✨VideoSSR: Video Self-Supervised Reinforcement Learning

📝 Summary:
VideoSSR is a novel self-supervised reinforcement learning framework that leverages intrinsic video information to generate high-quality training data. It uses three pretext tasks and the VideoSSR-30K dataset, improving MLLM performance across 17 benchmarks by over 5%.

🔹 Publication Date: Published on Nov 9

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.06281
• PDF: https://arxiv.org/pdf/2511.06281
• Project Page: https://github.com/lcqysl/VideoSSR
• Github: https://github.com/lcqysl/VideoSSR

🔹 Models citing this paper:
• https://huggingface.co/yhx12/VideoSSR

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#ReinforcementLearning #SelfSupervisedLearning #VideoAI #MachineLearning #DeepLearning

✨OlmoEarth: Stable Latent Image Modeling for Multimodal Earth Observation

📝 Summary:
OlmoEarth is a novel multimodal spatio-temporal foundation model for Earth observation data. It employs new self-supervised learning methods to achieve state-of-the-art performance on many tasks. It is deployed as a platform for non-profits and NGOs.

🔹 Publication Date: Published on Nov 17

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.13655
• PDF: https://arxiv.org/pdf/2511.13655
• Project Page: https://olmoearth.allenai.org/
• Github: https://github.com/allenai/olmoearth_pretrain

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#EarthObservation #FoundationModels #AI #RemoteSensing #SelfSupervisedLearning

✨UnSAMv2: Self-Supervised Learning Enables Segment Anything at Any Granularity

📝 Summary:
UnSAMv2 enables continuous segmentation granularity control for the SAM model without human annotations. It uses self-supervised learning on unlabeled data to discover mask-granularity pairs and a novel control embedding. UnSAMv2 significantly enhances SAM-2s performance across various segmentati...

🔹 Publication Date: Published on Nov 17

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.13714
• PDF: https://arxiv.org/pdf/2511.13714
• Project Page: https://yujunwei04.github.io/UnSAMv2-Project-Page/
• Github: https://github.com/yujunwei04/UnSAMv2

✨ Spaces citing this paper:
• https://huggingface.co/spaces/yujunwei04/UnSAMv2

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#AI #ComputerVision #SelfSupervisedLearning #ImageSegmentation #DeepLearning

✨Φeat: Physically-Grounded Feature Representation

📝 Summary:
Φeat is a new self-supervised visual backbone that captures material identity like reflectance and mesostructure. It learns robust features invariant to external physical factors such as shape and lighting, promoting physics-aware perception.

🔹 Publication Date: Published on Nov 14

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.11270
• PDF: https://arxiv.org/pdf/2511.11270

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#ComputerVision #SelfSupervisedLearning #DeepLearning #FeatureLearning #PhysicsAwareAI

✨EvoVLA: Self-Evolving Vision-Language-Action Model

📝 Summary:
EvoVLA is a self-supervised VLA framework tackling stage hallucination in long-horizon robotic manipulation. It uses triplet contrastive learning, pose-based exploration, and memory to prevent shortcuts. EvoVLA significantly improves success, sample efficiency, and reduces hallucination in sim an...

🔹 Publication Date: Published on Nov 20

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.16166
• PDF: https://arxiv.org/pdf/2511.16166
• Project Page: https://aigeeksgroup.github.io/EvoVLA/
• Github: https://aigeeksgroup.github.io/EvoVLA/

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#Robotics #VisionLanguageAction #SelfSupervisedLearning #AI #DeepLearning

✨TRivia: Self-supervised Fine-tuning of Vision-Language Models for Table Recognition

📝 Summary:
TRivia is a self-supervised fine-tuning method for vision-language models to learn table recognition from unlabeled data. It uses a question-answering reward mechanism to autonomously optimize the model. This open-source solution outperforms state-of-the-art systems on popular benchmarks.

🔹 Publication Date: Published on Dec 1

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.01248
• PDF: https://arxiv.org/pdf/2512.01248
• Github: https://github.com/opendatalab/TRivia

🔹 Models citing this paper:
• https://huggingface.co/opendatalab/TRivia-3B

✨ Spaces citing this paper:
• https://huggingface.co/spaces/opendatalab/TRivia-3B

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#TableRecognition #VisionLanguageModels #SelfSupervisedLearning #AI #DeepLearning

🤖🧠 S3PRL Toolkit: Advancing Self-Supervised Speech Representation Learning

🗓️ 13 Dec 2025
📚 AI News & Trends

The field of speech technology has witnessed a transformative shift in recent years, powered by the rise of self-supervised learning (SSL). Instead of relying on large amounts of labeled data, self-supervised models learn from the patterns and structures inherent in raw audio, enabling powerful and general-purpose speech representations. At the forefront of this innovation stands ...

#S3PRL #SelfSupervisedLearning #SpeechTechnology #SSL #SpeechRepresentationLearning #AI

✨Puzzle Curriculum GRPO for Vision-Centric Reasoning

📝 Summary:
Puzzle Curriculum GRPO PC-GRPO improves VLM visual reasoning without annotations. It uses self-supervised puzzle environments for verifiable rewards and a difficulty-aware curriculum to enhance consistency and accuracy.

🔹 Publication Date: Published on Dec 16

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.14944
• PDF: https://arxiv.org/pdf/2512.14944
• Project Page: https://pcgrpo.github.io/

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#VLM #VisualReasoning #SelfSupervisedLearning #ComputerVision #AI

✨DeFM: Learning Foundation Representations from Depth for Robotics

📝 Summary:
DeFM is a self-supervised foundation model for depth representation learning in robotics. It learns geometric and semantic features from 60M depth images, achieving state-of-the-art performance across diverse robotic tasks and strong sim-to-real generalization.

🔹 Publication Date: Published on Jan 26

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.18923
• PDF: https://arxiv.org/pdf/2601.18923
• Github: https://de-fm.github.io/

🔹 Models citing this paper:
• https://huggingface.co/leggedrobotics/defm

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#Robotics #FoundationModels #SelfSupervisedLearning #ComputerVision #MachineLearning

✨OmniRad: A Radiological Foundation Model for Multi-Task Medical Image Analysis

📝 Summary:
OmniRad is a self-supervised radiological foundation model pretrained on 1.2 million medical images. It improves classification F1 by 2.05 percent and achieves better segmentation through representation reuse and cross-task transferability.

🔹 Publication Date: Published on Feb 4

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.04547
• PDF: https://arxiv.org/pdf/2602.04547
• Github: https://github.com/unica-visual-intelligence-lab/OmniRad

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#MedicalAI #FoundationModels #Radiology #SelfSupervisedLearning #MedicalImaging

✨OmniVideo-R1: Reinforcing Audio-visual Reasoning with Query Intention and Modality Attention

📝 Summary:
OmniVideo-R1 is a reinforced framework that enhances audio-visual understanding. It uses self-supervised query-intensive grounding and contrastive modality-attentive fusion. Experiments show OmniVideo-R1 consistently outperforms baselines, demonstrating its effectiveness.

🔹 Publication Date: Published on Feb 5

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.05847
• PDF: https://arxiv.org/pdf/2602.05847

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#AudioVisualAI #SelfSupervisedLearning #DeepLearning #MultimodalAI #AIResearch

✨Learning Cross-View Object Correspondence via Cycle-Consistent Mask Prediction

📝 Summary:
This paper presents a conditional binary segmentation framework for robust cross-view object correspondence. It uses cycle-consistency training to create view-invariant representations without ground-truth annotations. This approach achieves state-of-the-art performance on relevant benchmarks.

🔹 Publication Date: Published on Feb 22

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.18996
• PDF: https://arxiv.org/pdf/2602.18996
• Github: https://github.com/shannany0606/CCMP

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#ComputerVision #MachineLearning #ObjectCorrespondence #ImageSegmentation #SelfSupervisedLearning

✨Latent Particle World Models: Self-supervised Object-centric Stochastic Dynamics Modeling

📝 Summary:
LPWM is a self-supervised object-centric world model that autonomously discovers object representations from video data. It models stochastic particle dynamics for decision-making, achieving state-of-the-art results.

🔹 Publication Date: Published on Mar 4

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.04553
• PDF: https://arxiv.org/pdf/2603.04553
• Project Page: https://taldatech.github.io/lpwm-web/
• Github: https://github.com/taldatech/lpwm

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#WorldModels #SelfSupervisedLearning #ObjectCentricAI #MachineLearning #AI

✨A Mixed Diet Makes DINO An Omnivorous Vision Encoder

📝 Summary:
The Omnivorous Vision Encoder learns modality-agnostic features by aligning multi-modal scene inputs and distilling semantics from a frozen teacher model. This resolves poor cross-modal alignment in existing encoders, yielding consistent, powerful embeddings for various modalities.

🔹 Publication Date: Published on Feb 27

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.24181
• PDF: https://arxiv.org/pdf/2602.24181

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#MultimodalAI #ComputerVision #DeepLearning #SelfSupervisedLearning #AIResearch

✨V-JEPA 2.1: Unlocking Dense Features in Video Self-Supervised Learning

📝 Summary:
V-JEPA 2.1 is a self-supervised model learning dense visual representations for images and videos. It combines dense predictive loss, deep self-supervision, multi-modal tokenizers, and scaling to achieve state-of-the-art performance across various benchmarks, significantly advancing visual unders...

🔹 Publication Date: Published on Mar 15

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.14482
• PDF: https://arxiv.org/pdf/2603.14482
• Project Page: https://ai.meta.com/blog/v-jepa-2-world-model-benchmarks/
• Github: https://github.com/facebookresearch/vjepa2

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#SelfSupervisedLearning #ComputerVision #DeepLearning #AI #VideoUnderstanding

✨V-JEPA 2: Self-Supervised Video Models Enable Understanding, Prediction and Planning

📝 Summary:
V-JEPA 2 uses self-supervised learning on web videos and minimal robot data. It excels at video understanding, anticipation, Q&A, and zero-shot robotic planning. This approach yields a powerful world model for physical world planning.

🔹 Publication Date: Published on Jun 11, 2025

🔹 Paper Links:
• arXiv Page: https://arxivexplained.com/papers/v-jepa-2-self-supervised-video-models-enable-understanding-prediction-and-planning
• PDF: https://arxiv.org/pdf/2506.09985
• Github: https://github.com/facebookresearch/vjepa2

✨ Datasets citing this paper:
• https://huggingface.co/datasets/ckadirt/vjxla

✨ Spaces citing this paper:
• https://huggingface.co/spaces/vselvarajijay/vjepa2-latent-prediction
• https://huggingface.co/spaces/aavi21458/vjepa2-latent-prediction

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#AI #SelfSupervisedLearning #VideoAI #Robotics #WorldModels

✨Ghost-FWL: A Large-Scale Full-Waveform LiDAR Dataset for Ghost Detection and Removal

📝 Summary:
This paper introduces Ghost-FWL, the first large-scale full-waveform LiDAR dataset for ghost point detection and removal. It leverages FWL data and a self-supervised learning approach to significantly improve LiDAR-based SLAM and 3D object detection accuracy by effectively removing false reflecti...

🔹 Publication Date: Published on Mar 30

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.28224
• PDF: https://arxiv.org/pdf/2603.28224
• Project Page: https://keio-csg.github.io/Ghost-FWL/
• Github: https://github.com/Keio-CSG/Ghost-FWL

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#LiDAR #GhostDetection #SLAM #3DObjectDetection #SelfSupervisedLearning

🔥 Self-Supervised Prompt Optimization

💡 The paper proposes a self supervised framework called Self Supervised Prompt Optimization that optimizes prompts for large language models without requiring external references. The problem addressed is that manually designed prompts require expertise and iterative experimentation, while existing prompt optimization methods rely heavily on external references such as ground truth or human evaluation, which can be costly to obtain. The proposed method derives evaluation and optimization signals purely from output comparisons, where a large language model evaluator selects superior prompts through pairwise output comparisons, and a large language model optimizer aligns outputs with task requirements. The results show that the proposed method outperforms state of the art prompt optimization methods, achieving comparable or superior results with significantly lower costs and fewer samples, demonstrating its effectiveness and efficiency. The method can optimize prompts for both closed and open ended tasks, and can be applied in real world scenarios where external references are unavailable or costly to obtain.

📅 Published on Feb 7, 2025

🔗 Links:
• arXiv: https://arxiv.org/abs/2502.06855
• PDF: https://arxiv.org/pdf/2502.06855
• GitHub: https://github.com/geekan/metagpt ⭐ 67.7k

🚀 Spaces citing this paper:
• https://huggingface.co/spaces/XiangJinYu/SPO
• https://huggingface.co/spaces/tang-x/SPO
• https://huggingface.co/spaces/ositamiles/SPO

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

#SelfSupervisedLearning #PromptOptimization #LargeLanguageModels #NaturalLanguageProcessing #LanguageModelEvaluation

🔥 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

🔥 PhiZero: A World Model Built Around Physical Language

💡 The paper introduces PhiZero, a physical world model that uses a compact discrete representation of world-state transitions, referred to as physical language. Existing physical world models typically predict future videos directly in pixel space, which leaves the underlying world dynamics implicit within high-dimensional visual predictors. In contrast, PhiZero is motivated by humans' ability to abstract predictive structure from visual experience and organize it in natural language for explicit reasoning.

The method used in PhiZero involves learning physical language from in-the-wild videos through self-supervision and using it to explicitly reason about how the physical world evolves. PhiZero adopts a reason-then-render paradigm, where it first infers future world evolution as a physical-language sequence and then renders the inferred transitions into videos.

The results of extensive experiments across generation and understanding benchmarks validate the ability of PhiZero to model physically coherent world evolution. The paper also shows the potential of PhiZero for realistic and interactive world modeling, fine-grained action-conditioned simulation, and zero-shot motion transfer. Overall, PhiZero provides a new approach to physical world modeling that is based on a compact and discrete representation of world-state transitions, and has the potential to enable more efficient and effective modeling of complex physical systems.

📅 Published on Jul 30

🔗 Links:
• GitHub: https://github.com/huggingface
• arXiv: https://arxiv.org/abs/2607.28624
• PDF: https://arxiv.org/pdf/2607.28624
• Project Page: https://phi-zero.github.io/

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

#PhysicalLanguageModeling #WorldModelArchitecture #DiscreteRepresentationLearning #SelfSupervisedLearning #PhysicalWorldReasoning