🔥 JoyAI-VL-Interaction: Real-Time Vision-Language Interaction Intelligence

💡 The paper introduces a new paradigm for vision-language models, shifting from turn-based systems that require user prompting to a model that operates in real-time, making autonomous decisions about when to respond or delegate. The problem with current large models is that they only answer when addressed and do not interact in real-time, even in video-call apps. To address this, the authors propose a model that continuously watches what is happening and decides on its own whether to speak or stay silent.

The authors make two main contributions. First, they release JoyAI-VL-Interaction, an 8B-scale vision-first vision-language interaction model that makes the response decision internally, choosing each second to stay silent, respond, or delegate to a background model. The model excels at vision-triggered responsiveness and time awareness. They also provide a transferable training recipe that allows for capabilities to emerge that were not explicitly trained for, such as guiding a shopper through changing app screens or improvising a lecture from a slide deck.

Second, they release a complete deployable system built around the model, which streams any ongoing video into the model, making it genuinely present in the world. The system has pluggable components, including ASR/TTS modules, memory, visualization UI, and a background brain that can connect to any API or agent.

The results show that human raters prefer JoyAI-VL-Interaction over in-app video-call assistants by a wide margin across six real-world scenarios. This is the first open, vision-driven interaction model released together with its training recipe, data, and complete deployable system, making it a significant contribution to the field of interaction models.

📅 Published on Jun 10

🔗 Links:
• GitHub: https://github.com/huggingface
• arXiv: https://arxiv.org/abs/2606.14777
• PDF: https://arxiv.org/pdf/2606.14777
• Project Page: https://joyai-vl-video-future-academy-jd.github.io/JoyAI-VL-Interaction/

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

#VisionLanguageModels #RealTimeInteraction #AutonomousDecisionMaking #VisionFirstApproach #MultimodalIntelligence

🔥 DreamX-World 1.0: A General-Purpose Interactive World Model

💡 DreamX-World 1.0 is a general-purpose interactive text-to-video model that generates long-horizon content with camera control and scene persistence. The problem addressed by this model is the need for a controllable and interactive world model that can generate high-quality video content. To solve this problem, the authors introduced several new methods, including a lightweight variant of projective positional encoding called E-PRoPE, which retains projective camera geometry while applying camera-aware attention to spatially reduced tokens.

The authors also converted a bidirectional video generator into a few-step autoregressive world model using causal forcing, DMD-style distillation, and long-rollout training. This training process exposes the model to its own generated history, reducing style and color drift that accumulates across autoregressive chunks. Additionally, the authors introduced Memory-Conditioned Scene Persistence, which retrieves earlier views through camera-geometry-based retrieval, and residual recycling, which makes the conditioning path less sensitive to imperfect memory latents.

The model also includes Event Instruction Tuning, which adds composable event control, and reinforcement learning alignment, which recovers camera control and visual quality after distillation. To improve efficiency, the authors used mixed-precision DiT execution, residual reuse, 75%-pruned VAE decoding, and asynchronous pipeline parallelism, allowing the model to reach up to 16 FPS on eight RTX 5090 GPUs.

The results show that DreamX-World 1.0 achieves a camera-control score of 73.75 and an overall score of 84.76, outperforming HY-WorldPlay 1.5 and LingBot-World in overall score. The model's ability to generate high-quality video content with camera control and scene persistence makes it a significant contribution to the field of interactive world models. Overall, DreamX-World 1.0 is a powerful tool for generating interactive and controllable video content, with potential applications in a variety of fields, including gaming, simulation, and education.

📅 Published on Jun 15

🔗 Links:
• GitHub: https://github.com/huggingface
• Project Page: https://huggingface.co/papers?q=projective%20positional%20encoding
• arXiv: https://arxiv.org/abs/2606.16993
• PDF: https://arxiv.org/pdf/2606.16993

🤖 Models citing this paper:
• https://huggingface.co/GD-ML/DreamX-World-5B

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

#TextToVideoSynthesis #InteractiveWorldModels #VideoContentGeneration #ScenePersistence #CameraControlMechanisms

🔥 Geometric Action Model for Robot Policy Learning

💡 The paper proposes a Geometric Action Model for robot policy learning that leverages pretrained geometric foundation models to enable language-conditioned manipulation policies in 3D physical environments. The problem addressed is that current vision-language-action models and video world-action models operate primarily on 2D image frames or 2D-derived latent spaces, leaving implicit the 3D geometry required for contact-rich manipulation.

The proposed method, Geometric Action Model, repurposes a pretrained geometric foundation model as a shared substrate for perception, temporal prediction, and action decoding. It splits the model at an intermediate layer, using the shallow layers as an observation encoder and inserting a causal future predictor to forecast future latent tokens conditioned on language, proprioception, and action history. The predicted future tokens are then routed through the remaining model blocks for feature propagation and decoding, allowing a single backbone to produce both future geometry and actions.

The results show that the Geometric Action Model is more accurate, more robust, faster, and lighter than current foundation-model-scale baselines across a broad suite of simulation and real-robot manipulation benchmarks. This design equips the geometric foundation model with language-conditioned temporal world modeling through minimal architectural modification while preserving its rich geometric priors, making it a significant contribution to robot policy learning in 3D physical environments.

📅 Published on Jun 15

🔗 Links:
• GitHub: https://github.com/huggingface
• arXiv: https://arxiv.org/abs/2606.17046
• PDF: https://arxiv.org/pdf/2606.17046
• Project Page: https://cvlab-kaist.github.io/Geometric-Action-Model/

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

#GeometricDeepLearning #RobotPolicyLearning #LanguageConditionedManipulation #3DPhysicalEnvironmentModeling #GeometricFoundationModels

🔥 VibeThinker-3B: Exploring the Frontier of Verifiable Reasoning in Small Language Models

💡 The paper introduces VibeThinker-3B, a compact language model with 3 billion parameters, that achieves state-of-the-art performance on verifiable reasoning tasks, challenging the conventional assumption that large models are necessary for such tasks. The model was developed using a specialized training pipeline that includes curriculum-based supervised fine-tuning, multi-domain reinforcement learning, and offline self-distillation. The model was evaluated on several highly demanding verifiable tasks and achieved impressive results, including a score of 94.3 on AIME26, 80.2 Pass@1 on LiveCodeBench v6, and a 96.1 percent acceptance rate on recent unseen LeetCode contests. These results place VibeThinker-3B in the performance band of first-tier reasoning systems, matching or exceeding the performance of much larger models. The paper also shows that the model's performance does not compromise its instruction controllability, with a score of 93.4 on IFEval. The results of this study support the Parametric Compression-Coverage Hypothesis, which suggests that verifiable reasoning can be compressed into compact reasoning cores, while open-domain knowledge and general-purpose competence require larger models with broader parameter coverage. Overall, the paper demonstrates that compact models can be a complementary path to achieving frontier-level performance on verifiable reasoning tasks, and that they are not just efficient substitutes for larger models.

📅 Published on Jun 15

🔗 Links:
• GitHub: https://github.com/huggingface
• arXiv: https://arxiv.org/abs/2606.16140
• PDF: https://arxiv.org/pdf/2606.16140
• Project Page: https://github.com/WeiboAI/VibeThinker

🤖 Models citing this paper:
• https://huggingface.co/WeiboAI/VibeThinker-3B
• https://huggingface.co/KakTakOne/VibeThinker-3B-GGUF
• https://huggingface.co/ffkbblu/pepekberbulu

🚀 Spaces citing this paper:
• https://huggingface.co/spaces/Mike0021/vibethinker-3b-zerogpu
• https://huggingface.co/spaces/ffkbblu/trst

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

#VerifiableReasoning #SmallLanguageModels #CompactModelArchitecture #ReinforcementLearningForNLP #EfficientLanguageModeling

🔥 GLM-5: from Vibe Coding to Agentic Engineering

💡 The paper introduces GLM-5, a next-generation foundation model that advances the field of coding and software engineering. The current paradigm of vibe coding, which relies on intuitive and often imprecise coding practices, is limited in its ability to handle complex real-world software engineering tasks. To address this, the authors propose GLM-5, which builds upon the agentic, reasoning, and coding capabilities of its predecessor and incorporates several key innovations.

The method used to develop GLM-5 involves the adoption of DSA, which significantly reduces training and inference costs while maintaining long-context fidelity. Additionally, the authors implement a new asynchronous reinforcement learning infrastructure that improves post-training efficiency by decoupling generation from training. Novel asynchronous agent RL algorithms are also proposed to further improve RL quality, enabling the model to learn from complex, long-horizon interactions more effectively.

The results of the paper demonstrate the effectiveness of GLM-5, which achieves state-of-the-art performance on major open benchmarks. Most notably, GLM-5 demonstrates unprecedented capability in real-world coding tasks, surpassing previous baselines in handling end-to-end software engineering challenges. The model's ability to handle complex coding tasks and its potential to transition the paradigm of vibe coding to agentic engineering make it a significant contribution to the field of data science and software engineering. Overall, the paper presents a major advancement in foundation models and has the potential to impact the way software engineering is practiced.

📅 Published on Feb 17

🔗 Links:
• GitHub: https://github.com/huggingface
• arXiv: https://arxiv.org/abs/2602.15763
• PDF: https://arxiv.org/pdf/2602.15763
• Project Page: https://huggingface.co/spaces/GenAISecurityProject/OWASP-AIBOM-Generator

🤖 Models citing this paper:
• https://huggingface.co/zai-org/GLM-5
• https://huggingface.co/zai-org/GLM-5.1
• https://huggingface.co/zai-org/GLM-5.2

📊 Datasets citing this paper:
• https://huggingface.co/datasets/zai-org/terminal-bench-2-verified
• https://huggingface.co/datasets/harithoppil/terminal-bench-2-verified

🚀 Spaces citing this paper:
• https://huggingface.co/spaces/pliny-the-prompter/obliteratus
• https://huggingface.co/spaces/akhaliq/anycoder
• https://huggingface.co/spaces/GenAISecurityProject/OWASP-AIBOM-Generator

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

#AgenticEngineering #FoundationModels #VibeCoding #SoftwareEngineeringInnovations #ReinforcementLearningTechniques

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🔥 Next-Latent Prediction Transformers Learn Compact World Models

💡 The paper introduces Next-Latent Prediction, a method that enhances transformer architectures by adding self-supervised latent state prediction to the standard next-token training. The problem with standard transformers is that they lack an incentive to compress history into compact latent states, leading to poor generalization. To address this, the authors propose Next-Latent Prediction, which trains a transformer to learn latent representations that can predict the next latent state given the next output token. This approach injects a recurrent inductive bias into transformers, encouraging them to form compact internal world models with their own belief states and transition dynamics. The method is simple and efficient, and it does not change the architecture, parallel training, or inference of the transformer. The authors show that this approach leads to significant gains in downstream accuracy, representation compression, and lookahead planning across various benchmarks, including world modeling, reasoning, planning, and language modeling. The results demonstrate that Next-Latent Prediction is a effective paradigm for shaping transformer representations toward stronger generalization.

📅 Published on Nov 8, 2025

🔗 Links:
• GitHub: https://github.com/huggingface
• arXiv: https://arxiv.org/abs/2511.05963
• PDF: https://arxiv.org/pdf/2511.05963
• Project Page: https://jaydenteoh.github.io/blog/2026/nextlat

📊 Datasets citing this paper:
• https://huggingface.co/datasets/JaydenTeoh/manhattan

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

#NextLatentPrediction #TransformerArchitectures #SelfSupervisedLearning #LatentStatePrediction #CompactWorldModels

🔥 Speaking the Language of Science: Toward a General-Purpose Generative Foundation Model for the Natural Sciences

💡 The paper introduces LOGOS, a scientific generative language model that unifies various tasks across the natural sciences within a single autoregressive framework. The model encodes diverse scientific objects and their spatial interactions as token sequences over a common vocabulary, allowing it to capture complex structural interactions in a purely sequential manner. This approach enables a wide range of downstream tasks to be formulated consistently as next-token prediction in the same grammar space, creating strong alignment between continued multi-domain pre-training and downstream objectives.

The researchers trained LOGOS models at different scales, including 1B, 3B, and 8B parameters, and found a consistent positive correlation between model size and performance. The model consistently matches or outperforms domain-specific baselines across diverse tasks, providing preliminary evidence for the feasibility of a single model that can perform well across multiple domains in the natural sciences.

The paper's main contribution is the demonstration of a unified scientific generative language model that can be applied to various tasks in the natural sciences, including those that involve spatial interactions and complex structural relationships. The results suggest that the future of AI for science may lie in deeply aligning scientific foundation models with large language models, rather than building separate technical stacks. The release of the model weights and associated resources is intended to facilitate further research in this area.

The problem addressed by the paper is the lack of a unified framework for modeling various tasks in the natural sciences, which often require separate domain-specific models. The method used to address this problem is the development of a scientific generative language model that can encode diverse scientific objects and spatial interactions as token sequences, allowing for a wide range of downstream tasks to be formulated consistently as next-token prediction.

The results of the paper demonstrate the effectiveness of the LOGOS model in performing various tasks across the natural sciences, including those that involve spatial interactions and complex structural relationships. The positive correlation between model size and performance suggests that larger models may be able to achieve even better results, and the release of the model weights and associated resources is intended to facilitate further research in this area. Overall, the paper contributes to the development of a unified framework for modeling various tasks in the natural sciences, and demonstrates the potential of scientific generative language models for advancing AI research in this area.

📅 Published on Jun 15

🔗 Links:
• GitHub: https://github.com/huggingface
• arXiv: https://arxiv.org/abs/2606.16905
• PDF: https://arxiv.org/pdf/2606.16905

🤖 Models citing this paper:
• https://huggingface.co/LOGOS-Hub/LOGOS-8B
• https://huggingface.co/LOGOS-Hub/LOGOS-pretrain-1B
• https://huggingface.co/LOGOS-Hub/LOGOS-pretrain-8B

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

#NaturalScienceLanguageModels #GenerativeFoundationModels #ScientificLanguageProcessing #AutoregressiveModeling #MultidomainLearning

🔥 Beyond the Current Observation: Evaluating Multimodal Large Language Models in Controllable Non-Markov Games

💡 The paper introduces a new benchmark suite called RNG-Bench to evaluate the ability of multimodal large language models to reconstruct past observations and use them for decision-making in multi-step interactions. The problem addressed is that existing benchmarks do not adequately test a model's ability to recall and act on past observations, which is a crucial skill for deploying these models in real-world applications. The RNG-Bench suite consists of two games, Matching Pairs and 3D Maze, which are designed to test a model's ability to reconstruct past observations and use them to make decisions. The games have controlled difficulty parameters, including grid size, visual pattern, and observation modality, which allow for a thorough evaluation of a model's skills. The benchmark also introduces a head-to-head duel protocol to control for instance-level variance and a Memory Gap metric to distinguish between forgetting and poor decision-making. The results show that most residual errors in the models' performance are due to forgetting earlier observations rather than suboptimal decision-making. The paper also demonstrates that fine-tuning a model on optimal-policy rollouts and filtered model demonstrations can improve its performance on RNG-Bench and transfer to existing benchmarks without degrading its general multimodal capability. Overall, the paper provides a new benchmark suite and evaluation methodology for multimodal large language models, and demonstrates the importance of testing these models' ability to recall and act on past observations.

📅 Published on Jun 17

🔗 Links:
• GitHub: https://github.com/huggingface
• arXiv: https://arxiv.org/abs/2606.19338
• PDF: https://arxiv.org/pdf/2606.19338
• Project Page: https://internlm.github.io/RNGBench/

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

#MultimodalLargeLanguageModels #ControllableNonMarkovGames #RNNGBench #MultistepInteractions #NonMarkovDecisionProcesses

🔥 Kairos: A Native World Model Stack for Physical AI

💡 The paper introduces Kairos, a native world model framework designed to support physical AI applications. The problem addressed is that current world models are limited in their ability to learn from diverse experiences, maintain persistent states over time, and deploy efficiently in real-world scenarios. To address this, Kairos pioneers a native pre-training paradigm that learns from open-world videos, human behavioral data, and robot interactions, organized into a progressive developmental pathway.

The method involves a native unified architecture equipped with hybrid linear temporal attention, which captures local dynamics, mid-range dependencies, and maintains persistent global memory. This architecture is designed to limit error accumulation and guarantee state propagation across extended horizons. Additionally, Kairos incorporates a deployment-aware system co-design to support low-latency rollout generation on various hardware platforms.

The results show that Kairos achieves top-level performance on embodied world-model, long-horizon, and action-policy benchmarks, while offering a strong efficiency-capability trade-off. The experiments demonstrate that Kairos can learn from diverse experiences, maintain persistent states, and deploy efficiently in real-world scenarios. Overall, the paper positions Kairos as a cohesive operational foundation for future self-evolving physical intelligence, providing a native world model stack that can support a wide range of physical AI applications.

📅 Published on Jun 16

🔗 Links:
• GitHub: https://github.com/huggingface
• arXiv: https://arxiv.org/abs/2606.16533
• PDF: https://arxiv.org/pdf/2606.16533

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

#PhysicalAI #WorldModeling #NativePreTraining #RobotLearning #TemporalAttention

🔥 GameCraft-Bench: Can Agents Build Playable Games End-to-End in a Real Game Engine?

💡 The paper introduces GameCraft-Bench, a benchmark for evaluating the ability of coding agents to generate playable games end-to-end in a real game engine. The problem of end-to-end game generation is challenging as it requires coding agents to create complete playable games from natural language descriptions while meeting specific evaluation criteria. The authors formalize this problem and propose an interaction-grounded evaluation framework that assesses executable gameplay through replayed demonstrations and rubric-guided multimodal judging.

The framework is instantiated as GameCraft-Bench, a benchmark comprising 140 Godot tasks across 15 game families. The benchmark evaluates coding agents based on three desiderata: Engine Grounding, Artifact Completeness, and Interactive Verification. The authors evaluate frontier coding agents using GameCraft-Bench and find that end-to-end game generation remains highly challenging, with the strongest agent achieving only 41.46 percent and most agents scoring below 40 percent.

Further analysis reveals that while agents often implement recognizable mechanics, they struggle to deliver complete games with sufficient content, functional visual feedback, and coherent presentation. The paper contributes to the field of game generation by providing a comprehensive evaluation framework and a benchmark for assessing the capabilities of coding agents in generating playable games. The results highlight the difficulties of end-to-end game generation and provide insights for future research in this area. The GameCraft-Bench benchmark, code, and data are made available for further research and development.

📅 Published on Jun 16

🔗 Links:
• GitHub: https://github.com/huggingface
• arXiv: https://arxiv.org/abs/2606.17861
• PDF: https://arxiv.org/pdf/2606.17861
• Project Page: https://tongxuluo.github.io/gamecraft-bench-website/

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

#GameDevelopmentAI #EndToEndGameGeneration #GameEngineBenchmarking #AIForGameDesign #ProceduralContentGeneration