🔥 DataClaw0: Agentic Tailoring Multimodal Data from Raw Streams

💡 The paper introduces a new paradigm called Agentic Data Tailoring which aims to structure high entropy multimodal data streams using learnable data processing. The problem addressed is that existing methods for processing unstructured multimodal data are costly and inefficient, failing to unlock the deep procedural logic embedded in the data. The proposed method uses a two stage pipeline, first generating a large scale dataset using generative semantic synthesis and deterministic factual anchors, and then training a model called DataClaw0-9B using supervised fine tuning and group relative policy optimization. The DataClaw0-9B model is able to achieve robust alignment with complex refinement and tailoring intents. The paper also introduces a new benchmark called DataClaw0-val for evaluating data refinement capabilities. The results show that the DataClaw0 model is able to deliver high information density tailored data, facilitating efficient model adaptation to new tasks with limited training data. The evaluations are done on tasks such as video generation, real world visual question answering, and GUI navigation, and the results confirm the effectiveness of the proposed method. Overall, the paper proposes a new paradigm for data processing and provides a method and benchmark for evaluating data refinement capabilities, with results showing the potential of the proposed method for efficient model adaptation and high quality data processing.

📅 Published on Jun 19

🔗 Links:
• GitHub: https://github.com/huggingface
• arXiv: https://arxiv.org/abs/2606.21337
• PDF: https://arxiv.org/pdf/2606.21337
• Project Page: https://czjdsg.github.io/MakeAnyData/#cases

━━━━━━━━━━━━━━━━━━━━━━━━
📢 By: https://xn--r1a.website/PaperNexus

#MultimodalDataProcessing #AgenticDataTailoring #LearnableDataProcessing #MultimodalStreamAnalysis #DeepProceduralLogicExtraction

🔥 EnterpriseClawBench: Benchmarking Agents from Real Workplace Sessions

💡 The paper introduces EnterpriseClawBench, a benchmark for evaluating enterprise agents based on real-world sessions. Enterprise agents are increasingly used in workspaces to read files, invoke tools, and deliver business artifacts. However, existing evaluation metrics are limited, focusing on single performance scores. To address this, the authors created EnterpriseClawBench, which consists of 852 reproducible tasks derived from a large archive of workplace sessions. Each task is paired with relevant information such as fixtures, prompts, and semantic rubrics.

The benchmark is not publicly released due to the proprietary nature of the data, but the construction and evaluation protocol is made available. The authors used this benchmark to evaluate the performance of various agent configurations and found that the best configuration achieved a score of 0.663, indicating that there is still significant room for improvement.

The key contribution of this paper is the introduction of a comprehensive evaluation protocol that goes beyond single performance scores. The authors argue that enterprise agent evaluation should consider multiple factors, including harness-model combinations, artifact delivery, visual quality, cost, runtime, and skill-transfer behavior. This approach provides a more nuanced understanding of an agent's capabilities and limitations, allowing for more effective evaluation and development of enterprise agents.

Overall, the paper highlights the need for more comprehensive evaluation metrics for enterprise agents and provides a benchmark and evaluation protocol to support this goal. The results demonstrate the challenges of developing effective enterprise agents and the importance of considering multiple factors in their evaluation.

📅 Published on Jun 22

🔗 Links:
• GitHub: https://github.com/huggingface
• arXiv: https://arxiv.org/abs/2606.23654
• PDF: https://arxiv.org/pdf/2606.23654
• Project Page: https://frontisai.github.io/EnterpriseClawBench/

━━━━━━━━━━━━━━━━━━━━━━━━
📢 By: https://xn--r1a.website/PaperNexus

#EnterpriseAgents #WorkplaceAutomation #BenchmarkingAI #ArtificialIntelligenceInBusiness #EnterpriseArtificialIntelligence

🔥 UniverSat: Resolution- and Modality-Agnostic Transformers for Earth Observation

💡 The paper introduces UniverSat, a new approach to applying Vision Transformers to Earth Observation data. The problem with current Vision Transformers is that they rely on rigid patch projectors, which makes it difficult to transfer them to Earth Observation tasks where the input data can vary widely in terms of modality, scale, and resolution. To address this issue, the authors propose a Universal Patch Encoder that can map patches from arbitrary spatial, spectral, and temporal resolutions, and from both optical and non-optical sensors, into a shared embedding space using a shared set of weights. This allows a single model to be trained on heterogeneous multimodal data using self-supervision, resulting in robust and sensor-agnostic spatial features. The authors validate their approach by achieving strong results on classification and segmentation tasks using standard Earth Observation benchmarks. The key contribution of UniverSat is its ability to enable resolution- and modality-agnostic spatial feature extraction, making it a versatile and effective tool for Earth Observation tasks. The authors make their code and models available for further research and development.

📅 Published on Jun 22

🔗 Links:
• GitHub: https://github.com/huggingface
• Project Page: https://huggingface.co/papers?q=patch%20projectors
• arXiv: https://arxiv.org/abs/2606.23503
• PDF: https://arxiv.org/pdf/2606.23503

🤖 Models citing this paper:
• https://huggingface.co/g-astruc/UniverSat

━━━━━━━━━━━━━━━━━━━━━━━━
📢 By: https://xn--r1a.website/PaperNexus

#EarthObservation #VisionTransformers #MultimodalLearning #RemoteSensing #GeospatialAnalysis

🔥 World Action Models: A Survey

💡 The paper World Action Models A Survey provides a comprehensive overview of World Action Models, which are predictive action systems that generate future states for decision making. These models balance representational richness against computational constraints, and recent developments have led to a blurring of boundaries among various related models. The survey aims to clarify these boundaries and provide a common account of the field.

The authors organize existing works into two complementary views. The first view examines what each method is required to generate, including rendered futures, latent futures, and video generation free action reasoning. The second view decomposes each method into its predictive substrate, backbone, action coupling, and deployment regime. This anatomy allows for a unified discussion of key aspects such as interactability, causality, persistence, physical plausibility, and generalization.

The survey reveals a consistent design pattern in World Action Models, where design choices trade representational richness against compute, memory, latency, and action label cost. The authors find that the field is moving towards methods that generate less of the future while preserving what is required for control. The survey provides a clear and unified account of the field, covering data, evaluation, and open challenges, and provides a foundation for future research in World Action Models.

The main contributions of the paper are to clarify the boundaries and definitions of World Action Models, to provide a comprehensive overview of existing works, and to identify a consistent design pattern in the field. The survey also highlights the key challenges and open issues in World Action Models, including the need for more efficient and effective methods that balance representational richness against computational constraints. Overall, the paper provides a valuable resource for researchers and practitioners in the field of World Action Models, and helps to advance the state of the art in predictive action systems.

📅 Published on Jun 18

🔗 Links:
• GitHub: https://github.com/huggingface
• arXiv: https://arxiv.org/abs/2606.20781
• PDF: https://arxiv.org/pdf/2606.20781
• Project Page: https://world-action-models.github.io/

━━━━━━━━━━━━━━━━━━━━━━━━
📢 By: https://xn--r1a.website/PaperNexus

#WorldActionModels #PredictiveActionSystems #DecisionMakingModels #ActionReasoning #ArtificialIntelligenceModels

🔥 Tmax: A simple recipe for terminal agents

💡 The paper presents a novel approach to training terminal agents using reinforcement learning, called Tmax. Terminal agents are a popular application of language models, but their training has been hindered by the lack of simple and effective methods, limited data, and challenging benchmarks. The authors address these issues by introducing a simplified recipe for training terminal agents, which achieves superior performance with fewer parameters than previous methods.

The method involves generating a large dataset of terminal environments using a novel taxonomy that combines difficulty control, personas, and verifier diversification. This allows for the cheap generation of large amounts of data, which is then used to train open-weight models using reinforcement learning with a simple outcome-only recipe.

The results show that Tmax achieves 27 percent on Terminal-Bench 2.0 with only 9 billion parameters, outperforming much larger models from prior work. The authors also release their terminal dataset, which is over 2.5 times larger than previously released terminal-agent datasets, as well as their models and code as a strong baseline for future academic work on terminal agents.

The contributions of the paper are threefold. First, it presents a simple and effective recipe for training terminal agents, which can be used as a baseline for future work. Second, it introduces a novel taxonomy for generating terminal environments, which allows for the cheap generation of large amounts of data. Third, it releases a large dataset of terminal environments, models, and code, which can be used by other researchers to advance the field of terminal agents. Overall, the paper provides a significant contribution to the field of terminal agents and reinforcement learning, and has the potential to advance the state of the art in this area.

📅 Published on Jun 22

🔗 Links:
• GitHub: https://github.com/huggingface
• arXiv: https://arxiv.org/abs/2606.23321
• PDF: https://arxiv.org/pdf/2606.23321
• Project Page: https://wai-org.com/blog/tmax/

🤖 Models citing this paper:
• https://huggingface.co/allenai/tmax-27b
• https://huggingface.co/allenai/tmax-9b
• https://huggingface.co/allenai/qwen35-9b-openthoughts

📊 Datasets citing this paper:
• https://huggingface.co/datasets/allenai/TMax-15K
• https://huggingface.co/datasets/allenai/tmax-15k-open-instruct
• https://huggingface.co/datasets/allenai/tmax-sft

━━━━━━━━━━━━━━━━━━━━━━━━
📢 By: https://xn--r1a.website/PaperNexus

#TerminalAgents #ReinforcementLearning #LanguageModels #TmaxAlgorithm #AgentTrainingMethods

🔥 Efficient Guided Generation for Large Language Models

💡 The paper presents an efficient method for guiding large language model text generation using regular expressions and context-free grammars. The problem addressed is that guided generation can be impractical due to significant overhead. The authors propose an approach that adds minimal overhead to the token sequence generation process. This method makes guided generation feasible in practice. The approach is implemented in the open source Python library Outlines, providing a practical solution for efficient guided generation. The results indicate that the method is effective, allowing for guided generation with little to no overhead, which is a significant contribution to the field of natural language processing.

📅 Published on Jul 19, 2023

🔗 Links:
• GitHub: https://github.com/huggingface
• arXiv: https://arxiv.org/abs/2307.09702
• PDF: https://arxiv.org/pdf/2307.09702

━━━━━━━━━━━━━━━━━━━━━━━━
📢 By: https://xn--r1a.website/PaperNexus

#LargeLanguageModels #GuidedTextGeneration #RegularExpressions #ContextFreeGrammars #EfficientGenerationMethods

🔥 OpenRath: Session-Centered Runtime State for Agent Systems

💡 The paper introduces OpenRath, a programming model for multi-agent systems that addresses the issue of fragmented runtime state. In current agent systems, various aspects such as transcripts, tool effects, and memory events are recorded separately, making it difficult to inspect or reproduce the system's behavior. OpenRath solves this problem by introducing a central runtime abstraction called Session, which is a first-class value that can be passed between agents and workflows.

The Session abstraction is designed to be branchable, inspectable, replayable, backend-aware, and composable, allowing it to record various execution state information such as conversation chunks, sandbox placement, and tool evidence. This enables explicit fork, merge, and replay operations as runtime operations rather than reconstructing states from external traces.

OpenRath also defines other key concepts such as Sandbox, Tool, Agent, Memory, Workflow, and Selector, which work together to provide a comprehensive programming model for multi-agent systems. The Selector is particularly important as it turns control flow into runtime-routed decisions.

The paper presents the programming model, architecture, and evidence protocol of OpenRath, and claims that the Session abstraction provides agent systems with a first-class runtime value for auditable composition. The results of this work are limited to controlled runtime properties, and further evaluation is needed to compare the performance of OpenRath with other systems and to assess its availability and quality.

Overall, OpenRath contributes a novel programming model for multi-agent systems that provides a unified and explicit way to manage runtime state, making it easier to inspect, reproduce, and debug the behavior of these systems.

📅 Published on Jun 17

🔗 Links:
• GitHub: https://github.com/huggingface
• arXiv: https://arxiv.org/abs/2606.19409
• PDF: https://arxiv.org/pdf/2606.19409
• Project Page: https://docs.openrath.com

━━━━━━━━━━━━━━━━━━━━━━━━
📢 By: https://xn--r1a.website/PaperNexus

#MultiAgentSystems #RuntimeStateManagement #AgentOrientedProgramming #SessionCenteredArchitecture #DistributedSystemDesign

🔥 Lift4D: Harmonizing Single-View 3D Estimation for 4D Reconstruction In-the-Wild

💡 The paper presents Lift4D, a test-time optimization framework for reconstructing dynamic non-rigid objects from monocular video. The problem addressed is the difficulty in reconstructing 4D representations of dynamic objects from single-view video due to the scarcity of 4D training data and the limitations of prior approaches that either directly predict 4D representations or initialize a 3D representation and refine it based on video evidence.

The method involves adapting a single-view 3D reconstruction model to yield temporally consistent per-frame predictions, which provides a coherent initialization for a deformable 3D Gaussian Splatting representation. This representation is then optimized to match the input video through an occlusion-aware optimization that recovers visible surface details and completes unobserved regions using a view-conditioned diffusion prior.

The results show that Lift4D improves over prior 4D reconstruction methods, particularly on challenging in-the-wild sequences with severe occlusions and non-rigid motion. The framework effectively handles complex scenarios by integrating visual cues from direct observations with data-driven priors over geometry and appearance, making it a significant contribution to the field of 4D reconstruction from monocular video.

📅 Published on Jun 22

🔗 Links:
• GitHub: https://github.com/huggingface
• arXiv: https://arxiv.org/abs/2606.23688
• PDF: https://arxiv.org/pdf/2606.23688
• Project Page: https://lift4d.github.io/

━━━━━━━━━━━━━━━━━━━━━━━━
📢 By: https://xn--r1a.website/PaperNexus

#4DReconstruction #SingleView3DEstimation #MonocularVideoAnalysis #DynamicNonRigidObjectReconstruction #TestTimeOptimizationFrameworks

🔥 MemGUI-Agent: An End-to-End Long-Horizon Mobile GUI Agent with Proactive Context Management

💡 The paper introduces MemGUI-Agent, a mobile GUI agent designed to address the limitations of existing agents on long-horizon tasks. Current agents struggle with retaining intermediate facts across many steps and app transitions, leading to unreliable performance. This limitation is attributed to the ReAct-style prompting approach, which passively accumulates per-step records, causing prompt explosion and dilution of critical cross-app facts.

To address this issue, the authors propose MemGUI-Agent, which uses proactive context management through Context-as-Action, or ConAct. ConAct casts context management as first-class actions emitted by the same policy that selects UI actions. This approach maintains three structured context fields: folded action history, folded UI state, and recent step record, preserving critical UI facts while keeping context compact.

The authors also introduce MemGUI-3K, a dataset with 2,956 trajectories and full ConAct annotations for supervised training and offline analysis. Training an 8B model on MemGUI-3K results in MemGUI-8B-SFT, an 8B MemGUI-Agent that achieves the best open-data 8B performance on MemGUI-Bench and generalizes to the out-of-distribution MobileWorld benchmark.

The contributions of the paper are threefold. Firstly, it identifies the limitations of existing mobile GUI agents on long-horizon tasks and attributes them to the ReAct-style prompting approach. Secondly, it proposes MemGUI-Agent with proactive context management through ConAct, which addresses the limitations of existing agents. Finally, it introduces MemGUI-3K, a dataset for supervised training and offline analysis, and demonstrates the effectiveness of MemGUI-8B-SFT, an 8B MemGUI-Agent trained on this dataset. The code, data, and trained models will be released to facilitate further research and development.

📅 Published on Jun 18

🔗 Links:
• GitHub: https://github.com/huggingface
• arXiv: https://arxiv.org/abs/2606.19926
• PDF: https://arxiv.org/pdf/2606.19926
• Project Page: https://memgui-agent.github.io/

🤖 Models citing this paper:
• https://huggingface.co/lgy0404/MemGUI-8B-SFT

📊 Datasets citing this paper:
• https://huggingface.co/datasets/lgy0404/MemGUI-3K

━━━━━━━━━━━━━━━━━━━━━━━━
📢 By: https://xn--r1a.website/PaperNexus

#MobileGUIAutomation #LongHorizonTaskLearning #ProactiveContextManagement #ContextAsAction #EndToEndGUIAgents

🔥 MobileForge: Annotation-Free Adaptation for Mobile GUI Agents with Hierarchical Feedback-Guided Policy Optimization

💡 The paper introduces MobileForge, a system for adapting mobile graphical user interface agents to real target apps without requiring manual annotations. The problem addressed is that current mobile GUI agents require costly and time-consuming human-written tasks, demonstrations, or reward labels to adapt to new apps. Existing annotation-free GUI learning methods lack a unified approach to connect target-app exploration, curriculum mining, rollout execution, and feedback, and policy optimization often relies on isolated rollouts and coarse rewards.

MobileForge consists of two main components: MobileGym, which generates tasks and evaluates rollouts based on real mobile app interaction, and Hierarchical Feedback-Guided Policy Optimization, which uses trajectory outcomes, step-level process feedback, and corrective hints to update the policy. This approach allows for efficient adaptation of mobile GUI agents to new apps without requiring manual annotations.

The results show that MobileForge can adapt a mobile GUI agent to achieve 67.2 percent Pass@3 on AndroidWorld, which is close to the performance of a specialized model trained on closed data. Further adaptation using MobileForge reaches 77.6 percent Pass@3 on AndroidWorld and 41.0 percent success on the out-of-domain MobileWorld GUI-only split, establishing the strongest open-data mobile GUI agent in the evaluation. Overall, MobileForge provides a unified and efficient approach to adapting mobile GUI agents to new apps without requiring manual annotations, making it a significant contribution to the field.

📅 Published on Jun 18

🔗 Links:
• GitHub: https://github.com/huggingface
• arXiv: https://arxiv.org/abs/2606.19930
• PDF: https://arxiv.org/pdf/2606.19930
• Project Page: https://mobile-forge.github.io

📊 Datasets citing this paper:
• https://huggingface.co/datasets/lgy0404/mobileforge-exploration-trajectories
• https://huggingface.co/datasets/lgy0404/mobileforge-training-data
• https://huggingface.co/datasets/lgy0404/mobileforge-benchmark-results

━━━━━━━━━━━━━━━━━━━━━━━━
📢 By: https://xn--r1a.website/PaperNexus

#MobileGUIAgents #HierarchicalFeedbackGuidedPolicyOptimization #AnnotationFreeLearning #MobileGraphicalUserInterface #PolicyOptimizationForMobileApps