✨Segment Length Matters: A Study of Segment Lengths on Audio Fingerprinting Performance

📝 Summary:
Neural audio fingerprinting performance varies with segment length, with short segments (0.5-second) generally providing better retrieval accuracy, and large language models showing promise in recomme...

🔹 Publication Date: Published on Jan 25

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.17690
• PDF: https://arxiv.org/pdf/2601.17690

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✨PRISM: Learning Design Knowledge from Data for Stylistic Design Improvement

📝 Summary:
PRISM leverages design data to create a knowledge base for improving graphic designs based on natural language instructions, achieving superior style alignment compared to existing methods. AI-generat...

🔹 Publication Date: Published on Jan 16

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.11747
• PDF: https://arxiv.org/pdf/2601.11747

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✨WorldBench: Disambiguating Physics for Diagnostic Evaluation of World Models

📝 Summary:
WorldBench is introduced as a video-based benchmark for disentangled evaluation of physical reasoning in generative models, revealing specific failure patterns in current state-of-the-art video world ...

🔹 Publication Date: Published on Jan 29

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.21282
• PDF: https://arxiv.org/pdf/2601.21282
• Project Page: https://world-bench.github.io/

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✨Idea2Story: An Automated Pipeline for Transforming Research Concepts into Complete Scientific Narratives

📝 Summary:
Offline knowledge construction through structured methodological graphs enables more reliable and scalable autonomous scientific discovery by reducing reliance on real-time literature processing. AI-g...

🔹 Publication Date: Published on Jan 28

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.20833
• PDF: https://arxiv.org/pdf/2601.20833

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✨OCRVerse: Towards Holistic OCR in End-to-End Vision-Language Models

📝 Summary:
OCRVerse unifies text and vision-centric OCR into a holistic end-to-end method for diverse visual documents. It uses comprehensive data and a two-stage SFT-RL training with domain-specific rewards to achieve competitive results.

🔹 Publication Date: Published on Jan 29

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.21639
• PDF: https://arxiv.org/pdf/2601.21639

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✨MetricAnything: Scaling Metric Depth Pretraining with Noisy Heterogeneous Sources

📝 Summary:
Metric Anything introduces a scalable pretraining framework for metric depth using Sparse Metric Prompts to handle diverse, noisy 3D data. It shows clear scaling trends and achieves state-of-the-art performance across various depth estimation and spatial intelligence tasks.

🔹 Publication Date: Published on Jan 29

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.22054
• PDF: https://arxiv.org/pdf/2601.22054
• Project Page: https://metric-anything.github.io/metric-anything-io/
• Github: https://github.com/metric-anything/metric-anything

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✨BMAM: Brain-inspired Multi-Agent Memory Framework

📝 Summary:
BMAM presents a brain-inspired multi-agent memory architecture that decomposes memory into specialized subsystems to address long-term reasoning challenges in language-model-based agents. AI-generated...

🔹 Publication Date: Published on Jan 28

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.20465
• PDF: https://arxiv.org/pdf/2601.20465
• Github: https://github.com/innovation64/BMAM

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✨Spotlighting Task-Relevant Features: Object-Centric Representations for Better Generalization in Robotic Manipulation

📝 Summary:
Slot-Based Object-Centric Representations outperform global and dense feature representations in robotic manipulation tasks by providing better generalization under visual distribution shifts. AI-gene...

🔹 Publication Date: Published on Jan 29

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.21416
• PDF: https://arxiv.org/pdf/2601.21416

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✨FROST: Filtering Reasoning Outliers with Attention for Efficient Reasoning

📝 Summary:
FROST is an attention-aware method that improves reasoning efficiency by pruning uncritical paths and removing reasoning outliers, leading to reduced token usage and improved accuracy. AI-generated su...

🔹 Publication Date: Published on Jan 26

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.19001
• PDF: https://arxiv.org/pdf/2601.19001

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✨ECO: Quantized Training without Full-Precision Master Weights

📝 Summary:
Error-compensating optimizer eliminates memory overhead from master weights in quantized LLM training while maintaining near-lossless accuracy. AI-generated summary Quantization has significantly impr...

🔹 Publication Date: Published on Jan 29

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.22101
• PDF: https://arxiv.org/pdf/2601.22101

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