion, and interaction.
This paper develops a framework to evaluate spatial cognition in image-generation models by asking them to draw or mark answers in a visual space, rather than relying on text-based inputs. Practitioners might care about this research to better understand how image-generation models think spatially and to improve their performance on tasks that require spatial reasoning.
This paper introduces a new video diffusion transformer called SANA-Video 2.0 that can generate high-quality videos efficiently, using a hybrid approach that combines linear attention with attention residuals. Practitioners might care about this paper because it presents a scalable and efficient method for generating high-resolution videos.
This paper develops a new framework called Skill Self-Play that helps large language models (LLMs) improve their capabilities by co-evolving skills that balance task diversity and verification reliability. Practitioners might care because this approach can lead to significant performance gains for LLMs.
This paper develops a large audio language model that can answer questions with specific timestamps, improving its ability to understand long audio recordings. Practitioners in audio and speech recognition may care about this development as it enables more accurate and context-specific information retrieval from audio data.
This paper introduces RynnBrain 1.1, a family of large-scale embodied foundation models that can perform tasks like spatial reasoning, localization, and planning, and shows promising results in real-world robot experiments. Practitioners may care about the potential of these models for robot manipulation and control.
This paper improves the efficiency of video generation by reducing the computational load of attention, a key bottleneck in diffusion transformers. Practitioners can benefit from the speedup achieved by Sol-Attn, which enables faster video generation and editing without compromising visual quality.
This paper proposes a new method for evaluating role-playing agents (RPAs) that simulates user experiences in interactive role-playing to provide more accurate and personalized assessments of RPA capabilities. Practitioners might care about this because it helps them evaluate RPAs more effectively, leading to better systems that meet user needs.
This paper creates a new AI model that can edit and generate videos without needing masks, and can also learn to mimic image editing capabilities. Practitioners might care about this because it could lead to more diverse and realistic video editing data, and enable AI models to understand and generate human-like video editing instructions.
This paper develops a new method to improve the stability of asynchronous reinforcement learning by adapting the trust region to account for staleness, which is a common problem in this field. Practitioners might care about this because stable reinforcement learning can lead to better performance and more efficient training.
This paper develops a multilingual speech recognition model for underrepresented languages, addressing the issue of data scarcity and uneven performance, and shows promising results in controlled comparisons.
This paper proposes a new training method for autoregressive video diffusion models called Self Gradient Forcing, which helps them better remember and use past information to generate future frames. Practitioners might care about this because it could lead to more realistic and stable video extrapolation.
This paper proposes a hierarchical structure for organizing data sources for embodied manipulation, aiming to balance scalability and robot alignment. Practitioners might care about this work if they're building or deploying embodied agents that require diverse and high-quality data.
This paper introduces a framework to audit system prompts in AI applications, examining how developers design and use these prompts to govern the behaviors of foundation models. Practitioners should care because the lack of transparency and accountability in system prompts can erode trust in AI systems.
This paper develops a new framework for evaluating and selecting document sets for AI agents, considering the interactions between documents, and proposes a training-free method that achieves the best downstream generation performance with fewer documents and search rounds.
This paper introduces a new framework for building AI agents in Python, allowing developers to write agents that are both deterministic and model-agnostic. Practitioners might care because this framework could simplify the development of reliable AI agents for various applications.
This paper introduces Molt, a lightweight PyTorch framework for agentic reinforcement learning that aims to simplify the development process by reducing the overhead of algorithm modifications and framework changes. Practitioners might care about Molt because it can help them build and train reinforcement learning models more efficiently.
This paper addresses a problem in on-policy distillation where a student model can get stuck on a wrong path, and proposes a new method called Relay-OPD that helps the student model recover by briefly taking over at certain points to produce a new trajectory. Practitioners might care about this because it could lead to better performance and more efficient training in models like language generators or math solvers.
This paper evaluates how well AI systems can retrieve information from their memory when the information is related to but not directly connected to the query, and how this performance changes when the information is stored and then retrieved. Practitioners might care because understanding this blind spot can help improve the performance of AI systems in real-world applications.
This paper proposes a method to improve reinforcement learning with verifiable rewards by using game solvers to provide turn-level credit to agents, allowing them to learn more effectively. Practitioners might care about this approach because it could lead to more robust and efficient AI decision-making.
This paper introduces Oxygen-TryOn, a new AI model that can generate photorealistic images of people wearing any fashion item, in any setting. Practitioners in the fashion industry might care about this model because it can revolutionize virtual try-on, allowing for more realistic and diverse scenarios.
This paper develops a new type of AI model that can understand and interact with both images and text in real-time, without needing a huge amount of training data. Practitioners may care about this model because it can be used in applications where fast and efficient visual perception is required.
This paper introduces a new model, N_0-TWAM, that can predict both future vision and future contact in tasks that involve contact-rich manipulation. Practitioners in robotics and manipulation might care because this model can improve the ability of robots to perform complex tasks that require both vision and tactile feedback.
This paper introduces a benchmark called ActiveVision to measure whether large language models (LLMs) exercise active observation, and finds that current LLMs are not robust in this regard, performing poorly on tasks that require repeated visual perception.
This paper introduces a new task called multi-reference image-grounded video captioning, where models must describe video content while referencing multiple images. Practitioners might care because this can improve the accuracy and faithfulness of video captions in real-world applications.
This paper proposes a new type of AI system that can watch and remember things over long periods, and use that memory to reason about the world. Practitioners might care because this could lead to more capable assistants that can learn and adapt over time.
This paper proposes a new method for reinforcement learning in large language models, called Group Entropy-Controlled Policy Optimization (GEPO), which helps balance exploration and exploitation by controlling entropy levels across different tasks. Practitioners might care about GEPO because it can lead to more balanced and task-specific exploration levels.
This paper introduces SeerGuard, a safety framework that helps mobile GUI agents make safe decisions by predicting potential outcomes of their actions before they are executed. Practitioners caring about the safety of autonomous mobile agents might be interested in this research.
This paper introduces Tencent WorkBuddy Bench, a benchmark for coding agents that tests their performance across multiple domains, including code, web, office, and security. Practitioners might care about this benchmark because it provides a standardized way to evaluate and compare the performance of coding agents.
This paper teaches language models to synthesize complete software programs from scratch, which is a challenging task. Practitioners might care because this can improve the models' performance on software engineering tasks.
This paper evaluates how multimodal large language models use intermediate visual states during reasoning and finds that these visual states are not as crucial as previously thought, but can still impact model performance under certain conditions. Practitioners might care because understanding how these models use visual states can help improve their performance and reliability.
This paper proposes a method to combine reinforcement learning with verifiable rewards and on-policy distillation to improve performance on complex tasks, and shows that this method can lead to more stable training and better results.
This paper investigates how text conditioning affects visual generation and proposes ways to improve it, leading to better performance on various benchmarks. Practitioners might care about the findings to develop more effective text-to-image models.
This paper proposes a new regularization technique for world models to improve planning performance by aligning latent samples with quantiles of a Gaussian distribution, which helps control heavy-tailed deviations. Practitioners caring about efficient planning in complex environments may find this approach useful.
This paper introduces a new method called REBASE that allows for training-free in-context segmentation, enabling the introduction of new object categories at inference time. Practitioners might care because it eliminates the need for retraining and reduces memory overhead, making it a more efficient approach for real-world applications.
This paper investigates how Large Language Models (LLMs) handle dynamic user intent in conversations, where users' goals change over time, and finds that current LLMs struggle with this capability, leading to significant performance drops when evaluated in a more realistic setting.
This paper explores how to improve long-horizon planning in AI agents, which is crucial for foundation models. Practitioners might care because better planning abilities can lead to more effective and efficient decision-making in complex environments.
This paper introduces Qwen-Music, a music generation model that can produce highly musical and high-fidelity songs from text descriptions and existing songs. Practitioners in music generation and AI may care about this research for its potential to create realistic and engaging music.
This paper introduces AgentDebugX, an open-source toolkit that helps debug and recover from failures in large language model (LLM) agents, making it easier to identify and fix errors. Practitioners can benefit from AgentDebugX as it provides a framework for detecting, attributing, and recovering from errors, which can improve the accuracy and reliability of LLM agents.
This paper investigates how to scale large language models to also understand and interact with the physical world by training them on multiple types of data from scratch, allowing them to reason about both text and images. Practitioners might care because this could lead to more robust and versatile AI systems that can handle a wider range of tasks.
This paper develops a new generative model called OmniVAE that can jointly generate synchronized audio and video with fine-grained cross-modal correspondence, and its approach is expected to improve the quality of downstream text-to-audio-video generation tasks.
This paper develops a new method for improving reasoning language models, called β-OPSD, which combines policy optimization and self-distillation to improve stability and performance. Practitioners might care about this method because it provides a more efficient and effective way to improve language model reasoning abilities.
This paper develops a framework, SpatialCLI, to help vision-language models (VLMs) better understand and use visual tools to make better decisions. By training VLMs to reason with spatial tools and then internalize those capabilities, SpatialCLI can improve the performance of VLMs in tasks that require visual reasoning.
This paper develops a new framework, SciForma, to generate scientific diagrams that accurately represent research logic, which is crucial for scientific communication and methodology validation. Practitioners can benefit from SciForma's ability to ensure structural fidelity in diagram generation, which can improve the accuracy and reliability of scientific research.
This paper proposes a new approach to managing the context of AI agents, which is crucial for their performance in production environments. By actively managing what an agent holds in mind, the agent can avoid accumulating unnecessary information and reduce costs, leading to improved accuracy and efficiency.
This paper proposes a new AI method to improve the way images are displayed on a screen, making them look more like the real world, and why a practitioner might care because better image display can improve how we interact with digital content.