VIII. Conclusion

• A Strong Take-Home Message: Provide a short, powerful summary of the study's main conclusions.
• Reiterate Significance: Briefly restate the importance of your work without simply repeating the abstract.
• No New Information: Do not introduce any new data, ideas, or citations in the conclusion.

IX. Acknowledgments

• Give Credit: Thank any individuals who provided technical help, intellectual contributions (but not enough for authorship), or feedback.
• Disclose Funding: Acknowledge all funding sources, grants, and scholarships that supported the research.

X. References

• Cite Everything: Ensure every source cited in the text is listed in the references section, and vice-versa.
• Maintain Consistency: Use a single, consistent citation style throughout the paper (e.g., APA, MLA, IEEE, Vancouver) as required by the target journal or institution.
• Use a Reference Manager: Tools like Zotero, Mendeley, or EndNote can help manage citations and prevent errors.

XI. General Writing Tips

• Clarity and Conciseness: Use clear, direct language. Avoid jargon where possible and define it if necessary. Write short, focused sentences.
• Voice and Tense:
Methods & Results: Use the past tense ("We collected...", "The temperature increased...").
Introduction & Discussion: Use a mix of present tense for established knowledge ("Diabetes is a chronic disease...") and past tense for referring to specific studies ("Smith (2020) found...").
* Active Voice: Prefer active voice ("We conducted the experiment") over passive voice ("The experiment was conducted") for clarity, especially in the Introduction and Discussion.
• Proofread Meticulously: Check for spelling, grammar, and punctuation errors. Read your paper aloud to catch awkward phrasing. Ask a colleague to review it before submission.
• Avoid Plagiarism: Always give proper credit to the work and ideas of others through correct citation.

#AcademicWriting #ScientificPaper #Research #Publishing #GradSchool

━━━━━━━━━━━━━━━
By: @DataScienceT ✨

✨ Title: GUI-AIMA: Aligning Intrinsic Multimodal Attention with a Context Anchor for GUI Grounding

📝 Summary:
GUI-AIMA is a coordinate-free framework that improves GUI grounding by aligning MLLM attention with patch-wise signals. It's highly data-efficient, achieving state-of-the-art results among 3B models with minimal training. This approach effectively triggers MLLMs' native grounding capability.

🔹 Publication Date: Published on Nov 2

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.00810
• PDF: https://arxiv.org/pdf/2511.00810
• Project Page: https://github.com/sjz5202/GUI-AIMA
• Github: https://github.com/sjz5202/GUI-AIMA

==================================

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🤖🧠 LongCat-Video: Meituan’s Groundbreaking Step Toward Efficient Long Video Generation with AI

🗓️ 04 Nov 2025
📚 AI News & Trends

In the rapidly advancing field of generative AI, the ability to create realistic, coherent, and high-quality videos from text or images has become one of the most sought-after goals. Meituan, one of the leading technology innovators in China, has made a remarkable stride in this domain with its latest open-source model — LongCat-Video. Designed as ...

#LongCatVideo #Meituan #GenerativeAI #VideoGeneration #AIInnovation #OpenSource

✨ Title: AthenaBench: A Dynamic Benchmark for Evaluating LLMs in Cyber Threat Intelligence

📝 Summary:
AthenaBench enhances CTI LLM evaluation, revealing current models, even top proprietary ones, have limited reasoning for tasks like threat attribution and risk mitigation. This highlights fundamental LLM weaknesses and the need for CTI-specific AI.

🔹 Publication Date: Published on Nov 3

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.01144
• PDF: https://arxiv.org/pdf/2511.01144
• Github: https://github.com/Athena-Software-Group/athenabench

==================================

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✨ Title: VCode: a Multimodal Coding Benchmark with SVG as Symbolic Visual Representation

📝 Summary:
VCode introduces a benchmark to generate SVG code from images, preserving symbolic meaning. VCoder, an agentic framework, boosts VLM performance via revision and visual tools. This shows VLM limitations and the promise of symbolic visual representation.

🔹 Publication Date: Published on Nov 4

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.02778
• PDF: https://arxiv.org/pdf/2511.02778
• Project Page: https://csu-jpg.github.io/VCode/
• Github: https://github.com/CSU-JPG/VCode

==================================

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✨ Title: D2D: Detector-to-Differentiable Critic for Improved Numeracy in Text-to-Image Generation

📝 Summary:
D2D transforms non-differentiable detectors into differentiable critics for text-to-image generation. This leverages their superior counting ability to greatly improve object numeracy, boosting accuracy with minimal impact on image quality.

🔹 Publication Date: Published on Oct 22

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.19278
• PDF: https://arxiv.org/pdf/2510.19278

==================================

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✨ Title: LiveSecBench: A Dynamic and Culturally-Relevant AI Safety Benchmark for LLMs in Chinese Context

📝 Summary:
LiveSecBench is a dynamic safety benchmark for Chinese LLMs, continuously updated to reflect new threats. It evaluates models across six critical dimensions tailored to Chinese legal and social frameworks. This benchmark offers a current landscape of AI safety in China, with a public leaderboard.

🔹 Publication Date: Published on Nov 4

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.02366
• PDF: https://arxiv.org/pdf/2511.02366
• Project Page: https://livesecbench.intokentech.cn/

==================================

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✨ Title: TWIST2: Scalable, Portable, and Holistic Humanoid Data Collection System

📝 Summary:
TWIST2 is a portable, mocap-free system for efficient humanoid data collection using VR and egocentric vision. It enables whole-body human-to-humanoid control and a hierarchical visuomotor policy for autonomous complex skills.

🔹 Publication Date: Published on Nov 4

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.02832
• PDF: https://arxiv.org/pdf/2511.02832
• Project Page: https://yanjieze.com/TWIST2/

==================================

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✨ Title: iFlyBot-VLA Technical Report

📝 Summary:
iFlyBot-VLA is a large VLA model that uses a latent action model and dual-level action representation. This enhances 3D perception and reasoning, achieving superior performance in diverse manipulation tasks.

🔹 Publication Date: Published on Nov 1

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.01914
• PDF: https://arxiv.org/pdf/2511.01914
• Project Page: https://xuwenjie401.github.io/iFlyBot-VLA.github.io/

==================================

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✨ Title: VidEmo: Affective-Tree Reasoning for Emotion-Centric Video Foundation Models

📝 Summary:
This paper introduces VidEmo, a new video emotion foundation model that uses an affective cues-guided reasoning framework. It is trained on the Emo-CFG dataset and achieves competitive performance in emotion understanding and face perception tasks.

🔹 Publication Date: Published on Nov 4

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.02712
• PDF: https://arxiv.org/pdf/2511.02712
• Project Page: https://zzcheng.top/VidEmo

==================================

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✨ Title: ChartM^3: A Multi-Stage Code-Driven Pipeline for Constructing Multi-Dimensional and Multi-Step Visual Reasoning Data in Chart Comprehension

📝 Summary:
A new automated code-driven pipeline, ChartM^3, generates diverse datasets for complex chart understanding via RAG and CoT. This improves MLLM reasoning and generalization, enabling smaller models to match larger ones in complex chart comprehension.

🔹 Publication Date: Published on Nov 4

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.02415
• PDF: https://arxiv.org/pdf/2511.02415

==================================

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✨ Title: Discriminately Treating Motion Components Evolves Joint Depth and Ego-Motion Learning

📝 Summary:
DiMoDE introduces a discriminative treatment of motion components for robust joint depth and ego-motion learning. By leveraging geometric constraints and reforming the learning process, it improves accuracy and achieves state-of-the-art performance.

🔹 Publication Date: Published on Nov 3

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.01502
• PDF: https://arxiv.org/pdf/2511.01502

==================================

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✨ Title: VCode: a Multimodal Coding Benchmark with SVG as Symbolic Visual Representation

📝 Summary:
VCode introduces a benchmark for generating SVG code from images, preserving symbolic meaning for visual reasoning. Frontier VLMs struggle with this visual-centric task. VCoder, an agentic framework, improves performance using iterative revision and visual tools.

🔹 Publication Date: Published on Nov 4

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.02778
• PDF: https://arxiv.org/pdf/2511.02778
• Project Page: https://csu-jpg.github.io/VCode/
• Github: https://github.com/CSU-JPG/VCode

==================================

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#VCode #MultimodalAI #SVG #VisualReasoning #VLMs

✨ Title: When Visualizing is the First Step to Reasoning: MIRA, a Benchmark for Visual Chain-of-Thought

📝 Summary:
MIRA is a new benchmark for evaluating models that use intermediate visual images to enhance reasoning. It includes 546 multimodal problems requiring models to generate and utilize visual cues. Experiments show models achieve a 33.7% performance gain with visual cues compared to text-only prompts...

🔹 Publication Date: Published on Nov 4

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.02779
• PDF: https://arxiv.org/pdf/2511.02779

==================================

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#VisualReasoning #ChainOfThought #MultimodalAI #AIBenchmark #ComputerVision

✨When Modalities Conflict: How Unimodal Reasoning Uncertainty Governs Preference Dynamics in MLLMs

📝 Summary:
A new framework explains MLLM conflict resolution by decomposing modality following into relative reasoning uncertainty and inherent modality preference. Modality following decreases with relative uncertainty. Inherent preference is measured at the balance point, offering mechanistic insights.

🔹 Publication Date: Published on Nov 4

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.02243
• PDF: https://arxiv.org/pdf/2511.02243

==================================

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#MLLMs #MultimodalAI #LLM #DeepLearning #AIResearch

✨Shorter but not Worse: Frugal Reasoning via Easy Samples as Length Regularizers in Math RLVR

📝 Summary:
LLMs for step-by-step reasoning become verbose as RLVR often filters easy problems. This work shows that retaining and modestly up-weighting moderately easy problems acts as an implicit length regularizer. This approach significantly reduces output verbosity by half while maintaining accuracy, wi...

🔹 Publication Date: Published on Nov 2

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.01937
• PDF: https://arxiv.org/pdf/2511.01937
• Github: https://github.com/MBZUAI-Paris/Frugal-AI-Math

🔹 Models citing this paper:
• https://huggingface.co/MBZUAI-Paris/Frugal-Math-4B

✨ Datasets citing this paper:
• https://huggingface.co/datasets/MBZUAI-Paris/frugal-maths-data-split-v1

==================================

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#LLM #AI #ReinforcementLearning #FrugalAI #MathematicalReasoning

✨BRAINS: A Retrieval-Augmented System for Alzheimer's Detection and Monitoring

📝 Summary:
BRAINS is an LLM-based system for Alzheimer's detection and monitoring. It integrates cognitive assessments and a case retrieval module for risk assessment and disease severity classification. Evaluations demonstrate its effectiveness as a scalable, explainable, early-stage detection tool.

🔹 Publication Date: Published on Nov 4

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.02490
• PDF: https://arxiv.org/pdf/2511.02490

==================================

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#Alzheimers #LLM #AI #MedicalAI #EarlyDetection

✨Kimi Linear: An Expressive, Efficient Attention Architecture

📝 Summary:
Kimi Linear is a new hybrid linear attention architecture that outperforms full attention in performance and efficiency across diverse scenarios. It leverages Kimi Delta Attention and Multi-Head Latent Attention, reducing KV cache by up to 75% and boosting decoding throughput by 6x.

🔹 Publication Date: Published on Oct 30

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.26692
• PDF: https://arxiv.org/pdf/2510.26692
• Github: https://github.com/MoonshotAI/Kimi-Linear

🔹 Models citing this paper:
• https://huggingface.co/moonshotai/Kimi-Linear-48B-A3B-Instruct
• https://huggingface.co/moonshotai/Kimi-Linear-48B-A3B-Base
• https://huggingface.co/aiqtech/Kimi-Linear-48B-A3B-Instruct

✨ Spaces citing this paper:
• https://huggingface.co/spaces/Speedofmastery/orynxml-agents

==================================

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#AttentionMechanisms #LLM #AIResearch #DeepLearning #ModelEfficiency

✨PaddleOCR-VL: Boosting Multilingual Document Parsing via a 0.9B Ultra-Compact Vision-Language Model

📝 Summary:
PaddleOCR-VL is a new 0.9B vision-language model for document parsing. It uses a NaViT-style visual encoder and ERNIE-4.5, achieving state-of-the-art performance across 109 languages with minimal resources and fast inference. This model is highly suitable for practical deployment.

🔹 Publication Date: Published on Oct 16

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.14528
• PDF: https://arxiv.org/pdf/2510.14528
• Github: https://github.com/PaddlePaddle/PaddleOCR

🔹 Models citing this paper:
• https://huggingface.co/PaddlePaddle/PaddleOCR-VL
• https://huggingface.co/PaddlePaddle/PP-DocLayoutV2
• https://huggingface.co/lvyufeng/PaddleOCR-VL-0.9B

✨ Spaces citing this paper:
• https://huggingface.co/spaces/PaddlePaddle/PaddleOCR-VL_Online_Demo
• https://huggingface.co/spaces/markobinario/PaddleOCR-VL_Online_Demo
• https://huggingface.co/spaces/waytoAGI/PaddleOCR-VL_Online_Demo

==================================

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#OCR #VisionLanguageModel #DocumentAI #DeepLearning #AI