✨RF-DETR: Neural Architecture Search for Real-Time Detection Transformers

📝 Summary:
RF-DETR is a light-weight detection transformer leveraging weight-sharing NAS to optimize accuracy-latency tradeoffs across diverse datasets. It significantly outperforms prior state-of-the-art, being the first real-time detector to surpass 60 AP on COCO.

🔹 Publication Date: Published on Nov 12

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.09554
• PDF: https://arxiv.org/pdf/2511.09554

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#ObjectDetection #ComputerVision #MachineLearning #NeuralArchitectureSearch #Transformers

✨Experience-Guided Adaptation of Inference-Time Reasoning Strategies

📝 Summary:
Experience-Guided Reasoner EGuR dynamically generates and optimizes complete computational strategies at inference time using accumulated experience. It adapts LLM calls tools and control logic improving accuracy up to 14 percent and reducing costs by up to 111x.

🔹 Publication Date: Published on Nov 14

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.11519
• PDF: https://arxiv.org/pdf/2511.11519

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#LLM #AI #Reasoning #Optimization #MachineLearning

✨miniF2F-Lean Revisited: Reviewing Limitations and Charting a Path Forward

📝 Summary:
An analysis of miniF2F showed AI systems had 36% accuracy due to problem errors. Correcting these errors created miniF2F-v2, improving accuracy to 70%. High-quality benchmarks like miniF2F-v2 are crucial for evaluating formal reasoning progress.

🔹 Publication Date: Published on Nov 5

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.03108
• PDF: https://arxiv.org/pdf/2511.03108
• Github: https://github.com/roozbeh-yz/miniF2F_v2

✨ Datasets citing this paper:
• https://huggingface.co/datasets/roozbeh-yz/miniF2F_v2

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#AI #FormalReasoning #Benchmarks #MachineLearning #Dataset

✨GroupRank: A Groupwise Reranking Paradigm Driven by Reinforcement Learning

📝 Summary:
GroupRank introduces a novel groupwise reranking paradigm addressing limitations of pointwise and listwise methods. It processes queries with document groups to assign comparative relevance scores, combining flexibility with global context. Trained via reinforcement learning and synthesized data,...

🔹 Publication Date: Published on Nov 10

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.11653
• PDF: https://arxiv.org/pdf/2511.11653

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#Reranking #ReinforcementLearning #InformationRetrieval #MachineLearning #DataScience

✨WebCoach: Self-Evolving Web Agents with Cross-Session Memory Guidance

📝 Summary:
WebCoach introduces a self-evolving framework for web agents with persistent cross-session memory. It uses a WebCondenser, External Memory Store, and a Coach to learn from past experiences without retraining. This significantly improves task success and enables smaller models to match larger LLM ...

🔹 Publication Date: Published on Nov 17

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.12997
• PDF: https://arxiv.org/pdf/2511.12997

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#WebAgents #AI #MachineLearning #LLM #MemoryAI

✨P1: Mastering Physics Olympiads with Reinforcement Learning

📝 Summary:
P1 is a family of open-source physics reasoning models trained via reinforcement learning. P1-235B-A22B achieved Gold-medal performance at IPhO 2025 and won 12 other competitions. These models also show strong generalizability on other reasoning tasks.

🔹 Publication Date: Published on Nov 17

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.13612
• PDF: https://arxiv.org/pdf/2511.13612
• Project Page: https://prime-rl.github.io/P1/
• Github: https://github.com/PRIME-RL/P1

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#ReinforcementLearning #Physics #AI #MachineLearning #OpenSource

✨Souper-Model: How Simple Arithmetic Unlocks State-of-the-Art LLM Performance

📝 Summary:
SoCE is a novel model souping technique that boosts LLM performance. It uses non-uniform weighted averaging of expert models identified for specific benchmark categories, unlike uniform methods. This leads to state-of-the-art results and improved robustness.

🔹 Publication Date: Published on Nov 17

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.13254
• PDF: https://arxiv.org/pdf/2511.13254

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#LLMs #ModelSouping #MachineLearning #AI #StateOfTheArt

✨Instella: Fully Open Language Models with Stellar Performance

📝 Summary:
Instella is a family of fully open language models trained on open data. It achieves state-of-the-art among fully open models and competes with leading open-weight LLMs. Specialized variants for long context and math reasoning are also offered.

🔹 Publication Date: Published on Nov 13

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.10628
• PDF: https://arxiv.org/pdf/2511.10628
• Github: https://github.com/AMD-AGI/Instella

🔹 Models citing this paper:
• https://huggingface.co/amd/AMD-OLMo
• https://huggingface.co/amd/Instella-3B-Instruct
• https://huggingface.co/amd/Instella-3B

✨ Datasets citing this paper:
• https://huggingface.co/datasets/amd/Instella-Long
• https://huggingface.co/datasets/amd/Instella-GSM8K-synthetic

✨ Spaces citing this paper:
• https://huggingface.co/spaces/DexterSptizu/AMD-OLMo-1B
• https://huggingface.co/spaces/universeofml/DeepFocusTrain

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

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#LLMs #OpenSource #AI #MachineLearning #NLP

✨Genomic Next-Token Predictors are In-Context Learners

📝 Summary:
In-context learning ICL emerges organically in genomic sequences through large-scale predictive training, mirroring its behavior in language models. This first evidence suggests ICL is a general phenomenon of large-scale modeling, not exclusive to human language.

🔹 Publication Date: Published on Nov 16

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.12797
• PDF: https://arxiv.org/pdf/2511.12797

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#Genomics #InContextLearning #AI #MachineLearning #LLMs

✨OpenUS: A Fully Open-Source Foundation Model for Ultrasound Image Analysis via Self-Adaptive Masked Contrastive Learning

📝 Summary:
OpenUS is an open-source ultrasound foundation model built on a large public dataset. It uses a vision Mamba backbone and a novel self-adaptive masking framework to enhance pre-training, enabling label-efficient fine-tuning for various US tasks.

🔹 Publication Date: Published on Nov 14

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.11510
• PDF: https://arxiv.org/pdf/2511.11510
• Github: https://github.com/XZheng0427/OpenUS

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#OpenSource #FoundationModel #UltrasoundAI #MachineLearning #MedicalImaging

✨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

✨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

✨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

✨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

✨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

✨Reasoning via Video: The First Evaluation of Video Models' Reasoning Abilities through Maze-Solving Tasks

📝 Summary:
VR-Bench evaluates video models' spatial reasoning using maze-solving tasks. It demonstrates that video models excel in spatial perception and reasoning, outperforming VLMs, and benefit from diverse sampling during inference. These findings show the strong potential of reasoning via video for spa...

🔹 Publication Date: Published on Nov 19

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.15065
• PDF: https://arxiv.org/pdf/2511.15065
• Project Page: https://imyangc7.github.io/VRBench_Web/
• Github: https://github.com/ImYangC7/VR-Bench

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#VideoModels #AIReasoning #SpatialAI #ComputerVision #MachineLearning

✨VisPlay: Self-Evolving Vision-Language Models from Images

📝 Summary:
VisPlay is a self-evolving RL framework that improves Vision-Language Models using unlabeled images. It employs interacting Questioner and Reasoner roles, trained with GRPO, to enhance reasoning, generalization, and reduce hallucination. This scalable method achieves consistent improvements.

🔹 Publication Date: Published on Nov 19

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.15661
• PDF: https://arxiv.org/pdf/2511.15661

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#VisionLanguageModels #ReinforcementLearning #ArtificialIntelligence #MachineLearning #SelfEvolvingAI

✨ARC-Chapter: Structuring Hour-Long Videos into Navigable Chapters and Hierarchical Summaries

📝 Summary:
ARC-Chapter is a large-scale video chaptering model trained on millions of long video chapters, using a new bilingual and hierarchical dataset. It introduces a novel evaluation metric, GRACE, to better reflect real-world chaptering. The model achieves state-of-the-art performance and demonstrates...

🔹 Publication Date: Published on Nov 18

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.14349
• PDF: https://arxiv.org/pdf/2511.14349
• Project Page: https://arcchapter.github.io/index_en.html
• Github: https://github.com/TencentARC/ARC-Chapter

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#VideoChaptering #AI #MachineLearning #VideoSummarization #ComputerVision