✨Less is More: Recursive Reasoning with Tiny Networks

📝 Summary:
Tiny Recursive Model TRM uses a simple, two-layer network for recursive reasoning. It significantly outperforms larger language models on complex puzzle tasks like ARC-AGI, achieving high generalization with vastly fewer parameters. TRM demonstrates superior performance with minimal resources.

🔹 Publication Date: Published on Oct 6

🔹 Paper Links:
• arXiv Page: https://arxivexplained.com/papers/less-is-more-recursive-reasoning-with-tiny-networks
• PDF: https://arxiv.org/pdf/2510.04871
• Project Page: https://alexiajm.github.io/2025/09/29/tiny_recursive_models.html
• Github: https://github.com/SamsungSAILMontreal/TinyRecursiveModels/issues/2

🔹 Models citing this paper:
• https://huggingface.co/wtfmahe/Samsung-TRM
• https://huggingface.co/ordlibrary/X402

✨ Datasets citing this paper:
• https://huggingface.co/datasets/emiliocantuc/sudoku-extreme-1k-aug-1000

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#RecursiveReasoning #TinyAI #EfficientAI #AIResearch #MachineLearning

✨Brain-IT: Image Reconstruction from fMRI via Brain-Interaction Transformer

📝 Summary:
Brain-IT reconstructs high-fidelity images from fMRI using a Brain Interaction Transformer. It surpasses current methods visually and objectively, and requires significantly less training data for new subjects.

🔹 Publication Date: Published on Oct 29

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.25976
• PDF: https://arxiv.org/pdf/2510.25976
• Project Page: https://amitzalcher.github.io/Brain-IT/
• Github: https://amitzalcher.github.io/Brain-IT/

✨ Datasets citing this paper:
• https://huggingface.co/datasets/Amitz244/Brain-IT_Results

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#fMRI #ImageReconstruction #DeepLearning #Neuroscience #BrainIT

✨Can Visual Input Be Compressed? A Visual Token Compression Benchmark for Large Multimodal Models

📝 Summary:
UniPruneBench is a new benchmark for evaluating visual token pruning in large multimodal models LMMs. It standardizes evaluation across tasks and models, revealing that random pruning is a strong baseline and OCR is most sensitive to pruning. The pruning ratio greatly impacts performance.

🔹 Publication Date: Published on Nov 4

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.02650
• PDF: https://arxiv.org/pdf/2511.02650
• Project Page: https://uniprunebench-lmm.github.io/
• Github: https://github.com/TianfanPeng/VLMUniPruneBench

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#LMMs #VisualCompression #DeepLearning #ComputerVision #AIResearch

✨CodeClash: Benchmarking Goal-Oriented Software Engineering

📝 Summary:
CodeClash is a benchmark evaluating language models on open-ended, goal-oriented code development through competitive tournaments. It shows LMs struggle with strategic reasoning and long-term codebase maintenance, performing poorly against human experts.

🔹 Publication Date: Published on Nov 2

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.00839
• PDF: https://arxiv.org/pdf/2511.00839

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#LanguageModels #SoftwareEngineering #AIEvaluation #CodeDevelopment #Benchmarking

✨TabDSR: Decompose, Sanitize, and Reason for Complex Numerical Reasoning in Tabular Data

📝 Summary:
TabDSR improves LLM performance on complex tabular numerical reasoning by decomposing queries, sanitizing tables, and using program-of-thoughts reasoning. It achieves state-of-the-art accuracy, consistently outperforming existing methods.

🔹 Publication Date: Published on Nov 4

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.02219
• PDF: https://arxiv.org/pdf/2511.02219

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#LLM #TabularData #NumericalReasoning #DataScience #AI

✨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

✨The Collaboration Gap

📝 Summary:
A new benchmark reveals a collaboration gap where AI models performing well solo degrade significantly when paired. Starting with a stronger agent relay inference helps bridge this gap. This suggests a need for collaboration-aware evaluation and training.

🔹 Publication Date: Published on Nov 4

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.02687
• PDF: https://arxiv.org/pdf/2511.02687

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#AI #Collaboration #MultiAgentSystems #AIResearch #AIEvaluation

✨LightRAG: Simple and Fast Retrieval-Augmented Generation

📝 Summary:
LightRAG improves Retrieval-Augmented Generation by addressing limitations of flat data representations and inadequate contextual awareness. It integrates graph structures into text indexing and retrieval, enhancing accuracy, efficiency, and response times through a dual-level system.

🔹 Publication Date: Published on Oct 8, 2024

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2410.05779
• PDF: https://arxiv.org/pdf/2410.05779
• Github: https://github.com/hkuds/lightrag

✨ Spaces citing this paper:
• https://huggingface.co/spaces/rm-lht/lightrag

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

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#RAG #AI #NLP #GraphAI #InformationRetrieval

✨RiddleBench: A New Generative Reasoning Benchmark for LLMs

📝 Summary:
RiddleBench, a new benchmark of 1,737 puzzles, reveals fundamental weaknesses in state-of-the-art LLMs, including hallucination cascades and poor self-correction. Models achieve only about 60% accuracy, underscoring the need for more robust and reliable reasoning capabilities.

🔹 Publication Date: Published on Oct 28

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.24932
• PDF: https://arxiv.org/pdf/2510.24932

✨ Datasets citing this paper:
• https://huggingface.co/datasets/ai4bharat/RiddleBench

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

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#LLMs #GenerativeAI #AIResearch #Benchmarks #NLP

✨AgentScope 1.0: A Developer-Centric Framework for Building Agentic Applications

📝 Summary:
AgentScope 1.0 is a developer-centric framework for building agentic applications. It offers flexible tool-based interactions, unified interfaces, and ReAct-based infrastructure to enable efficient and safe development and deployment.

🔹 Publication Date: Published on Aug 22

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.16279
• PDF: https://arxiv.org/pdf/2508.16279
• Github: https://github.com/agentscope-ai/agentscope

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#AIAgents #AIdevelopment #SoftwareFramework #AItools #ReActAI

✨RoboChallenge: Large-scale Real-robot Evaluation of Embodied Policies

📝 Summary:
RoboChallenge is an online evaluation system for robotic control algorithms, especially VLA models. It enables large-scale, reproducible real-robot testing to survey state-of-the-art models.

🔹 Publication Date: Published on Oct 20

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.17950
• PDF: https://arxiv.org/pdf/2510.17950

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#Robotics #AI #MachineLearning #EmbodiedAI #RoboticsEvaluation

✨Reg-DPO: SFT-Regularized Direct Preference Optimization with GT-Pair for Improving Video Generation

📝 Summary:
This paper presents GT-Pair for automatic preference data construction and Reg-DPO, which adds SFT loss to DPO for stable training. Combined with memory optimizations, it significantly improves video generation quality, outperforming existing methods.

🔹 Publication Date: Published on Nov 3

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.01450
• PDF: https://arxiv.org/pdf/2511.01450
• Github: https://github.com/JieDuTQS/Reg-DPO

🔹 Models citing this paper:
• https://huggingface.co/dujielvtqs/Reg-DPO

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#VideoGeneration #GenerativeAI #DeepLearning #DPO #AIResearch

✨AyurParam: A State-of-the-Art Bilingual Language Model for Ayurveda

📝 Summary:
AyurParam-2.9B is a bilingual language model for Ayurveda, outperforming smaller models and competing with larger ones on medical tasks. Highlighting the need for domain adaptation and quality data.

🔹 Publication Date: Published on Nov 4

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.02374
• PDF: https://arxiv.org/pdf/2511.02374

🔹 Models citing this paper:
• https://huggingface.co/bharatgenai/AyurParam

✨ Spaces citing this paper:
• https://huggingface.co/spaces/Swanand3/BharatGen_AyurParam

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

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#Ayurveda #LanguageModel #BilingualAI #NLP #HealthcareAI

✨3D Gaussian Splatting for Real-Time Radiance Field Rendering

📝 Summary:
This paper introduces a method using 3D Gaussians for scene representation to achieve state-of-the-art, high-quality real-time novel-view synthesis at 1080p resolution. It optimizes anisotropic Gaussians and uses a fast rendering algorithm, outperforming previous radiance field methods.

🔹 Publication Date: Published on Aug 8, 2023

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2308.04079
• PDF: https://arxiv.org/pdf/2308.04079
• Github: https://github.com/graphdeco-inria/gaussian-splatting

✨ Datasets citing this paper:
• https://huggingface.co/datasets/Voxel51/gaussian_splatting

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

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#3DGaussianSplatting #RadianceFields #ComputerGraphics #RealTimeRendering #NovelViewSynthesis

🤖🧠 Krea Realtime 14B: Redefining Real-Time Video Generation with AI

🗓️ 05 Nov 2025
📚 AI News & Trends

The field of artificial intelligence is undergoing a remarkable transformation and one of the most exciting developments is the rise of real-time video generation. From cinematic visual effects to immersive virtual environments, AI is rapidly blurring the boundaries between imagination and reality. At the forefront of this innovation stands Krea Realtime 14B, an advanced open-source ...

#AI #RealTimeVideo #ArtificialIntelligence #OpenSource #VideoGeneration #KreaRealtime14B

✨DyPE: Dynamic Position Extrapolation for Ultra High Resolution Diffusion

📝 Summary:
DyPE enhances diffusion transformers for ultra-high-resolution image generation by dynamically adjusting positional encodings. This training-free method allows pre-trained models to synthesize images far beyond their training resolution, achieving state-of-the-art fidelity without extra sampling ...

🔹 Publication Date: Published on Oct 23

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.20766
• PDF: https://arxiv.org/pdf/2510.20766
• Project Page: https://noamissachar.github.io/DyPE/
• Github: https://github.com/guyyariv/DyPE

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#DiffusionModels #ImageGeneration #HighResolution #DeepLearning #ComputerVision

✨MME-CC: A Challenging Multi-Modal Evaluation Benchmark of Cognitive Capacity

📝 Summary:
MME-CC is a new vision-grounded benchmark to evaluate multimodal large language models cognitive capacity in spatial, geometric, and knowledge-based reasoning tasks. It reveals that while some models lead, spatial and geometric reasoning remain broadly weak. This highlights the need for better ev...

🔹 Publication Date: Published on Nov 5

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.03146
• PDF: https://arxiv.org/pdf/2511.03146
• Project Page: https://randomtutu.github.io/MME-CC/

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#MultimodalAI #LLMs #Benchmarking #CognitiveAI #ComputerVision

✨LEGO-Eval: Towards Fine-Grained Evaluation on Synthesizing 3D Embodied Environments with Tool Augmentation

📝 Summary:
The paper introduces LEGO-Eval, a tool-augmented framework, and LEGO-Bench, a detailed instruction benchmark, to improve 3D scene evaluation. It shows LEGO-Eval accurately assesses scene-instruction alignment, outperforming VLMs, and current generation methods largely fail to create realistic sce...

🔹 Publication Date: Published on Nov 4

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.03001
• PDF: https://arxiv.org/pdf/2511.03001
• Project Page: https://gyeomh.github.io/LEGO-Eval/

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#EmbodiedAI #3DGeneration #EvaluationMetrics #VLMs #Benchmarking

✨Let Multimodal Embedders Learn When to Augment Query via Adaptive Query Augmentation

📝 Summary:
M-Solomon is a multimodal embedder that adaptively decides when to augment queries. It uses a Multimodal LLM to generate augmentations for queries that require them, learning to augment only when necessary. This approach improves performance and significantly reduces embedding latency compared to...

🔹 Publication Date: Published on Nov 4

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.02358
• PDF: https://arxiv.org/pdf/2511.02358

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

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#MultimodalAI #LLM #Embeddings #MachineLearning #DeepLearning