✨Recurrent-Depth VLA: Implicit Test-Time Compute Scaling of Vision-Language-Action Models via Latent Iterative Reasoning
📝 Summary:
RD-VLA introduces a recurrent architecture for VLA models, using latent iterative refinement for adaptive compute. It maintains constant memory, boosts success on complex tasks, and offers significant speedups.
🔹 Publication Date: Published on Feb 8
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.07845
• PDF: https://arxiv.org/pdf/2602.07845
• Project Page: https://rd-vla.github.io/
• Github: https://github.com/rd-vla/rd-vla
==================================
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📝 Summary:
RD-VLA introduces a recurrent architecture for VLA models, using latent iterative refinement for adaptive compute. It maintains constant memory, boosts success on complex tasks, and offers significant speedups.
🔹 Publication Date: Published on Feb 8
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.07845
• PDF: https://arxiv.org/pdf/2602.07845
• Project Page: https://rd-vla.github.io/
• Github: https://github.com/rd-vla/rd-vla
==================================
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arXiv.org
Recurrent-Depth VLA: Implicit Test-Time Compute Scaling of...
Current Vision-Language-Action (VLA) models rely on fixed computational depth, expending the same amount of compute on simple adjustments and complex multi-step manipulation. While...
✨Demo-ICL: In-Context Learning for Procedural Video Knowledge Acquisition
📝 Summary:
Researchers introduce a new video understanding task and benchmark that evaluates models' ability to learn from few-shot demonstrations, along with a specialized MLLM architecture trained using a two-...
🔹 Publication Date: Published on Feb 9
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.08439
• PDF: https://arxiv.org/pdf/2602.08439
• Github: https://github.com/dongyh20/Demo-ICL
==================================
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📝 Summary:
Researchers introduce a new video understanding task and benchmark that evaluates models' ability to learn from few-shot demonstrations, along with a specialized MLLM architecture trained using a two-...
🔹 Publication Date: Published on Feb 9
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.08439
• PDF: https://arxiv.org/pdf/2602.08439
• Github: https://github.com/dongyh20/Demo-ICL
==================================
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✨QuantaAlpha: An Evolutionary Framework for LLM-Driven Alpha Mining
📝 Summary:
Financial markets are noisy and non-stationary, making alpha mining highly sensitive to noise in backtesting results and sudden market regime shifts. While recent agentic frameworks improve alpha mini...
🔹 Publication Date: Published on Feb 6
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.07085
• PDF: https://arxiv.org/pdf/2602.07085
• Github: https://github.com/QuantaAlpha/QuantaAlpha
==================================
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📝 Summary:
Financial markets are noisy and non-stationary, making alpha mining highly sensitive to noise in backtesting results and sudden market regime shifts. While recent agentic frameworks improve alpha mini...
🔹 Publication Date: Published on Feb 6
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.07085
• PDF: https://arxiv.org/pdf/2602.07085
• Github: https://github.com/QuantaAlpha/QuantaAlpha
==================================
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✨LLaDA2.1: Speeding Up Text Diffusion via Token Editing
📝 Summary:
LLaDA2.1 introduces a novel token-to-token editing approach with speed and quality modes, enhanced through reinforcement learning for improved reasoning and instruction following in large language dif...
🔹 Publication Date: Published on Feb 9
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.08676
• PDF: https://arxiv.org/pdf/2602.08676
• Github: https://github.com/inclusionAI/LLaDA2.X
🔹 Models citing this paper:
• https://huggingface.co/inclusionAI/LLaDA2.1-mini
• https://huggingface.co/inclusionAI/LLaDA2.1-flash
==================================
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📝 Summary:
LLaDA2.1 introduces a novel token-to-token editing approach with speed and quality modes, enhanced through reinforcement learning for improved reasoning and instruction following in large language dif...
🔹 Publication Date: Published on Feb 9
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.08676
• PDF: https://arxiv.org/pdf/2602.08676
• Github: https://github.com/inclusionAI/LLaDA2.X
🔹 Models citing this paper:
• https://huggingface.co/inclusionAI/LLaDA2.1-mini
• https://huggingface.co/inclusionAI/LLaDA2.1-flash
==================================
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✨WorldCompass: Reinforcement Learning for Long-Horizon World Models
📝 Summary:
WorldCompass enhances long-horizon video-based world models through reinforcement learning post-training with clip-level rollouts, complementary rewards, and efficient RL algorithms. AI-generated summ...
🔹 Publication Date: Published on Feb 9
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.09022
• PDF: https://arxiv.org/pdf/2602.09022
• Project Page: https://3d-models.hunyuan.tencent.com/world/
==================================
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📝 Summary:
WorldCompass enhances long-horizon video-based world models through reinforcement learning post-training with clip-level rollouts, complementary rewards, and efficient RL algorithms. AI-generated summ...
🔹 Publication Date: Published on Feb 9
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.09022
• PDF: https://arxiv.org/pdf/2602.09022
• Project Page: https://3d-models.hunyuan.tencent.com/world/
==================================
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arXiv.org
WorldCompass: Reinforcement Learning for Long-Horizon World Models
This work presents WorldCompass, a novel Reinforcement Learning (RL) post-training framework for the long-horizon, interactive video-based world models, enabling them to explore the world more...
✨WildReward: Learning Reward Models from In-the-Wild Human Interactions
📝 Summary:
WildReward demonstrates that reward models can be effectively trained from in-the-wild user interactions using ordinal regression, achieving performance comparable to traditional methods while benefit...
🔹 Publication Date: Published on Feb 9
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.08829
• PDF: https://arxiv.org/pdf/2602.08829
==================================
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📝 Summary:
WildReward demonstrates that reward models can be effectively trained from in-the-wild user interactions using ordinal regression, achieving performance comparable to traditional methods while benefit...
🔹 Publication Date: Published on Feb 9
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.08829
• PDF: https://arxiv.org/pdf/2602.08829
==================================
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✨Reliable and Responsible Foundation Models: A Comprehensive Survey
📝 Summary:
Foundation models including LLMs, MLLMs, and generative models require reliable and responsible development addressing bias, security, explainability, and other critical issues for trustworthy deploym...
🔹 Publication Date: Published on Feb 4
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.08145
• PDF: https://arxiv.org/pdf/2602.08145
==================================
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📝 Summary:
Foundation models including LLMs, MLLMs, and generative models require reliable and responsible development addressing bias, security, explainability, and other critical issues for trustworthy deploym...
🔹 Publication Date: Published on Feb 4
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.08145
• PDF: https://arxiv.org/pdf/2602.08145
==================================
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✨MOVA: Towards Scalable and Synchronized Video-Audio Generation
📝 Summary:
MOVA is an open-source model generating synchronized video-audio content, including lip-synced speech and sound effects. It employs a 32B-parameter Mixture-of-Experts architecture for image-text to video-audio generation, overcoming limitations of previous cascaded and closed-source systems.
🔹 Publication Date: Published on Feb 9
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.08794
• PDF: https://arxiv.org/pdf/2602.08794
• Project Page: https://mosi.cn/models/mova
• Github: https://github.com/OpenMOSS/MOVA
==================================
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📝 Summary:
MOVA is an open-source model generating synchronized video-audio content, including lip-synced speech and sound effects. It employs a 32B-parameter Mixture-of-Experts architecture for image-text to video-audio generation, overcoming limitations of previous cascaded and closed-source systems.
🔹 Publication Date: Published on Feb 9
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.08794
• PDF: https://arxiv.org/pdf/2602.08794
• Project Page: https://mosi.cn/models/mova
• Github: https://github.com/OpenMOSS/MOVA
==================================
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✨InternAgent-1.5: A Unified Agentic Framework for Long-Horizon Autonomous Scientific Discovery
📝 Summary:
InternAgent-1.5 is a unified system for autonomous scientific discovery that integrates computational modeling and experimental research through coordinated subsystems for generation, verification, an...
🔹 Publication Date: Published on Feb 9
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.08990
• PDF: https://arxiv.org/pdf/2602.08990
==================================
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📝 Summary:
InternAgent-1.5 is a unified system for autonomous scientific discovery that integrates computational modeling and experimental research through coordinated subsystems for generation, verification, an...
🔹 Publication Date: Published on Feb 9
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.08990
• PDF: https://arxiv.org/pdf/2602.08990
==================================
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✨How2Everything: Mining the Web for How-To Procedures to Evaluate and Improve LLMs
📝 Summary:
A scalable framework for evaluating and improving goal-conditioned procedure generation using large-scale web mining, automated scoring, and reinforcement learning to enhance step-by-step instruction ...
🔹 Publication Date: Published on Feb 9
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.08808
• PDF: https://arxiv.org/pdf/2602.08808
• Github: https://github.com/lilakk/how2everything
==================================
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📝 Summary:
A scalable framework for evaluating and improving goal-conditioned procedure generation using large-scale web mining, automated scoring, and reinforcement learning to enhance step-by-step instruction ...
🔹 Publication Date: Published on Feb 9
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.08808
• PDF: https://arxiv.org/pdf/2602.08808
• Github: https://github.com/lilakk/how2everything
==================================
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✨GISA: A Benchmark for General Information-Seeking Assistant
📝 Summary:
A new benchmark called GISA is introduced for evaluating information-seeking assistants, featuring human-crafted queries with structured answer formats and live updates to prevent memorization. AI-gen...
🔹 Publication Date: Published on Feb 9
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.08543
• PDF: https://arxiv.org/pdf/2602.08543
==================================
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📝 Summary:
A new benchmark called GISA is introduced for evaluating information-seeking assistants, featuring human-crafted queries with structured answer formats and live updates to prevent memorization. AI-gen...
🔹 Publication Date: Published on Feb 9
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.08543
• PDF: https://arxiv.org/pdf/2602.08543
==================================
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✨Rolling Sink: Bridging Limited-Horizon Training and Open-Ended Testing in Autoregressive Video Diffusion
📝 Summary:
Autoregressive video diffusion models suffer from train-test gaps when generating long videos, but a training-free approach called Rolling Sink addresses this by maintaining AR cache and enabling ultr...
🔹 Publication Date: Published on Feb 8
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.07775
• PDF: https://arxiv.org/pdf/2602.07775
• Project Page: https://rolling-sink.github.io/
==================================
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📝 Summary:
Autoregressive video diffusion models suffer from train-test gaps when generating long videos, but a training-free approach called Rolling Sink addresses this by maintaining AR cache and enabling ultr...
🔹 Publication Date: Published on Feb 8
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.07775
• PDF: https://arxiv.org/pdf/2602.07775
• Project Page: https://rolling-sink.github.io/
==================================
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arXiv.org
Rolling Sink: Bridging Limited-Horizon Training and Open-Ended...
Recently, autoregressive (AR) video diffusion models has achieved remarkable performance. However, due to their limited training durations, a train-test gap emerges when testing at longer...
✨Concept-Aware Privacy Mechanisms for Defending Embedding Inversion Attacks
📝 Summary:
SPARSE is a user-centric framework that protects text embeddings from privacy leaks by selectively perturbing sensitive dimensions using differentiable masking and Mahalanobis noise calibration. AI-ge...
🔹 Publication Date: Published on Feb 6
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.07090
• PDF: https://arxiv.org/pdf/2602.07090
==================================
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📝 Summary:
SPARSE is a user-centric framework that protects text embeddings from privacy leaks by selectively perturbing sensitive dimensions using differentiable masking and Mahalanobis noise calibration. AI-ge...
🔹 Publication Date: Published on Feb 6
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.07090
• PDF: https://arxiv.org/pdf/2602.07090
==================================
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✨Aster: Autonomous Scientific Discovery over 20x Faster Than Existing Methods
📝 Summary:
Aster is an AI agent that accelerates scientific discovery by iteratively improving programs, achieving state-of-the-art results across multiple domains including mathematics, biology, and machine lea...
🔹 Publication Date: Published on Feb 3
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.07040
• PDF: https://arxiv.org/pdf/2602.07040
• Project Page: https://www.asterlab.ai/
==================================
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📝 Summary:
Aster is an AI agent that accelerates scientific discovery by iteratively improving programs, achieving state-of-the-art results across multiple domains including mathematics, biology, and machine lea...
🔹 Publication Date: Published on Feb 3
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.07040
• PDF: https://arxiv.org/pdf/2602.07040
• Project Page: https://www.asterlab.ai/
==================================
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✨Weak-Driven Learning: How Weak Agents make Strong Agents Stronger
📝 Summary:
WMSS is a post-training paradigm that uses weak model checkpoints to identify and fill learning gaps, enabling continued improvement beyond conventional saturation points in large language models. AI-...
🔹 Publication Date: Published on Feb 9
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.08222
• PDF: https://arxiv.org/pdf/2602.08222
==================================
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📝 Summary:
WMSS is a post-training paradigm that uses weak model checkpoints to identify and fill learning gaps, enabling continued improvement beyond conventional saturation points in large language models. AI-...
🔹 Publication Date: Published on Feb 9
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.08222
• PDF: https://arxiv.org/pdf/2602.08222
==================================
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✨Theory of Space: Can Foundation Models Construct Spatial Beliefs through Active Exploration?
📝 Summary:
Current multimodal foundation models show limitations in maintaining coherent spatial beliefs during active exploration, exhibiting gaps between active and passive performance, inefficient exploration...
🔹 Publication Date: Published on Feb 4
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.07055
• PDF: https://arxiv.org/pdf/2602.07055
• Project Page: https://theory-of-space.github.io/
• Github: https://github.com/mll-lab-nu/Theory-of-Space
==================================
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📝 Summary:
Current multimodal foundation models show limitations in maintaining coherent spatial beliefs during active exploration, exhibiting gaps between active and passive performance, inefficient exploration...
🔹 Publication Date: Published on Feb 4
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.07055
• PDF: https://arxiv.org/pdf/2602.07055
• Project Page: https://theory-of-space.github.io/
• Github: https://github.com/mll-lab-nu/Theory-of-Space
==================================
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✨Learning-guided Kansa collocation for forward and inverse PDEs beyond linearity
📝 Summary:
Research explores PDE solvers including neural frameworks for scientific simulations, examining forward solutions, inverse problems, and equation discovery across multi-variable and non-linear systems...
🔹 Publication Date: Published on Feb 8
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.07970
• PDF: https://arxiv.org/pdf/2602.07970
==================================
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📝 Summary:
Research explores PDE solvers including neural frameworks for scientific simulations, examining forward solutions, inverse problems, and equation discovery across multi-variable and non-linear systems...
🔹 Publication Date: Published on Feb 8
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.07970
• PDF: https://arxiv.org/pdf/2602.07970
==================================
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✨MotionCrafter: Dense Geometry and Motion Reconstruction with a 4D VAE
📝 Summary:
MotionCrafter is a video diffusion framework that jointly reconstructs 4D geometry and estimates dense motion using a novel joint representation and 4D VAE architecture. AI-generated summary We introd...
🔹 Publication Date: Published on Feb 9
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.08961
• PDF: https://arxiv.org/pdf/2602.08961
• Project Page: https://ruijiezhu94.github.io/MotionCrafter_Page
• Github: https://github.com/TencentARC/MotionCrafter
🔹 Models citing this paper:
• https://huggingface.co/TencentARC/MotionCrafter
==================================
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📝 Summary:
MotionCrafter is a video diffusion framework that jointly reconstructs 4D geometry and estimates dense motion using a novel joint representation and 4D VAE architecture. AI-generated summary We introd...
🔹 Publication Date: Published on Feb 9
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.08961
• PDF: https://arxiv.org/pdf/2602.08961
• Project Page: https://ruijiezhu94.github.io/MotionCrafter_Page
• Github: https://github.com/TencentARC/MotionCrafter
🔹 Models citing this paper:
• https://huggingface.co/TencentARC/MotionCrafter
==================================
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✨SoulX-Singer: Towards High-Quality Zero-Shot Singing Voice Synthesis
📝 Summary:
A high-quality open-source singing voice synthesis system is presented with support for multiple languages and controllable generation, along with a dedicated benchmark for evaluating zero-shot perfor...
🔹 Publication Date: Published on Feb 8
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.07803
• PDF: https://arxiv.org/pdf/2602.07803
==================================
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📝 Summary:
A high-quality open-source singing voice synthesis system is presented with support for multiple languages and controllable generation, along with a dedicated benchmark for evaluating zero-shot perfor...
🔹 Publication Date: Published on Feb 8
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.07803
• PDF: https://arxiv.org/pdf/2602.07803
==================================
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✨AVERE: Improving Audiovisual Emotion Reasoning with Preference Optimization
📝 Summary:
A benchmark and optimization technique are presented to improve multimodal large language models' emotion understanding by addressing spurious associations and hallucinations in audiovisual cues. AI-g...
🔹 Publication Date: Published on Feb 4
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.07054
• PDF: https://arxiv.org/pdf/2602.07054
• Project Page: https://avere-iclr.github.io/
• Github: https://avere-iclr.github.io/
✨ Datasets citing this paper:
• https://huggingface.co/datasets/chaubeyG/EmoReAlM
==================================
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📝 Summary:
A benchmark and optimization technique are presented to improve multimodal large language models' emotion understanding by addressing spurious associations and hallucinations in audiovisual cues. AI-g...
🔹 Publication Date: Published on Feb 4
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.07054
• PDF: https://arxiv.org/pdf/2602.07054
• Project Page: https://avere-iclr.github.io/
• Github: https://avere-iclr.github.io/
✨ Datasets citing this paper:
• https://huggingface.co/datasets/chaubeyG/EmoReAlM
==================================
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✨Learning Query-Aware Budget-Tier Routing for Runtime Agent Memory
📝 Summary:
BudgetMem is a runtime memory framework for LLM agents. It uses modular components with budget tiers and a neural router to optimize memory performance-cost trade-offs, outperforming baselines and achieving better accuracy-cost frontiers.
🔹 Publication Date: Published on Feb 5
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.06025
• PDF: https://arxiv.org/pdf/2602.06025
• Project Page: https://viktoraxelsen.github.io/BudgetMem/
• Github: https://github.com/ViktorAxelsen/BudgetMem
==================================
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📝 Summary:
BudgetMem is a runtime memory framework for LLM agents. It uses modular components with budget tiers and a neural router to optimize memory performance-cost trade-offs, outperforming baselines and achieving better accuracy-cost frontiers.
🔹 Publication Date: Published on Feb 5
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.06025
• PDF: https://arxiv.org/pdf/2602.06025
• Project Page: https://viktoraxelsen.github.io/BudgetMem/
• Github: https://github.com/ViktorAxelsen/BudgetMem
==================================
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