✨Sparse Video Generation Propels Real-World Beyond-the-View Vision-Language Navigation
📝 Summary:
Vision-language navigation systems traditionally require detailed instructions but can be improved by incorporating video generation models with sparse future planning for faster, more efficient real-...
🔹 Publication Date: Published on Feb 5
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.05827
• PDF: https://arxiv.org/pdf/2602.05827
• Project Page: https://opendrivelab.com/SparseVideoNav/
• Github: https://github.com/opendrivelab/sparsevideonav
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
Vision-language navigation systems traditionally require detailed instructions but can be improved by incorporating video generation models with sparse future planning for faster, more efficient real-...
🔹 Publication Date: Published on Feb 5
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.05827
• PDF: https://arxiv.org/pdf/2602.05827
• Project Page: https://opendrivelab.com/SparseVideoNav/
• Github: https://github.com/opendrivelab/sparsevideonav
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
✨Adapting Vision-Language Models for E-commerce Understanding at Scale
📝 Summary:
This paper demonstrates that targeted adaptation of general Vision-Language Models significantly improves e-commerce product understanding while preserving broad multimodal capabilities. A novel evaluation suite for deep product understanding is also proposed.
🔹 Publication Date: Published on Feb 12
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.11733
• PDF: https://arxiv.org/pdf/2602.11733
==================================
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✓ https://t.iss.one/DataScienceT
#VisionLanguageModels #EcommerceAI #ProductUnderstanding #DeepLearning #MultimodalAI
📝 Summary:
This paper demonstrates that targeted adaptation of general Vision-Language Models significantly improves e-commerce product understanding while preserving broad multimodal capabilities. A novel evaluation suite for deep product understanding is also proposed.
🔹 Publication Date: Published on Feb 12
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.11733
• PDF: https://arxiv.org/pdf/2602.11733
==================================
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#VisionLanguageModels #EcommerceAI #ProductUnderstanding #DeepLearning #MultimodalAI
✨ExStrucTiny: A Benchmark for Schema-Variable Structured Information Extraction from Document Images
📝 Summary:
ExStrucTiny is a new benchmark dataset for structured information extraction from document images. It addresses limitations of existing datasets by covering diverse document types and flexible schemas. This aims to improve generalist models for structured information extraction.
🔹 Publication Date: Published on Feb 12
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.12203
• PDF: https://arxiv.org/pdf/2602.12203
==================================
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✓ https://t.iss.one/DataScienceT
#InformationExtraction #DocumentAI #MachineLearning #Dataset #ComputerVision
📝 Summary:
ExStrucTiny is a new benchmark dataset for structured information extraction from document images. It addresses limitations of existing datasets by covering diverse document types and flexible schemas. This aims to improve generalist models for structured information extraction.
🔹 Publication Date: Published on Feb 12
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.12203
• PDF: https://arxiv.org/pdf/2602.12203
==================================
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#InformationExtraction #DocumentAI #MachineLearning #Dataset #ComputerVision
✨Towards Robust Mathematical Reasoning
📝 Summary:
IMO-Bench introduces advanced math benchmarks including short-answer and proof-writing tasks for foundation models. Gemini Deep Think achieved gold-level IMO 2025 performance using IMO-Bench, showing significant progress in robust mathematical reasoning.
🔹 Publication Date: Published on Nov 3, 2025
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.01846
• PDF: https://arxiv.org/pdf/2511.01846
• Project Page: https://imobench.github.io/
• Github: https://github.com/google-deepmind/superhuman
✨ Datasets citing this paper:
• https://huggingface.co/datasets/Hwilner/imo-answerbench
• https://huggingface.co/datasets/OpenEvals/IMO-AnswerBench
• https://huggingface.co/datasets/Hwilner/imo-gradingbench
==================================
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✓ https://t.iss.one/DataScienceT
#MathematicalReasoning #AIBenchmarks #FoundationModels #DeepLearning #IMOBench
📝 Summary:
IMO-Bench introduces advanced math benchmarks including short-answer and proof-writing tasks for foundation models. Gemini Deep Think achieved gold-level IMO 2025 performance using IMO-Bench, showing significant progress in robust mathematical reasoning.
🔹 Publication Date: Published on Nov 3, 2025
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.01846
• PDF: https://arxiv.org/pdf/2511.01846
• Project Page: https://imobench.github.io/
• Github: https://github.com/google-deepmind/superhuman
✨ Datasets citing this paper:
• https://huggingface.co/datasets/Hwilner/imo-answerbench
• https://huggingface.co/datasets/OpenEvals/IMO-AnswerBench
• https://huggingface.co/datasets/Hwilner/imo-gradingbench
==================================
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#MathematicalReasoning #AIBenchmarks #FoundationModels #DeepLearning #IMOBench
❤2
✨Stemphonic: All-at-once Flexible Multi-stem Music Generation
📝 Summary:
Stemphonic is a new AI framework that generates variable sets of synchronized musical stems in a single pass. This diffusion- and flow-based method improves quality and is 25 to 50 percent faster than previous approaches, which were either fixed or slow.
🔹 Publication Date: Published on Feb 10
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.09891
• PDF: https://arxiv.org/pdf/2602.09891
• Project Page: https://stemphonic-demo.vercel.app
==================================
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✓ https://t.iss.one/DataScienceT
#AI #MusicGeneration #MachineLearning #GenerativeAI #DiffusionModels
📝 Summary:
Stemphonic is a new AI framework that generates variable sets of synchronized musical stems in a single pass. This diffusion- and flow-based method improves quality and is 25 to 50 percent faster than previous approaches, which were either fixed or slow.
🔹 Publication Date: Published on Feb 10
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.09891
• PDF: https://arxiv.org/pdf/2602.09891
• Project Page: https://stemphonic-demo.vercel.app
==================================
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#AI #MusicGeneration #MachineLearning #GenerativeAI #DiffusionModels
❤1
✨Single-minus gluon tree amplitudes are nonzero
📝 Summary:
Single-minus gluon tree amplitudes, often presumed zero, are shown to be nonvanishing for half-collinear configurations or complex momenta. A closed-form expression is derived for their decay.
🔹 Publication Date: Published on Feb 12
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.12176
• PDF: https://arxiv.org/pdf/2602.12176
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
Single-minus gluon tree amplitudes, often presumed zero, are shown to be nonvanishing for half-collinear configurations or complex momenta. A closed-form expression is derived for their decay.
🔹 Publication Date: Published on Feb 12
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.12176
• PDF: https://arxiv.org/pdf/2602.12176
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
✨EvoCorps: An Evolutionary Multi-Agent Framework for Depolarizing Online Discourse
📝 Summary:
EvoCorps is an evolutionary multi-agent framework for proactively depolarizing online discourse. It uses dynamic social game coordination and closed-loop learning to adapt strategies in real time. EvoCorps improves discourse outcomes across emotional polarization, viewpoint extremity, and argumen...
🔹 Publication Date: Published on Feb 9
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.08529
• PDF: https://arxiv.org/pdf/2602.08529
• Github: https://github.com/ln2146/EvoCorps
✨ Datasets citing this paper:
• https://huggingface.co/datasets/loge2146/evocorps-misinformation-news
• https://huggingface.co/datasets/loge2146/evocorps-neutral-news
• https://huggingface.co/datasets/loge2146/evocorps-neutral-personas
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
EvoCorps is an evolutionary multi-agent framework for proactively depolarizing online discourse. It uses dynamic social game coordination and closed-loop learning to adapt strategies in real time. EvoCorps improves discourse outcomes across emotional polarization, viewpoint extremity, and argumen...
🔹 Publication Date: Published on Feb 9
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.08529
• PDF: https://arxiv.org/pdf/2602.08529
• Github: https://github.com/ln2146/EvoCorps
✨ Datasets citing this paper:
• https://huggingface.co/datasets/loge2146/evocorps-misinformation-news
• https://huggingface.co/datasets/loge2146/evocorps-neutral-news
• https://huggingface.co/datasets/loge2146/evocorps-neutral-personas
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
❤1
✨MemFly: On-the-Fly Memory Optimization via Information Bottleneck
📝 Summary:
MemFly addresses the challenge of long-term memory in language models by using information bottleneck principles to create an adaptive memory structure with hybrid retrieval mechanisms for improved ta...
🔹 Publication Date: Published on Feb 8
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.07885
• PDF: https://arxiv.org/pdf/2602.07885
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
MemFly addresses the challenge of long-term memory in language models by using information bottleneck principles to create an adaptive memory structure with hybrid retrieval mechanisms for improved ta...
🔹 Publication Date: Published on Feb 8
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.07885
• PDF: https://arxiv.org/pdf/2602.07885
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
✨Moonshine: Speech Recognition for Live Transcription and Voice Commands
📝 Summary:
Moonshine is an efficient transformer-based speech recognition model employing Rotary Position Embedding. It reduces compute requirements by 5x compared to Whisper Tiny.en for live transcription without sacrificing accuracy, ideal for real-time use.
🔹 Publication Date: Published on Oct 21, 2024
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2410.15608
• PDF: https://arxiv.org/pdf/2410.15608
• Github: https://github.com/usefulsensors/moonshine
🔹 Models citing this paper:
• https://huggingface.co/UsefulSensors/moonshine
• https://huggingface.co/UsefulSensors/moonshine-base
• https://huggingface.co/UsefulSensors/moonshine-tiny
✨ Spaces citing this paper:
• https://huggingface.co/spaces/microsoft/paza-bench
• https://huggingface.co/spaces/8bitkick/reachy_mini_reactions
• https://huggingface.co/spaces/fastrtc/moonshine-live
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
Moonshine is an efficient transformer-based speech recognition model employing Rotary Position Embedding. It reduces compute requirements by 5x compared to Whisper Tiny.en for live transcription without sacrificing accuracy, ideal for real-time use.
🔹 Publication Date: Published on Oct 21, 2024
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2410.15608
• PDF: https://arxiv.org/pdf/2410.15608
• Github: https://github.com/usefulsensors/moonshine
🔹 Models citing this paper:
• https://huggingface.co/UsefulSensors/moonshine
• https://huggingface.co/UsefulSensors/moonshine-base
• https://huggingface.co/UsefulSensors/moonshine-tiny
✨ Spaces citing this paper:
• https://huggingface.co/spaces/microsoft/paza-bench
• https://huggingface.co/spaces/8bitkick/reachy_mini_reactions
• https://huggingface.co/spaces/fastrtc/moonshine-live
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
arXiv.org
Moonshine: Speech Recognition for Live Transcription and Voice Commands
This paper introduces Moonshine, a family of speech recognition models optimized for live transcription and voice command processing. Moonshine is based on an encoder-decoder transformer...
✨Flavors of Moonshine: Tiny Specialized ASR Models for Edge Devices
📝 Summary:
Flavors of Moonshine are tiny monolingual ASR models for underrepresented languages. They outperform larger multilingual models by using balanced data, achieving 48% lower error rates. This enables accurate on-device speech recognition.
🔹 Publication Date: Published on Sep 2, 2025
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2509.02523
• PDF: https://arxiv.org/pdf/2509.02523
• Github: https://github.com/moonshine-ai/moonshine
🔹 Models citing this paper:
• https://huggingface.co/UsefulSensors/moonshine-tiny-ja
• https://huggingface.co/UsefulSensors/moonshine-tiny-ar
• https://huggingface.co/UsefulSensors/moonshine-tiny-zh
✨ Spaces citing this paper:
• https://huggingface.co/spaces/wmoto-ai/moonshine-tiny-ja-demo
==================================
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✓ https://t.iss.one/DataScienceT
#ASR #EdgeAI #LowResourceLanguages #MachineLearning #TinyML
📝 Summary:
Flavors of Moonshine are tiny monolingual ASR models for underrepresented languages. They outperform larger multilingual models by using balanced data, achieving 48% lower error rates. This enables accurate on-device speech recognition.
🔹 Publication Date: Published on Sep 2, 2025
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2509.02523
• PDF: https://arxiv.org/pdf/2509.02523
• Github: https://github.com/moonshine-ai/moonshine
🔹 Models citing this paper:
• https://huggingface.co/UsefulSensors/moonshine-tiny-ja
• https://huggingface.co/UsefulSensors/moonshine-tiny-ar
• https://huggingface.co/UsefulSensors/moonshine-tiny-zh
✨ Spaces citing this paper:
• https://huggingface.co/spaces/wmoto-ai/moonshine-tiny-ja-demo
==================================
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#ASR #EdgeAI #LowResourceLanguages #MachineLearning #TinyML
✨Kronos: A Foundation Model for the Language of Financial Markets
📝 Summary:
Kronos is a novel foundation model for financial K-line data, employing a specialized tokenizer and autoregressive pre-training on a massive dataset. It significantly outperforms existing models in forecasting, volatility prediction, and generating synthetic financial data.
🔹 Publication Date: Published on Aug 2, 2025
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.02739
• PDF: https://arxiv.org/pdf/2508.02739
• Github: https://github.com/shiyu-coder/Kronos
🔹 Models citing this paper:
• https://huggingface.co/NeoQuasar/Kronos-base
• https://huggingface.co/NeoQuasar/Kronos-Tokenizer-base
• https://huggingface.co/NeoQuasar/Kronos-mini
✨ Spaces citing this paper:
• https://huggingface.co/spaces/xianqiu/qlang
• https://huggingface.co/spaces/ByronWang2005/Kronos-CS2-Skins-Forecast-Demo
• https://huggingface.co/spaces/superyan/kronos-jp
==================================
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✓ https://t.iss.one/DataScienceT
#FinancialAI #FoundationModels #DeepLearning #QuantitativeFinance #MarketPrediction
📝 Summary:
Kronos is a novel foundation model for financial K-line data, employing a specialized tokenizer and autoregressive pre-training on a massive dataset. It significantly outperforms existing models in forecasting, volatility prediction, and generating synthetic financial data.
🔹 Publication Date: Published on Aug 2, 2025
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.02739
• PDF: https://arxiv.org/pdf/2508.02739
• Github: https://github.com/shiyu-coder/Kronos
🔹 Models citing this paper:
• https://huggingface.co/NeoQuasar/Kronos-base
• https://huggingface.co/NeoQuasar/Kronos-Tokenizer-base
• https://huggingface.co/NeoQuasar/Kronos-mini
✨ Spaces citing this paper:
• https://huggingface.co/spaces/xianqiu/qlang
• https://huggingface.co/spaces/ByronWang2005/Kronos-CS2-Skins-Forecast-Demo
• https://huggingface.co/spaces/superyan/kronos-jp
==================================
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#FinancialAI #FoundationModels #DeepLearning #QuantitativeFinance #MarketPrediction
arXiv.org
Kronos: A Foundation Model for the Language of Financial Markets
The success of large-scale pre-training paradigm, exemplified by Large Language Models (LLMs), has inspired the development of Time Series Foundation Models (TSFMs). However, their application to...
❤1
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❤1
✨MedXIAOHE: A Comprehensive Recipe for Building Medical MLLMs
📝 Summary:
MedXIAOHE is a medical vision-language foundation model achieving state-of-the-art performance. It uses entity-aware pretraining, reinforcement learning, and tool-augmented training for reliable, expert-level diagnostic reasoning with low hallucination.
🔹 Publication Date: Published on Feb 13
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.12705
• PDF: https://arxiv.org/pdf/2602.12705
==================================
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#MedicalAI #MLLMs #VisionLanguage #DiagnosticAI #FoundationModels
📝 Summary:
MedXIAOHE is a medical vision-language foundation model achieving state-of-the-art performance. It uses entity-aware pretraining, reinforcement learning, and tool-augmented training for reliable, expert-level diagnostic reasoning with low hallucination.
🔹 Publication Date: Published on Feb 13
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.12705
• PDF: https://arxiv.org/pdf/2602.12705
==================================
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#MedicalAI #MLLMs #VisionLanguage #DiagnosticAI #FoundationModels
✨GeoAgent: Learning to Geolocate Everywhere with Reinforced Geographic Characteristics
📝 Summary:
GeoAgent improves geolocation reasoning by using GeoSeek, a new expert-annotated dataset, and novel geo-similarity and consistency rewards. This ensures geographic accuracy and reasoning consistency. It outperforms existing methods and generates human-aligned conclusions.
🔹 Publication Date: Published on Feb 13
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.12617
• PDF: https://arxiv.org/pdf/2602.12617
• Project Page: https://ghost233lism.github.io/GeoAgent-page/
• Github: https://github.com/HVision-NKU/GeoAgent
🔹 Models citing this paper:
• https://huggingface.co/ghost233lism/GeoAgent
✨ Datasets citing this paper:
• https://huggingface.co/datasets/ghost233lism/GeoSeek
==================================
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#Geolocation #AI #ReinforcementLearning #GeospatialAI #DataScience
📝 Summary:
GeoAgent improves geolocation reasoning by using GeoSeek, a new expert-annotated dataset, and novel geo-similarity and consistency rewards. This ensures geographic accuracy and reasoning consistency. It outperforms existing methods and generates human-aligned conclusions.
🔹 Publication Date: Published on Feb 13
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.12617
• PDF: https://arxiv.org/pdf/2602.12617
• Project Page: https://ghost233lism.github.io/GeoAgent-page/
• Github: https://github.com/HVision-NKU/GeoAgent
🔹 Models citing this paper:
• https://huggingface.co/ghost233lism/GeoAgent
✨ Datasets citing this paper:
• https://huggingface.co/datasets/ghost233lism/GeoSeek
==================================
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#Geolocation #AI #ReinforcementLearning #GeospatialAI #DataScience
✨Towards Universal Video MLLMs with Attribute-Structured and Quality-Verified Instructions
📝 Summary:
Researchers created ASID-1M, a dataset of structured, quality-verified audiovisual instructions, and ASID-Captioner, a model trained on it. This improves fine-grained caption quality, reduces hallucinations, and achieves SOTA results.
🔹 Publication Date: Published on Feb 13
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.13013
• PDF: https://arxiv.org/pdf/2602.13013
• Github: https://github.com/ASID-Caption/ASID-Caption
🔹 Models citing this paper:
• https://huggingface.co/AudioVisual-Caption/ASID-Captioner-3B
• https://huggingface.co/AudioVisual-Caption/ASID-Captioner-7B
✨ Datasets citing this paper:
• https://huggingface.co/datasets/AudioVisual-Caption/ASID-1M
==================================
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✓ https://t.iss.one/DataScienceT
#MLLM #VideoAI #DeepLearning #ComputerVision #NLP
📝 Summary:
Researchers created ASID-1M, a dataset of structured, quality-verified audiovisual instructions, and ASID-Captioner, a model trained on it. This improves fine-grained caption quality, reduces hallucinations, and achieves SOTA results.
🔹 Publication Date: Published on Feb 13
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.13013
• PDF: https://arxiv.org/pdf/2602.13013
• Github: https://github.com/ASID-Caption/ASID-Caption
🔹 Models citing this paper:
• https://huggingface.co/AudioVisual-Caption/ASID-Captioner-3B
• https://huggingface.co/AudioVisual-Caption/ASID-Captioner-7B
✨ Datasets citing this paper:
• https://huggingface.co/datasets/AudioVisual-Caption/ASID-1M
==================================
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#MLLM #VideoAI #DeepLearning #ComputerVision #NLP
✨Zooming without Zooming: Region-to-Image Distillation for Fine-Grained Multimodal Perception
📝 Summary:
MLLMs struggle with fine-grained perception due to latency from iterative zooming. Region-to-Image Distillation internalizes zooming into a single forward pass by training a model on region-grounded data. This significantly improves fine-grained perception without tool calls, achieving leading pe...
🔹 Publication Date: Published on Feb 12
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.11858
• PDF: https://arxiv.org/pdf/2602.11858
• Github: https://github.com/inclusionAI/Zooming-without-Zooming
🔹 Models citing this paper:
• https://huggingface.co/inclusionAI/ZwZ-8B
• https://huggingface.co/inclusionAI/ZwZ-4B
• https://huggingface.co/inclusionAI/ZwZ-7B
✨ Datasets citing this paper:
• https://huggingface.co/datasets/inclusionAI/ZwZ-RL-VQA
• https://huggingface.co/datasets/inclusionAI/ZoomBench
==================================
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#MultimodalAI #ComputerVision #FineGrainedPerception #DeepLearning #ModelDistillation
📝 Summary:
MLLMs struggle with fine-grained perception due to latency from iterative zooming. Region-to-Image Distillation internalizes zooming into a single forward pass by training a model on region-grounded data. This significantly improves fine-grained perception without tool calls, achieving leading pe...
🔹 Publication Date: Published on Feb 12
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.11858
• PDF: https://arxiv.org/pdf/2602.11858
• Github: https://github.com/inclusionAI/Zooming-without-Zooming
🔹 Models citing this paper:
• https://huggingface.co/inclusionAI/ZwZ-8B
• https://huggingface.co/inclusionAI/ZwZ-4B
• https://huggingface.co/inclusionAI/ZwZ-7B
✨ Datasets citing this paper:
• https://huggingface.co/datasets/inclusionAI/ZwZ-RL-VQA
• https://huggingface.co/datasets/inclusionAI/ZoomBench
==================================
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✓ https://t.iss.one/DataScienceT
#MultimodalAI #ComputerVision #FineGrainedPerception #DeepLearning #ModelDistillation
arXiv.org
Zooming without Zooming: Region-to-Image Distillation for...
Multimodal Large Language Models (MLLMs) excel at broad visual understanding but still struggle with fine-grained perception, where decisive evidence is small and easily overwhelmed by global...
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✨OneVision-Encoder: Codec-Aligned Sparsity as a Foundational Principle for Multimodal Intelligence
📝 Summary:
OneVision-Encoder improves visual understanding by aligning architectures with video compression principles. It uses codec-aligned sparsity to focus on high-entropy regions, significantly boosting efficiency and accuracy. This method outperforms strong vision backbones across various benchmarks, ...
🔹 Publication Date: Published on Feb 9
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.08683
• PDF: https://arxiv.org/pdf/2602.08683
• Project Page: https://www.lmms-lab.com/onevision-encoder/index.html
• Github: https://github.com/EvolvingLMMs-Lab/OneVision-Encoder/blob/main/docs/data_card.md
==================================
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✓ https://t.iss.one/DataScienceT
#MultimodalAI #ComputerVision #DeepLearning #Sparsity #AIResearch
📝 Summary:
OneVision-Encoder improves visual understanding by aligning architectures with video compression principles. It uses codec-aligned sparsity to focus on high-entropy regions, significantly boosting efficiency and accuracy. This method outperforms strong vision backbones across various benchmarks, ...
🔹 Publication Date: Published on Feb 9
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.08683
• PDF: https://arxiv.org/pdf/2602.08683
• Project Page: https://www.lmms-lab.com/onevision-encoder/index.html
• Github: https://github.com/EvolvingLMMs-Lab/OneVision-Encoder/blob/main/docs/data_card.md
==================================
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#MultimodalAI #ComputerVision #DeepLearning #Sparsity #AIResearch
✨Intelligent AI Delegation
📝 Summary:
AI agents require better task decomposition and robust delegation. This paper proposes an adaptive framework for intelligent AI delegation, incorporating authority transfer, responsibility, and trust to handle dynamic environments and failures in complex AI and human networks.
🔹 Publication Date: Published on Feb 12
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.11865
• PDF: https://arxiv.org/pdf/2602.11865
==================================
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✓ https://t.iss.one/DataScienceT
#AIDelegation #AIagents #TaskDecomposition #HumanAICollaboration #MultiAgentSystems
📝 Summary:
AI agents require better task decomposition and robust delegation. This paper proposes an adaptive framework for intelligent AI delegation, incorporating authority transfer, responsibility, and trust to handle dynamic environments and failures in complex AI and human networks.
🔹 Publication Date: Published on Feb 12
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.11865
• PDF: https://arxiv.org/pdf/2602.11865
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#AIDelegation #AIagents #TaskDecomposition #HumanAICollaboration #MultiAgentSystems