✨OmegaUse: Building a General-Purpose GUI Agent for Autonomous Task Execution

📝 Summary:
OmegaUse is a general-purpose GUI agent model that achieves state-of-the-art performance on mobile and desktop platforms through a combination of high-quality data construction, decoupled training met...

🔹 Publication Date: Published on Jan 28

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.20380
• PDF: https://arxiv.org/pdf/2601.20380

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨SE-DiCoW: Self-Enrolled Diarization-Conditioned Whisper

📝 Summary:
SE-DiCoW improves speaker-attributed ASR by using diarization output to identify an enrollment segment for each speaker. This segment provides fixed conditioning in cross-attention layers, resolving ambiguities and significantly reducing transcription error rates compared to DiCoW.

🔹 Publication Date: Published on Jan 27

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.19194
• PDF: https://arxiv.org/pdf/2601.19194

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨UPLiFT: Efficient Pixel-Dense Feature Upsampling with Local Attenders

📝 Summary:
UPLiFT is an efficient iterative upsampling architecture with a Local Attender operator that creates dense features from visual backbones. It achieves state-of-the-art performance with lower inference costs than cross-attention methods, overcoming prior limitations.

🔹 Publication Date: Published on Jan 25

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.17950
• PDF: https://arxiv.org/pdf/2601.17950
• Project Page: https://www.cs.umd.edu/~mwalmer/uplift/
• Github: https://github.com/mwalmer-umd/UPLiFT/

🔹 Models citing this paper:
• https://huggingface.co/UPLiFT-upsampler/uplift_dinov2-s14
• https://huggingface.co/UPLiFT-upsampler/uplift_dinov3-splus16
• https://huggingface.co/UPLiFT-upsampler/uplift_sd1.5vae

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#ComputerVision #DeepLearning #FeatureUpsampling #AttentionMechanisms #EfficientAI

✨Shallow-π: Knowledge Distillation for Flow-based VLAs

📝 Summary:
Shallow-pi is a knowledge distillation framework that reduces transformer depth in vision-language-action models. It achieves over two times faster inference with less than one percent performance drop, enabling efficient real-world robotic deployment.

🔹 Publication Date: Published on Jan 28

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.20262
• PDF: https://arxiv.org/pdf/2601.20262
• Project Page: https://icsl-jeon.github.io/shallow-pi/
• Github: https://icsl-jeon.github.io/shallow-pi/

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#KnowledgeDistillation #Robotics #VLAModels #EfficientAI #DeepLearning

✨Reinforcement Learning via Self-Distillation

📝 Summary:
Self-Distillation Policy Optimization SDPO leverages rich textual feedback to address the credit-assignment bottleneck in reinforcement learning. SDPO treats the model as a self-teacher, distilling feedback-informed predictions to improve sample efficiency and accuracy. It significantly enhances ...

🔹 Publication Date: Published on Jan 28

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.20802
• PDF: https://arxiv.org/pdf/2601.20802

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#ReinforcementLearning #SelfDistillation #MachineLearning #AI #PolicyOptimization

✨Training Reasoning Models on Saturated Problems via Failure-Prefix Conditioning

📝 Summary:
Reinforcement learning training stalls on saturated problems as informative failures are hard to find. Failure-prefix conditioning addresses this by training on prefixes from rare incorrect reasoning paths, exposing models to failures. This boosts performance, maintains efficiency, and improves r...

🔹 Publication Date: Published on Jan 28

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.20829
• PDF: https://arxiv.org/pdf/2601.20829
• Github: https://github.com/minwukim/training-on-saturated-problems

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#ReinforcementLearning #MachineLearning #ArtificialIntelligence #DeepLearning #AIResearch

✨MM-Agent: LLM as Agents for Real-world Mathematical Modeling Problem

📝 Summary:
MM-Agent is an expert-inspired framework that enables LLMs to excel in real-world mathematical modeling by decomposing the task into four stages. It significantly outperforms human experts and baseline agents on a new benchmark, proving its practical effectiveness as a modeling copilot.

🔹 Publication Date: Published on May 20, 2025

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2505.14148
• PDF: https://arxiv.org/pdf/2505.14148
• Github: https://github.com/usail-hkust/llm-mm-agent

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#LLM #MathematicalModeling #AIAgents #ArtificialIntelligence #DataScience

✨VERGE: Formal Refinement and Guidance Engine for Verifiable LLM Reasoning

📝 Summary:
VERGE is a neurosymbolic framework that combines LLMs with SMT solvers for verification-guided iterative refinement of reasoning. It enhances logical correctness through formal semantic checking, semantic routing, and precise error localization, achieving an 18.7% performance uplift on reasoning ...

🔹 Publication Date: Published on Jan 27

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.20055
• PDF: https://arxiv.org/pdf/2601.20055

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#LLM #NeurosymbolicAI #FormalVerification #AIReasoning #SMTSolvers

✨Group Distributionally Robust Optimization-Driven Reinforcement Learning for LLM Reasoning

📝 Summary:
This paper introduces Multi-Adversary GDRO to improve LLM reasoning. It dynamically adapts training distributions by classifying prompt difficulty and reallocating resources. This boosts accuracy by over 10% compared to GRPO, focusing compute on hard problems.

🔹 Publication Date: Published on Jan 27

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.19280
• PDF: https://arxiv.org/pdf/2601.19280

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#LLMReasoning #ReinforcementLearning #Optimization #MachineLearning #AI

✨Persona Prompting as a Lens on LLM Social Reasoning

📝 Summary:
Persona prompting improves LLM classification on subjective tasks like hate speech but degrades explanation quality. It fails to mitigate demographic biases and align with real-world personas, as models remain resistant to significant steering and over-flag content as harmful. This reveals a crit...

🔹 Publication Date: Published on Jan 28

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.20757
• PDF: https://arxiv.org/pdf/2601.20757
• Github: https://github.com/jingyng/PP-social-reasoning

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#LLM #PersonaPrompting #BiasInAI #AIethics #NLP

✨How AI Impacts Skill Formation

📝 Summary:
AI assistance impairs skill acquisition for novice workers, hindering conceptual understanding and debugging. Heavy AI reliance is not a shortcut to competence. Careful AI adoption is crucial to preserve skill formation.

🔹 Publication Date: Published on Jan 28

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.20245
• PDF: https://arxiv.org/pdf/2601.20245
• Project Page: https://www.anthropic.com/research/AI-assistance-coding-skills

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#AI #SkillFormation #WorkforceDevelopment #LearningScience #HumanAICollaboration

✨FP8-RL: A Practical and Stable Low-Precision Stack for LLM Reinforcement Learning

📝 Summary:
FP8-RL presents a practical FP8 rollout stack for LLM reinforcement learning, addressing computational and memory bottlenecks. It employs blockwise FP8, KV-cache recalibration, and importance sampling to mitigate train-inference mismatch. This achieves up to 44% throughput gains while preserving ...

🔹 Publication Date: Published on Jan 26

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.18150
• PDF: https://arxiv.org/pdf/2601.18150

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

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#LLM #ReinforcementLearning #FP8 #MachineLearning #AIResearch

✨Language-based Trial and Error Falls Behind in the Era of Experience

📝 Summary:
LLMs struggle in nonlinguistic tasks due to costly exploration. SCOUT uses lightweight scouts for efficient exploration, then fine-tunes LLMs via SFT and RL. This boosts performance and saves GPU hours, outperforming proprietary models.

🔹 Publication Date: Published on Jan 29

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.21754
• PDF: https://arxiv.org/pdf/2601.21754
• Project Page: https://scout-cs.github.io/
• Github: https://github.com/Harry-mic/SCOUT

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨Llama-3.1-FoundationAI-SecurityLLM-Reasoning-8B Technical Report

📝 Summary:
A two-stage trained cybersecurity reasoning model achieves competitive performance on specialized tasks while maintaining general capabilities through supervised fine-tuning and reinforcement learning...

🔹 Publication Date: Published on Jan 28

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.21051
• PDF: https://arxiv.org/pdf/2601.21051
• Project Page: https://huggingface.co/fdtn-ai/Foundation-Sec-8B-Reasoning

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨VTC-R1: Vision-Text Compression for Efficient Long-Context Reasoning

📝 Summary:
VTC-R1 enables efficient long-context reasoning by compressing textual traces into compact images and iteratively feeding them back into vision-language models as optical memory, achieving significant...

🔹 Publication Date: Published on Jan 29

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.22069
• PDF: https://arxiv.org/pdf/2601.22069
• Github: https://github.com/w-yibo/VTC-R1

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨Typhoon-S: Minimal Open Post-Training for Sovereign Large Language Models

📝 Summary:
A minimal post-training approach using supervised fine-tuning, on-policy distillation, and small-scale reinforcement fine-tuning enables the development of high-quality sovereign language models with ...

🔹 Publication Date: Published on Jan 26

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.18129
• PDF: https://arxiv.org/pdf/2601.18129
• Project Page: https://opentyphoon.ai/model/typhoon-s
• Github: https://github.com/scb-10x/typhoon-s

🔹 Models citing this paper:
• https://huggingface.co/typhoon-ai/typhoon-s-thaillm-8b-instruct-research-preview
• https://huggingface.co/typhoon-ai/typhoon-s-4b-nitibench-ccl-legal-agent-research-preview

✨ Datasets citing this paper:
• https://huggingface.co/datasets/typhoon-ai/typhoon-s-instruct-post-training
• https://huggingface.co/datasets/typhoon-ai/typhoon-s-sovereign-capability-dataset

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨Exploring Reasoning Reward Model for Agents

📝 Summary:
Agent-RRM, a multi-faceted reward model, provides structured feedback for agentic trajectories through reasoning traces, critiques, and performance scores, with unified feedback integration showing su...

🔹 Publication Date: Published on Jan 29

🔹 Paper Links:
• arXiv Page: https://arxiv.org/pdf/2601.22154
• PDF: https://arxiv.org/pdf/2601.22154
• Github: https://github.com/kxfan2002/Reagent

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨Beyond Imitation: Reinforcement Learning for Active Latent Planning

📝 Summary:
Active latent planning method improves reasoning accuracy and efficiency by modeling latent token supervision as conditional VAE and using reinforcement learning with coherence rewards. AI-generated s...

🔹 Publication Date: Published on Jan 29

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.21598
• PDF: https://arxiv.org/pdf/2601.21598

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨Generation Enhances Understanding in Unified Multimodal Models via Multi-Representation Generation

📝 Summary:
UniMRG enhances unified multimodal models by training them to generate multiple visual representations, improving both understanding and generation capabilities through complementary information captu...

🔹 Publication Date: Published on Jan 29

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.21406
• PDF: https://arxiv.org/pdf/2601.21406
• Github: https://github.com/Sugewud/UniMRG

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨WebArbiter: A Principle-Guided Reasoning Process Reward Model for Web Agents

📝 Summary:
WebArbiter introduces a reasoning-first WebPRM that formulates reward modeling as text generation to improve web navigation through structured justifications and preference verdicts, outperforming exi...

🔹 Publication Date: Published on Jan 29

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.21872
• PDF: https://arxiv.org/pdf/2601.21872

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

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#AI #DataScience #MachineLearning #HuggingFace #Research