✨ATLAS: A High-Difficulty, Multidisciplinary Benchmark for Frontier Scientific Reasoning

📝 Summary:
ATLAS is a new, high-difficulty, multidisciplinary benchmark for LLMs, featuring 800 original problems across seven scientific fields. It addresses current benchmark limitations with complex, open-ended answers and aims to differentiate advanced scientific reasoning, serving as a ruler for AGI pr...

🔹 Publication Date: Published on Nov 18

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.14366
• PDF: https://arxiv.org/pdf/2511.14366

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#LLM #AGI #AIResearch #ScientificReasoning #Benchmark

✨Orion: A Unified Visual Agent for Multimodal Perception, Advanced Visual Reasoning and Execution

📝 Summary:
Orion is a visual agent framework that orchestrates specialized computer vision tools to execute complex visual workflows. It achieves competitive performance on benchmarks and enables autonomous, tool-driven visual reasoning.

🔹 Publication Date: Published on Nov 18

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.14210
• PDF: https://arxiv.org/pdf/2511.14210

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#ComputerVision #AIagents #VisualReasoning #MultimodalAI #DeepLearning

✨A Style is Worth One Code: Unlocking Code-to-Style Image Generation with Discrete Style Space

📝 Summary:
CoTyle introduces code-to-style image generation, creating consistent visual styles from numerical codes. It is the first open-source academic method for this task, using a discrete style codebook and a text-to-image diffusion model for diverse, reproducible styles.

🔹 Publication Date: Published on Nov 13

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.10555
• PDF: https://arxiv.org/pdf/2511.10555
• Project Page: https://Kwai-Kolors.github.io/CoTyle/
• Github: https://github.com/Kwai-Kolors/CoTyle

✨ Spaces citing this paper:
• https://huggingface.co/spaces/Kwai-Kolors/CoTyle

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#ImageGeneration #DiffusionModels #NeuralStyle #ComputerVision #DeepLearning

✨MVI-Bench: A Comprehensive Benchmark for Evaluating Robustness to Misleading Visual Inputs in LVLMs

📝 Summary:
MVI-Bench introduces a new benchmark to evaluate Large Vision-Language Models robustness against misleading visual inputs. It utilizes a hierarchical taxonomy and a novel metric to uncover significant vulnerabilities in state-of-the-art LVLMs.

🔹 Publication Date: Published on Nov 18

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.14159
• PDF: https://arxiv.org/pdf/2511.14159
• Github: https://github.com/chenyil6/MVI-Bench

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#LVLMs #ComputerVision #AIrobustness #MachineLearning #AI

✨REVISOR: Beyond Textual Reflection, Towards Multimodal Introspective Reasoning in Long-Form Video Understanding

📝 Summary:
Text-only self-reflection is insufficient for long-form video understanding. REVISOR is a new framework enabling MLLMs to perform multimodal introspective reflection across text and visual modalities. This significantly enhances reasoning for long videos without extra fine-tuning, achieving stron...

🔹 Publication Date: Published on Nov 17

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.13026
• PDF: https://arxiv.org/pdf/2511.13026

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#MultimodalAI #VideoUnderstanding #MLLMs #AIResearch #ComputerVision

✨Agent-R1: Training Powerful LLM Agents with End-to-End Reinforcement Learning

📝 Summary:
This paper clarifies RL for LLM Agents by extending the MDP framework. It introduces Agent-R1, a modular and flexible training framework, demonstrating its effectiveness on Multihop QA tasks.

🔹 Publication Date: Published on Nov 18

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.14460
• PDF: https://arxiv.org/pdf/2511.14460
• Github: https://github.com/0russwest0/Agent-R1

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#LLMAgents #ReinforcementLearning #AI #DeepLearning #NLP

✨Can World Simulators Reason? Gen-ViRe: A Generative Visual Reasoning Benchmark

📝 Summary:
Current video model benchmarks miss assessing Chain-of-Frames CoF reasoning, crucial for world simulators. Gen-ViRe is a new benchmark that decomposes CoF reasoning into cognitive subtasks, offering the first quantitative assessment. It reveals poor reasoning depth despite impressive visual quali...

🔹 Publication Date: Published on Nov 17

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.13853
• PDF: https://arxiv.org/pdf/2511.13853

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#AI #WorldSimulators #VisualReasoning #GenerativeAI #Benchmarks

✨Agent READMEs: An Empirical Study of Context Files for Agentic Coding

📝 Summary:
This study analyzed 2303 agent context files, finding them complex and evolving like config code. Developers prioritize functional details but rarely specify non-functional requirements like security or performance. This suggests a gap in guardrails for agent-written code quality.

🔹 Publication Date: Published on Nov 17

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.12884
• PDF: https://arxiv.org/pdf/2511.12884

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#AIAgents #SoftwareEngineering #CodeQuality #LLMs #AIResearch

✨UniMoE-Audio: Unified Speech and Music Generation with Dynamic-Capacity MoE

📝 Summary:
UniMoE-Audio unifies speech and music generation using a novel Dynamic-Capacity Mixture-of-Experts framework. It addresses data imbalance and task conflicts through a hybrid expert design and a three-stage training, achieving state-of-the-art performance and synergistic cross-domain learning.

🔹 Publication Date: Published on Oct 15

🔹 Paper Links:
• arXiv Page: https://arxivexplained.com/papers/unimoe-audio-unified-speech-and-music-generation-with-dynamic-capacity-moe
• PDF: https://arxiv.org/pdf/2510.13344
• Project Page: https://mukioxun.github.io/Uni-MoE-site/home.html
• Github: https://github.com/HITsz-TMG/Uni-MoE/blob/master/UniMoE-Audio

🔹 Models citing this paper:
• https://huggingface.co/HIT-TMG/UniMoE-Audio-Preview

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#SpeechGeneration #MusicGeneration #MixtureOfExperts #GenerativeAI #DeepLearning

✨OmniZip: Audio-Guided Dynamic Token Compression for Fast Omnimodal Large Language Models

📝 Summary:
OmniZip is a training-free framework that addresses the computational bottleneck in omnimodal LLMs by dynamically compressing audio-visual tokens. It uses audio retention scores to guide video token pruning, achieving 3.42X inference speedup and 1.4X memory reduction without performance loss.

🔹 Publication Date: Published on Nov 18

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.14582
• PDF: https://arxiv.org/pdf/2511.14582
• Github: https://github.com/KD-TAO/OmniZip

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#OmnimodalLLM #TokenCompression #LLMs #AI #ModelEfficiency

✨Think-at-Hard: Selective Latent Iterations to Improve Reasoning Language Models

📝 Summary:
Think-at-Hard TaH improves LLM reasoning by dynamically refining only hard tokens. It uses a neural decider to identify them and LoRA for focused refinement, boosting performance with minimal overhead.

🔹 Publication Date: Published on Nov 11

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.08577
• PDF: https://arxiv.org/pdf/2511.08577
• Github: https://github.com/thu-nics/TaH

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#LLM #AI #MachineLearning #NaturalLanguageProcessing #Reasoning

✨Uni-MoE: Scaling Unified Multimodal LLMs with Mixture of Experts

📝 Summary:
Uni-MoE introduces a sparse Multimodal Mixture of Experts LLM efficiently handling diverse data types. It uses modality-specific encoders and a progressive training strategy, reducing performance bias and improving collaboration across modalities.

🔹 Publication Date: Published on May 18, 2024

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2405.11273
• PDF: https://arxiv.org/pdf/2405.11273
• Github: https://github.com/hitsz-tmg/umoe-scaling-unified-multimodal-llms

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#MultimodalAI #LLMs #MixtureOfExperts #DeepLearning #AIResearch

✨AraLingBench A Human-Annotated Benchmark for Evaluating Arabic Linguistic Capabilities of Large Language Models

📝 Summary:
AraLingBench is a human-annotated benchmark evaluating Arabic LLM linguistic competence using expert-designed questions. It reveals models achieve surface proficiency but lack deep understanding, often relying on memorization rather than true comprehension.

🔹 Publication Date: Published on Nov 18

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.14295
• PDF: https://arxiv.org/pdf/2511.14295

✨ Datasets citing this paper:
• https://huggingface.co/datasets/hammh0a/AraLingBench

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#ArabicNLP #LLMEvaluation #AIResearch #LanguageModels #NLPBenchmarking

✨Mitigating Label Length Bias in Large Language Models

📝 Summary:
Large Language Models exhibit a label length bias with multi-token class labels. This paper introduces Normalized Contextual Calibration NCC to mitigate this issue by normalizing and calibrating predictions at the full-label level. NCC significantly improves performance and reliability across div...

🔹 Publication Date: Published on Nov 18

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.14385
• PDF: https://arxiv.org/pdf/2511.14385

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#LLM #AI #NLP #BiasInAI #MachineLearning

✨Φeat: Physically-Grounded Feature Representation

📝 Summary:
Φeat is a new self-supervised visual backbone that captures material identity like reflectance and mesostructure. It learns robust features invariant to external physical factors such as shape and lighting, promoting physics-aware perception.

🔹 Publication Date: Published on Nov 14

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.11270
• PDF: https://arxiv.org/pdf/2511.11270

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#ComputerVision #SelfSupervisedLearning #DeepLearning #FeatureLearning #PhysicsAwareAI

✨Large Language Models Meet Extreme Multi-label Classification: Scaling and Multi-modal Framework

📝 Summary:
This paper improves Extreme Multi-label Classification XMC by using larger decoder-only models and introduces ViXML, a vision-enhanced framework. ViXML efficiently integrates visual information, significantly outperforming text-only models and achieving new state-of-the-art.

🔹 Publication Date: Published on Nov 17

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.13189
• PDF: https://arxiv.org/pdf/2511.13189

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#LLM #XMC #MultiModalAI #MachineLearning #AIResearch

✨A Brain Wave Encodes a Thousand Tokens: Modeling Inter-Cortical Neural Interactions for Effective EEG-based Emotion Recognition

📝 Summary:
RBTransformer, a Transformer-based model, improves EEG-based emotion recognition by modeling inter-cortical neural dynamics. It uses Band Differential Entropy tokens and multi-head attention. This approach significantly outperforms existing state-of-the-art methods on multiple datasets and dimens...

🔹 Publication Date: Published on Nov 17

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.13954
• PDF: https://arxiv.org/pdf/2511.13954
• Github: https://github.com/nnilayy/RBTransformer

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#EEG #EmotionRecognition #Transformers #Neuroscience #MachineLearning

✨Proactive Hearing Assistants that Isolate Egocentric Conversations

📝 Summary:
A proactive hearing assistant system automatically identifies and isolates the wearers conversation partners from binaural audio. It uses a dual-model AI architecture that adapts to conversational dynamics in real-time, improving speech clarity without user prompts.

🔹 Publication Date: Published on Nov 14

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.11473
• PDF: https://arxiv.org/pdf/2511.11473
• Project Page: https://proactivehearing.cs.washington.edu/
• Github: https://github.com/guilinhu/proactive_hearing_assistant

🔹 Models citing this paper:
• https://huggingface.co/guilinhu/proactive_hearing

✨ Datasets citing this paper:
• https://huggingface.co/datasets/guilinhu/libri_conversation

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#HearingTech #AI #SpeechEnhancement #AssistiveTechnology #AudioProcessing

✨NORA-1.5: A Vision-Language-Action Model Trained using World Model- and Action-based Preference Rewards

📝 Summary:
NORA-1.5, an enhanced vision-language-action model with a flow-matching-based action expert and reward-driven post-training, improves performance and reliability in both simulated and real-world setti...

🔹 Publication Date: Published on Nov 18

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.14659
• PDF: https://arxiv.org/pdf/2511.14659
• Project Page: https://declare-lab.github.io/nora-1.5
• Github: https://github.com/declare-lab/nora-1.5

🔹 Models citing this paper:
• https://huggingface.co/declare-lab/nora-1.5

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨TopoPerception: A Shortcut-Free Evaluation of Global Visual Perception in Large Vision-Language Models

📝 Summary:
Large Vision-Language Models (LVLMs) typically align visual features from an encoder with a pre-trained Large Language Model (LLM). However, this makes the visual perception module a bottleneck, which...

🔹 Publication Date: Published on Nov 14

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.11831
• PDF: https://arxiv.org/pdf/2511.11831
• Github: https://github.com/Wenhao-Zhou/TopoPerception

✨ Datasets citing this paper:
• https://huggingface.co/datasets/Wenhao-Zhou/TopoPerception

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨LLM-Powered Fully Automated Chaos Engineering: Towards Enabling Anyone to Build Resilient Software Systems at Low Cost

📝 Summary:
Manual planning and improvement hinder Chaos Engineering adoption. ChaosEater automates the entire Chaos Engineering cycle for Kubernetes using LLMs, handling tasks from requirements to debugging. This enables anyone to build resilient systems quickly and affordably.

🔹 Publication Date: Published on Nov 11

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.07865
• PDF: https://arxiv.org/pdf/2511.07865
• Project Page: https://ntt-dkiku.github.io/chaos-eater/
• Github: https://github.com/ntt-dkiku/chaos-eater

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#ChaosEngineering #LLM #CloudNative #SoftwareResilience #DevOps