✨DeepPlanning: Benchmarking Long-Horizon Agentic Planning with Verifiable Constraints

📝 Summary:
DeepPlanning is a new benchmark for long-horizon agent planning, addressing the lack of global optimization and fine-grained local constraints in current LLM assessments. It features complex real-world tasks where even frontier LLMs struggle, highlighting the need for explicit reasoning and paral...

🔹 Publication Date: Published on Jan 26

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.18137
• PDF: https://arxiv.org/pdf/2601.18137

✨ Datasets citing this paper:
• https://huggingface.co/datasets/Qwen/DeepPlanning

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

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#AIPlanning #LLMs #AgentAI #Benchmarking #DeepLearning

✨A Mechanistic View on Video Generation as World Models: State and Dynamics

📝 Summary:
Video generation models are categorized based on state construction and dynamics modeling approaches, with emphasis on transitioning evaluation metrics from visual quality to functional capabilities l...

🔹 Publication Date: Published on Jan 22

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.17067
• PDF: https://arxiv.org/pdf/2601.17067

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨TensorLens: End-to-End Transformer Analysis via High-Order Attention Tensors

📝 Summary:
TensorLens presents a novel mathematical framework that represents the complete transformer architecture as a single input-dependent linear operator using high-order tensors, enabling comprehensive an...

🔹 Publication Date: Published on Jan 25

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.17958
• PDF: https://arxiv.org/pdf/2601.17958

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨HalluGuard: Demystifying Data-Driven and Reasoning-Driven Hallucinations in LLMs

📝 Summary:
HalluGuard presents a theoretical framework that decomposes LLM hallucination risk into data-driven and reasoning-driven components. It introduces an NTK-based score to jointly detect both types of hallucinations, achieving state-of-the-art performance across various benchmarks and LLMs.

🔹 Publication Date: Published on Jan 26

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.18753
• PDF: https://arxiv.org/pdf/2601.18753

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

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#LLMs #AI #MachineLearning #Hallucination #NLP

✨MortalMATH: Evaluating the Conflict Between Reasoning Objectives and Emergency Contexts

📝 Summary:
Specialized AI reasoning models prioritize task completion over safety. Our MortalMATH benchmark shows these models ignore emergencies to complete math, unlike generalist models. This relentless focus on correctness may remove crucial safety instincts and cause dangerous delays.

🔹 Publication Date: Published on Jan 26

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.18790
• PDF: https://arxiv.org/pdf/2601.18790

✨ Datasets citing this paper:
• https://huggingface.co/datasets/sileod/MortalMATH

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

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#AISafety #AIethics #MachineLearning #AIReasoning #MortalMATH

✨Interp3D: Correspondence-aware Interpolation for Generative Textured 3D Morphing

📝 Summary:
Interp3D is a training-free framework for textured 3D morphing. It solves existing issues of structural misalignment and texture blurring by ensuring geometric consistency and texture alignment using generative priors and progressive alignment. The method outperforms prior approaches.

🔹 Publication Date: Published on Jan 20

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.14103
• PDF: https://arxiv.org/pdf/2601.14103
• Project Page: https://interp3d.github.io/
• Github: https://github.com/xiaolul2/Interp3D

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

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#3DMorphing #GenerativeAI #ComputerGraphics #DeepLearning #AIResearch

✨TSRBench: A Comprehensive Multi-task Multi-modal Time Series Reasoning Benchmark for Generalist Models

📝 Summary:
TSRBench introduces a multi-modal benchmark to evaluate generalist models on time series reasoning. It reveals scaling laws break down for prediction, strong reasoning doesnt guarantee accurate forecasting, and multimodal models fail to effectively fuse diverse inputs.

🔹 Publication Date: Published on Jan 26

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.18744
• PDF: https://arxiv.org/pdf/2601.18744

✨ Datasets citing this paper:
• https://huggingface.co/datasets/umd-zhou-lab/TSRBench

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

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#TimeSeries #MultimodalAI #GeneralistModels #MachineLearning #AIResearch

✨Masked Depth Modeling for Spatial Perception

📝 Summary:
LingBot-Depth is a depth completion model that refines inaccurate depth maps using masked depth modeling, visual context, and automated data curation. It significantly outperforms top-tier RGB-D cameras in depth precision and pixel coverage. This improves spatial perception for robotics and auton...

🔹 Publication Date: Published on Jan 25

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.17895
• PDF: https://arxiv.org/pdf/2601.17895
• Github: https://github.com/Robbyant/lingbot-depth

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨Agentic Search in the Wild: Intents and Trajectory Dynamics from 14M+ Real Search Requests

📝 Summary:
Analyzing 14M agentic search requests, this study found most multi-turn sessions are short and fast. Behavior differs by intent, with fact-seeking showing repetition and reasoning needing broader exploration. Agents effectively reuse previous evidence in subsequent queries.

🔹 Publication Date: Published on Jan 24

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.17617
• PDF: https://arxiv.org/pdf/2601.17617

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨Yunjue Agent Tech Report: A Fully Reproducible, Zero-Start In-Situ Self-Evolving Agent System for Open-Ended Tasks

📝 Summary:
Agents that evolve tools through continuous interaction and feedback can adapt to dynamic environments and transfer knowledge across domains more effectively than traditional systems. AI-generated sum...

🔹 Publication Date: Published on Jan 26

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.18226
• PDF: https://arxiv.org/pdf/2601.18226
• Project Page: https://www.yunjuetech.com/en
• Github: https://github.com/YunjueTech/Yunjue-Agent?tab=readme-ov-file

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨Fast KVzip: Efficient and Accurate LLM Inference with Gated KV Eviction

📝 Summary:
A novel gating-based key-value cache eviction method for frozen-weight large language models achieves high compression ratios with minimal computational overhead while maintaining near-lossless perfor...

🔹 Publication Date: Published on Jan 25

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.17668
• PDF: https://arxiv.org/pdf/2601.17668
• Github: https://janghyun1230.github.io/fastkvzip/

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨C-RADIOv4 (Tech Report)

📝 Summary:
Multi-teacher distillation enables unified student models that maintain and enhance multiple teacher capabilities, with C-RADIOv4 offering improved performance and efficiency through updated training ...

🔹 Publication Date: Published on Jan 24

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.17237
• PDF: https://arxiv.org/pdf/2601.17237

🔹 Models citing this paper:
• https://huggingface.co/nvidia/C-RADIOv4-SO400M
• https://huggingface.co/nvidia/C-RADIOv4-H

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨AVMeme Exam: A Multimodal Multilingual Multicultural Benchmark for LLMs' Contextual and Cultural Knowledge and Thinking

📝 Summary:
AVMeme Exam is introduced, a benchmark of over 1000 Internet sound and video memes with Q&A, to test MLLMs' cultural and contextual understanding. Current models struggle significantly with textless audio and deep contextual/cultural thinking, revealing a gap in multimodal AI.

🔹 Publication Date: Published on Jan 25

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.17645
• PDF: https://arxiv.org/pdf/2601.17645
• Github: https://avmemeexam.github.io/public

✨ Datasets citing this paper:
• https://huggingface.co/datasets/naplab/AVMeme-Exam

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨Visual Generation Unlocks Human-Like Reasoning through Multimodal World Models

📝 Summary:
Visual generation enhances reasoning capabilities in multimodal models by providing more natural world models for physical and spatial tasks, while verbal reasoning remains sufficient for abstract dom...

🔹 Publication Date: Published on Jan 27

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.19834
• PDF: https://arxiv.org/pdf/2601.19834
• Project Page: https://thuml.github.io/Reasoning-Visual-World/
• Github: https://github.com/thuml/reasoning-visual-world

✨ Datasets citing this paper:
• https://huggingface.co/datasets/thuml/VisWorld-Eval

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨AgentDoG: A Diagnostic Guardrail Framework for AI Agent Safety and Security

📝 Summary:
AI agents face safety and security challenges from autonomous tool use and environmental interactions, requiring advanced guardrail frameworks for risk diagnosis and transparent monitoring. AI-generat...

🔹 Publication Date: Published on Jan 26

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.18491
• PDF: https://arxiv.org/pdf/2601.18491
• Github: https://github.com/AI45Lab/AgentDoG

🔹 Models citing this paper:
• https://huggingface.co/AI45Research/AgentDoG-Qwen3-4B
• https://huggingface.co/AI45Research/AgentDoG-Qwen2.5-7B
• https://huggingface.co/AI45Research/AgentDoG-Llama3.1-8B

✨ Datasets citing this paper:
• https://huggingface.co/datasets/AI45Research/ATBench

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨Selective Steering: Norm-Preserving Control Through Discriminative Layer Selection

📝 Summary:
Selective Steering enables continuous, norm-preserving control of language model behavior through targeted layer selection and mathematically rigorous rotation techniques. AI-generated summary Despite...

🔹 Publication Date: Published on Jan 27

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.19375
• PDF: https://arxiv.org/pdf/2601.19375
• Project Page: https://knoveleng.github.io/steering/
• Github: https://github.com/knoveleng/steering

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

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✨Revisiting Parameter Server in LLM Post-Training

📝 Summary:
On-Demand Communication (ODC) adapts parameter server principles to Fully Sharded Data Parallel training by replacing collective communication with point-to-point communication, improving device utili...

🔹 Publication Date: Published on Jan 27

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.19362
• PDF: https://arxiv.org/pdf/2601.19362
• Github: https://github.com/sail-sg/odc

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨GPCR-Filter: a deep learning framework for efficient and precise GPCR modulator discovery

📝 Summary:
GPCR-Filter is a deep learning framework that combines protein language models and graph neural networks to identify GPCR modulators with high accuracy and generalization across unseen receptors and l...

🔹 Publication Date: Published on Jan 27

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.19149
• PDF: https://arxiv.org/pdf/2601.19149

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨AdaReasoner: Dynamic Tool Orchestration for Iterative Visual Reasoning

📝 Summary:
AdaReasoner teaches multimodal models general tool use for visual reasoning using scalable data, reinforcement learning for tool selection, and adaptive learning. It dynamically orchestrates tools, generalizes to new ones, and achieves state-of-the-art performance on complex visual tasks.

🔹 Publication Date: Published on Jan 26

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.18631
• PDF: https://arxiv.org/pdf/2601.18631
• Project Page: https://adareasoner.github.io/
• Github: https://adareasoner.github.io

🔹 Models citing this paper:
• https://huggingface.co/AdaReasoner/AdaReasoner-7B-Randomized
• https://huggingface.co/AdaReasoner/AdaReasoner-TC-7B-Non-Randomized
• https://huggingface.co/AdaReasoner/AdaReasoner-7B-Non-Randomized

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨DPM-Solver: A Fast ODE Solver for Diffusion Probabilistic Model Sampling in Around 10 Steps

📝 Summary:
A new solver, DPM-Solver, accelerates sampling from diffusion probabilistic models by analytically solving the diffusion ordinary differential equations, achieving high-quality results with fewer func...

🔹 Publication Date: Published on Jun 2, 2022

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2206.00927
• PDF: https://arxiv.org/pdf/2206.00927
• Project Page: https://huggingface.co/spaces/huggingface-projects/stable-diffusion-latent-upscaler
• Github: https://github.com/MaximeVandegar/Papers-in-100-Lines-of-Code/tree/main/DPM_Solver_A_Fast_ODE_Solver_for_Diffusion_Probabilistic_Model_Sampling_in_Around_10_Steps

🔹 Models citing this paper:
• https://huggingface.co/raisahil/scunge-model

✨ Spaces citing this paper:
• https://huggingface.co/spaces/huggingface-projects/stable-diffusion-latent-upscaler
• https://huggingface.co/spaces/Rooni/finetuned_diffusion
• https://huggingface.co/spaces/anzorq/finetuned_diffusion

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

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#AI #DataScience #MachineLearning #HuggingFace #Research