✨Don't Blind Your VLA: Aligning Visual Representations for OOD Generalization

📝 Summary:
Naive action fine-tuning degrades visual representations in Vision-Language-Action models. This study analyzes this degradation and introduces a simple method to align representations, improving out-of-distribution generalization.

🔹 Publication Date: Published on Oct 29

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.25616
• PDF: https://arxiv.org/pdf/2510.25616
• Project Page: https://blind-vla-paper.github.io
• Github: https://github.com/CognitiveAISystems/BlindVLA

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#VLA #OODGeneralization #ComputerVision #MachineLearning #RepresentationLearning

✨Dynamic Reflections: Probing Video Representations with Text Alignment

📝 Summary:
This work presents the first comprehensive study on video-text representation alignment. It reveals alignment depends on data richness and correlates with downstream task performance, suggesting its value for general video understanding. This introduces video-text alignment as a zero-shot method ...

🔹 Publication Date: Published on Nov 4

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.02767
• PDF: https://arxiv.org/pdf/2511.02767
• Github: https://video-prh.github.io/

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#VideoUnderstanding #TextAlignment #VideoTextAI #ZeroShotLearning #RepresentationLearning

✨FedRE: A Representation Entanglement Framework for Model-Heterogeneous Federated Learning

📝 Summary:
FedRE is a federated learning framework for model-heterogeneous environments. Clients create and upload entangled representations and entangled-label encodings to train a global classifier. This method enhances performance, protects privacy, and reduces communication overhead.

🔹 Publication Date: Published on Nov 27

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.22265
• PDF: https://arxiv.org/pdf/2511.22265

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#FederatedLearning #MachineLearning #AI #PrivacyPreservingAI #RepresentationLearning

✨In-Context Representation Hijacking

📝 Summary:
Doublespeak is an in-context attack that hijacks LLM representations. It replaces harmful keywords with benign ones in examples, making LLMs interpret innocuous prompts as harmful, bypassing safety. This highlights a need for representation-level alignment.

🔹 Publication Date: Published on Dec 3

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.03771
• PDF: https://arxiv.org/pdf/2512.03771

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#LLM #AISafety #AIsecurity #InContextLearning #RepresentationLearning

✨The Prism Hypothesis: Harmonizing Semantic and Pixel Representations via Unified Autoencoding

📝 Summary:
The Prism Hypothesis posits semantic encoders capture low-frequency meaning, while pixel encoders retain high-frequency details. Unified Autoencoding UAE leverages this with a frequency-band modulator to harmonize both into a single latent space. This achieves state-of-the-art performance on imag...

🔹 Publication Date: Published on Dec 22

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.19693
• PDF: https://arxiv.org/pdf/2512.19693
• Github: https://github.com/WeichenFan/UAE

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#DeepLearning #ComputerVision #Autoencoders #RepresentationLearning #AIResearch

✨Dynamic Large Concept Models: Latent Reasoning in an Adaptive Semantic Space

📝 Summary:
DLCM shifts computation from individual tokens to a compressed concept space, enabling more efficient reasoning. This hierarchical approach learns semantic boundaries end-to-end and improves performance on benchmarks by reallocating compute.

🔹 Publication Date: Published on Dec 31, 2025

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.24617
• PDF: https://arxiv.org/pdf/2512.24617

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#AI #MachineLearning #LargeModels #RepresentationLearning #EfficientAI