✨Teaching Models to Teach Themselves: Reasoning at the Edge of Learnability

📝 Summary:
A self-improvement framework enables pretrained language models to generate automated curricula for solving previously unsolvable problems by leveraging latent knowledge and meta-reinforcement learnin...

🔹 Publication Date: Published on Jan 26

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.18778
• PDF: https://arxiv.org/pdf/2601.18778
• Github: https://ssundaram21.github.io/soar/

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨Diffusion In Diffusion: Reclaiming Global Coherence in Semi-Autoregressive Diffusion

📝 Summary:
A 'draft-then-refine' framework for discrete diffusion language models that recovers global contextual understanding while maintaining semi-autoregressive efficiency through block diffusion and global...

🔹 Publication Date: Published on Jan 20

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.13599
• PDF: https://arxiv.org/pdf/2601.13599
• Project Page: https://noah-dllm.github.io/
• Github: https://noah-dllm.github.io/

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨The Side Effects of Being Smart: Safety Risks in MLLMs' Multi-Image Reasoning

📝 Summary:
A new benchmark, MIR-SafetyBench, reveals advanced MLLMs are more vulnerable to safety risks in multi-image reasoning. These models often give superficial safe responses, with unsafe generations showing lower attention entropy, indicating an over-focus on task completion.

🔹 Publication Date: Published on Jan 20

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.14127
• PDF: https://arxiv.org/pdf/2601.14127
• Github: https://github.com/thu-coai/MIR-SafetyBench

✨ Datasets citing this paper:
• https://huggingface.co/datasets/thu-coai/MIR-SafetyBench

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#AIsafety #MLLMs #MultiModalAI #ResponsibleAI #AIresearch

✨End-to-End Joint ASR and Speaker Role Diarization with Child-Adult Interactions

📝 Summary:
This paper presents a unified end-to-end framework extending Whisper for joint ASR and child-adult speaker role diarization. It significantly improves trannoscription accuracy and scalability by preventing error propagation, achieving lower word error rates and competitive diarization performance.

🔹 Publication Date: Published on Jan 25

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.17640
• PDF: https://arxiv.org/pdf/2601.17640
• Github: https://github.com/usc-sail/joint-asr-diarization-child-adult

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#ASR #SpeakerDiarization #SpeechProcessing #DeepLearning #ChildAdultInteraction

✨PingPong: A Natural Benchmark for Multi-Turn Code-Switching Dialogues

📝 Summary:
PingPong is a new human-authored benchmark for natural, multi-party code-switching dialogues, including trilingual conversations. It offers greater structural diversity than machine-generated data. Evaluations show current language models struggle with code-switched inputs, emphasizing the need f...

🔹 Publication Date: Published on Jan 24

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.17277
• PDF: https://arxiv.org/pdf/2601.17277

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#CodeSwitching #NLP #DialogueSystems #MultilingualAI #LLMs

✨Less Is More -- Until It Breaks: Security Pitfalls of Vision Token Compression in Large Vision-Language Models

📝 Summary:
Visual token compression degrades LVLM robustness via unstable token importance ranking. This causes critical information loss, creating vulnerabilities only under compression. An attack exploits this, revealing an efficiency-security trade-off.

🔹 Publication Date: Published on Jan 17

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.12042
• PDF: https://arxiv.org/pdf/2601.12042

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#LVLM #AIsecurity #VisionAI #ModelRobustness #DeepLearning

✨Plug-and-Play Benchmarking of Reinforcement Learning Algorithms for Large-Scale Flow Control

📝 Summary:
FluidGym presents a standalone, fully differentiable reinforcement learning benchmark for active flow control that operates without external CFD solvers and supports standardized evaluation protocols....

🔹 Publication Date: Published on Jan 21

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.15015v1
• PDF: https://arxiv.org/pdf/2601.15015
• Github: https://github.com/safe-autonomous-systems/fluidgym

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨STAR: Semantic Table Representation with Header-Aware Clustering and Adaptive Weighted Fusion

📝 Summary:
STAR improves table representation for table retrieval tasks. It uses header-aware clustering to create diverse partial tables and generate cluster-specific queries. STAR then employs weighted fusion for fine-grained alignment, outperforming previous methods on benchmarks.

🔹 Publication Date: Published on Jan 22

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.15860
• PDF: https://arxiv.org/pdf/2601.15860
• Github: https://github.com/adsl135789/STAR

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#TableRepresentation #InformationRetrieval #Clustering #DataScience #MachineLearning

✨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