✨DrivingGen: A Comprehensive Benchmark for Generative Video World Models in Autonomous Driving

📝 Summary:
DrivingGen is the first comprehensive benchmark for generative driving world models, addressing prior evaluation gaps. It uses diverse datasets and new metrics to assess visual realism, trajectory plausibility, temporal coherence, and controllability. Benchmarking reveals trade-offs between visua...

🔹 Publication Date: Published on Jan 4

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.01528
• PDF: https://arxiv.org/pdf/2601.01528
• Project Page: https://drivinggen-bench.github.io/
• Github: https://github.com/youngzhou1999/DrivingGen

✨ Datasets citing this paper:
• https://huggingface.co/datasets/yangzhou99/DrivingGen

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#AutonomousDriving #GenerativeAI #WorldModels #AIResearch #Benchmarking

✨Gecko: An Efficient Neural Architecture Inherently Processing Sequences with Arbitrary Lengths

📝 Summary:
Gecko is a neural architecture for efficient processing of arbitrary length sequential data. It improves long range dependency capture with new components like timestep decay normalization and sliding chunk attention. Gecko outperforms Llama2 7B and Megalodon 7B, inherently handling sequences up ...

🔹 Publication Date: Published on Jan 10

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.06463
• PDF: https://arxiv.org/pdf/2601.06463
• Github: https://github.com/XuezheMax/gecko-llm

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#Gecko #NeuralNetworks #SequenceModeling #LLM #DeepLearning

✨Forest Before Trees: Latent Superposition for Efficient Visual Reasoning

📝 Summary:
Laser introduces Dynamic Windowed Alignment Learning DWAL for visual reasoning. This method maintains global feature superposition, achieving state-of-the-art performance with significantly reduced computational costs and high efficiency.

🔹 Publication Date: Published on Jan 11

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.06803
• PDF: https://arxiv.org/pdf/2601.06803

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#VisualReasoning #MachineLearning #AIResearch #ComputerVision #EfficientAI

✨FlyPose: Towards Robust Human Pose Estimation From Aerial Views

📝 Summary:
FlyPose is a lightweight, real-time aerial human pose estimation system. It achieves significantly improved accuracy through multi-dataset training and performs efficiently on UAVs. A new challenging dataset, FlyPose-104, is also released.

🔹 Publication Date: Published on Jan 9

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.05747
• PDF: https://arxiv.org/pdf/2601.05747
• Github: https://github.com/farooqhassaan/FlyPose

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#HumanPoseEstimation #UAV #ComputerVision #DeepLearning #AI

✨mHC-lite: You Don't Need 20 Sinkhorn-Knopp Iterations

📝 Summary:
mHC-lite proposes a novel reparameterization for Hyper-Connections, explicitly constructing exactly doubly stochastic matrices via convex combinations of permutations. This approach guarantees stability, improves training throughput with native operations, and outperforms prior methods.

🔹 Publication Date: Published on Jan 9

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.05732
• PDF: https://arxiv.org/pdf/2601.05732
• Github: https://github.com/FFTYYY/mhc-lite

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#DeepLearning #MachineLearning #Optimization #Algorithm #AI

✨Benchmarking Small Language Models and Small Reasoning Language Models on System Log Severity Classification

📝 Summary:
Severity classification benchmarks small language models for log understanding and deployability. RAG significantly boosts many models, even tiny ones, but efficiency and RAG integration vary widely, crucial for real-time systems.

🔹 Publication Date: Published on Jan 12

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.07790
• PDF: https://arxiv.org/pdf/2601.07790
• Github: https://github.com/stccenter/Benchmarking-SLMs-and-SRLMs-on-System-Log-Severity-Classification

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨RealMem: Benchmarking LLMs in Real-World Memory-Driven Interaction

📝 Summary:
RealMem benchmark evaluates memory systems for long-term project-oriented interactions in large language models, revealing challenges in managing dynamic context dependencies. AI-generated summary As ...

🔹 Publication Date: Published on Jan 11

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.06966
• PDF: https://arxiv.org/pdf/2601.06966

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨Sci-Reasoning: A Dataset Decoding AI Innovation Patterns

📝 Summary:
Sci-Reasoning is a new dataset that maps intellectual synthesis patterns in AI research. It traces key papers to their predecessors, identifying 15 distinct thinking patterns that drive breakthroughs. This dataset enables quantitative study of scientific progress and trains next-generation AI res...

🔹 Publication Date: Published on Jan 8

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.04577
• PDF: https://arxiv.org/pdf/2601.04577
• Github: https://github.com/AmberLJC/Sci-Reasoning

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨Does Inference Scaling Improve Reasoning Faithfulness? A Multi-Model Analysis of Self-Consistency Tradeoffs

📝 Summary:
Self-consistency improves reasoning accuracy for some models while potentially sacrificing faithfulness, with varying effects across different language models and problem difficulties. AI-generated su...

🔹 Publication Date: Published on Jan 10

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.06423
• PDF: https://arxiv.org/pdf/2601.06423

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨Can Textual Reasoning Improve the Performance of MLLMs on Fine-grained Visual Classification?

📝 Summary:
Multi-modal large language models struggle with fine-grained visual classification, and chain-of-thought reasoning harms performance due to increased reasoning length; a new framework called ReFine-RF...

🔹 Publication Date: Published on Jan 11

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.06993
• PDF: https://arxiv.org/pdf/2601.06993
• Github: https://github.com/jiezhu23/ReFine-RFT

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨Stochastic CHAOS: Why Deterministic Inference Kills, and Distributional Variability Is the Heartbeat of Artifical Cognition

📝 Summary:
Deterministic inference in LLMs is detrimental, suppressing uncertainty, emergent abilities, and safety awareness by enforcing single-output predictions. This approach misrepresents capabilities and risks. The paper advocates embracing distributional variability as essential for artificial cognit...

🔹 Publication Date: Published on Jan 12

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.07239
• PDF: https://arxiv.org/pdf/2601.07239

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨A Rising Tide Lifts All Boats: MTQE Rewards for Idioms Improve General Translation Quality

📝 Summary:
GRPO-style fine-tuning with MTQE models as rewards improves idiom translation by 14 points while enhancing general translation and cross-lingual capabilities. AI-generated summary Non-compositional ex...

🔹 Publication Date: Published on Jan 9

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.06307
• PDF: https://arxiv.org/pdf/2601.06307

🔹 Models citing this paper:
• https://huggingface.co/ishikaa/Chinese_llama8b-da
• https://huggingface.co/ishikaa/Chinese_llama8b-qe-cons
• https://huggingface.co/ishikaa/Chinese_llama8b-qe-pos

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨SPINAL -- Scaling-law and Preference Integration in Neural Alignment Layers

📝 Summary:
SPINAL diagnoses how DPO alignment reshapes representations layer by layer, revealing geometric localization of preference gradients in final decoder blocks and enabling practical auditing of alignmen...

🔹 Publication Date: Published on Jan 8

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.06238
• PDF: https://arxiv.org/pdf/2601.06238

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨Artificial Entanglement in the Fine-Tuning of Large Language Models

📝 Summary:
Using Artificial Entanglement, this paper finds that LLM fine-tuning like LoRA creates distinct internal parameter entanglement. Yet, external attention outputs are robust and similar to full fine-tuning. This no hair property explains LoRAs effectiveness.

🔹 Publication Date: Published on Jan 11

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.06788
• PDF: https://arxiv.org/pdf/2601.06788

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨How Do Large Language Models Learn Concepts During Continual Pre-Training?

📝 Summary:
Large language models develop concept circuits during continual pretraining that exhibit learning and forgetting patterns, with semantically similar concepts showing stronger interference and varying ...

🔹 Publication Date: Published on Jan 7

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.03570
• PDF: https://arxiv.org/pdf/2601.03570

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨On the Non-decoupling of Supervised Fine-tuning and Reinforcement Learning in Post-training

📝 Summary:
Supervised fine-tuning SFT and reinforcement learning RL in large language model post-training cannot be decoupled. Separating them causes performance degradation because RL increases SFT loss, and SFT lowers RL reward.

🔹 Publication Date: Published on Jan 12

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.07389
• PDF: https://arxiv.org/pdf/2601.07389

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨Ministral 3

📝 Summary:
Ministral 3 is a series of parameter-efficient dense language models available in three sizes 3B, 8B, 14B with three variants each. Designed for compute-constrained applications, they are trained via Cascade Distillation and include image understanding capabilities.

🔹 Publication Date: Published on Jan 13

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.08584
• PDF: https://arxiv.org/pdf/2601.08584

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨End-to-End Video Character Replacement without Structural Guidance

📝 Summary:
MoCha enables controllable video character replacement using a single frame mask through condition-aware RoPE and a comprehensive data construction pipeline with specialized datasets. AI-generated sum...

🔹 Publication Date: Published on Jan 13

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.08587
• PDF: https://arxiv.org/pdf/2601.08587

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨JudgeRLVR: Judge First, Generate Second for Efficient Reasoning

📝 Summary:
Reinforcement learning with verifiable rewards is enhanced through a judge-then-generate paradigm that improves both efficiency and accuracy in mathematical problem-solving. AI-generated summary Reinf...

🔹 Publication Date: Published on Jan 13

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.08468
• PDF: https://arxiv.org/pdf/2601.08468

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨The Confidence Dichotomy: Analyzing and Mitigating Miscalibration in Tool-Use Agents

📝 Summary:
Tool-integrated language model agents exhibit different calibration behaviors based on tool type, with a reinforcement learning framework improving both task accuracy and reliable uncertainty estimati...

🔹 Publication Date: Published on Jan 12

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.07264
• PDF: https://arxiv.org/pdf/2601.07264

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨ArenaRL: Scaling RL for Open-Ended Agents via Tournament-based Relative Ranking

📝 Summary:
Reinforcement learning for large language model agents suffers from discrimination collapse in open-ended tasks due to pointwise scalar scoring, which ArenaRL addresses through relative ranking and pa...

🔹 Publication Date: Published on Jan 10

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.06487
• PDF: https://arxiv.org/pdf/2601.06487
• Github: https://github.com/Alibaba-NLP/qqr

✨ Datasets citing this paper:
• https://huggingface.co/datasets/Alibaba-NLP/Open-Travel
• https://huggingface.co/datasets/Alibaba-NLP/Open-DeepResearch

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

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#AI #DataScience #MachineLearning #HuggingFace #Research