✨Deep Search with Hierarchical Meta-Cognitive Monitoring Inspired by Cognitive Neuroscience

📝 Summary:
Deep search agents with hierarchical metacognitive monitoring enhance reasoning and retrieval performance through fast consistency checks and experience-driven corrective interventions. AI-generated s...

🔹 Publication Date: Published on Jan 30

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.23188
• PDF: https://arxiv.org/pdf/2601.23188

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨Robust Tool Use via Fission-GRPO: Learning to Recover from Execution Errors

📝 Summary:
A framework called Fission-GRPO is introduced to improve multi-turn tool execution in large language models by converting execution errors into corrective supervision during reinforcement learning tra...

🔹 Publication Date: Published on Jan 22

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.15625
• PDF: https://arxiv.org/pdf/2601.15625

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨FourierSampler: Unlocking Non-Autoregressive Potential in Diffusion Language Models via Frequency-Guided Generation

📝 Summary:
Diffusion language models face positional bias. FourierSampler uses frequency analysis to guide generation by separating global structure from local details. This sliding window approach significantly outperforms previous methods and autoregressive models.

🔹 Publication Date: Published on Jan 30

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.23182
• PDF: https://arxiv.org/pdf/2601.23182
• Github: https://github.com/ShirleYoung/FourierSampler

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨Statistical Estimation of Adversarial Risk in Large Language Models under Best-of-N Sampling

📝 Summary:
A scaling-aware risk estimation method called SABER is introduced for predicting large-scale adversarial vulnerability in language models through Best-of-N sampling, enabling accurate assessment with ...

🔹 Publication Date: Published on Jan 30

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.22636
• PDF: https://arxiv.org/pdf/2601.22636

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨PaddleOCR-VL-1.5: Towards a Multi-Task 0.9B VLM for Robust In-the-Wild Document Parsing

📝 Summary:
A compact vision-language model achieves state-of-the-art accuracy on document understanding tasks while maintaining efficiency through specialized benchmarking and extended functionality. AI-generate...

🔹 Publication Date: Published on Jan 29

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.21957
• PDF: https://arxiv.org/pdf/2601.21957

🔹 Models citing this paper:
• https://huggingface.co/PaddlePaddle/PaddleOCR-VL-1.5
• https://huggingface.co/PaddlePaddle/PP-DocLayoutV3

✨ Spaces citing this paper:
• https://huggingface.co/spaces/PaddlePaddle/PaddleOCR-VL-1.5_Online_Demo
• https://huggingface.co/spaces/AAAASSSASDASD3000/PaddleOCR-VL-1.5_Online_Demo

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨Revisiting Diffusion Model Predictions Through Dimensionality

📝 Summary:
Diffusion models using direct data prediction outperform traditional noise or velocity prediction in high-dimensional settings, with a proposed framework automatically learning optimal prediction para...

🔹 Publication Date: Published on Jan 29

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.21419
• PDF: https://arxiv.org/pdf/2601.21419

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨Machine Learning for Energy-Performance-aware Scheduling

📝 Summary:
We propose a Bayesian Optimization framework using Gaussian Processes to automate scheduling configuration on multi-core systems. It approximates the energy-time Pareto Frontier and reveals dominant hardware parameters through sensitivity analysis.

🔹 Publication Date: Published on Jan 30

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.23134
• PDF: https://arxiv.org/pdf/2601.23134

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#MachineLearning #Optimization #EnergyEfficiency #ComputerArchitecture #DataScience

✨Continual GUI Agents

📝 Summary:
The Continual GUI Agents framework addresses performance degradation in dynamic UI environments. It introduces GUI-Anchoring in Flux GUI-AiF, a reinforcement fine-tuning method with novel anchoring rewards that stabilize learning across shifting UI domains and resolutions, outperforming existing ...

🔹 Publication Date: Published on Jan 28

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.20732
• PDF: https://arxiv.org/pdf/2601.20732

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

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#ContinualLearning #ReinforcementLearning #AIAgents #HumanComputerInteraction #MachineLearning

✨RM -RF: Reward Model for Run-Free Unit Test Evaluation

📝 Summary:
RM-RF is a lightweight reward model predicting unit test outcomes directly from source code, skipping compile and run. It forecasts test suite success, coverage, and mutation kill rate, offering faster, cheaper evaluation for AI generated tests. This enables scalable feedback for test generation.

🔹 Publication Date: Published on Jan 19

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.13097
• PDF: https://arxiv.org/pdf/2601.13097
• Github: https://github.com/trndcenter/RM-RF-unit-tests

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#RewardModels #UnitTesting #AIGeneratedTests #SoftwareEngineering #MachineLearning

✨TAM-Eval: Evaluating LLMs for Automated Unit Test Maintenance

📝 Summary:
TAM-Eval is a new framework and benchmark for evaluating LLMs on comprehensive test suite maintenance tasks like creation, repair, and updating across Python, Java, and Go. It operates at the test file level with full repository context. Empirical results show current LLMs have limited capabiliti...

🔹 Publication Date: Published on Jan 26

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.18241
• PDF: https://arxiv.org/pdf/2601.18241
• Github: https://github.com/trndcenter/TAM-Eval

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#LLM #SoftwareEngineering #TestAutomation #AI4Code #TAMEval

✨Scaling Multiagent Systems with Process Rewards

📝 Summary:
The paper proposes MAPPA, a method that uses per-action AI feedback for process rewards to improve multiagent systems. This enhances credit assignment and sample efficiency, significantly boosting performance on math and data analysis tasks.

🔹 Publication Date: Published on Jan 30

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.23228
• PDF: https://arxiv.org/pdf/2601.23228
• Project Page: https://ltjed.github.io/MAPPA/
• Github: https://github.com/ltjed/multiagent-coaching

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

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#MultiagentSystems #AI #ReinforcementLearning #MachineLearning #DataScience

✨Why Attention Patterns Exist: A Unifying Temporal Perspective Analysis

📝 Summary:
TAPPA unifies LLM attention patterns by temporal analysis, classifying them as predictable or unpredictable based on query self-similarity. This framework deepens understanding and guides acceleration, improving KV cache and LLM pruning.

🔹 Publication Date: Published on Jan 29

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.21709
• PDF: https://arxiv.org/pdf/2601.21709

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#LLM #AttentionMechanism #AIResearch #NaturalLanguageProcessing #MachineLearning

✨Golden Goose: A Simple Trick to Synthesize Unlimited RLVR Tasks from Unverifiable Internet Text

📝 Summary:
Golden Goose synthesizes unlimited RLVR tasks from unverifiable internet text by creating multiple-choice questions from fill-in-the-middle tasks. This method enables large-scale training, yielding state-of-the-art results across various domains, including cybersecurity, by leveraging previously ...

🔹 Publication Date: Published on Jan 30

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.22975
• PDF: https://arxiv.org/pdf/2601.22975

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

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#RLVR #DataSynthesis #MachineLearning #NLP #Cybersecurity

✨Quartet II: Accurate LLM Pre-Training in NVFP4 by Improved Unbiased Gradient Estimation

📝 Summary:
Quartet II improves LLM pre-training in NVFP4 by introducing MS-EDEN for enhanced unbiased gradient estimation, significantly reducing quantization error. This achieves better accuracy and up to 4.2x faster execution on NVIDIA Blackwell GPUs compared to BF16.

🔹 Publication Date: Published on Jan 30

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.22813
• PDF: https://arxiv.org/pdf/2601.22813
• Github: https://github.com/IST-DASLab/Quartet-II

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

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#LLM #DeepLearning #Quantization #GPUAcceleration #AIResearch

✨ExpAlign: Expectation-Guided Vision-Language Alignment for Open-Vocabulary Grounding

📝 Summary:
ExpAlign proposes an expectation-guided vision-language alignment framework using multiple instance learning and attention pooling. It implicitly selects tokens and instances without extra annotations, significantly boosting open-vocabulary detection and zero-shot instance segmentation.

🔹 Publication Date: Published on Jan 30

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.22666
• PDF: https://arxiv.org/pdf/2601.22666

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

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#ComputerVision #DeepLearning #AI #VisionLanguage #OpenVocabulary

✨KAPSO: A Knowledge-grounded framework for Autonomous Program Synthesis and Optimization

📝 Summary:
KAPSO is a modular framework for autonomous program synthesis. It uses iterative optimization loops, a git-native experimentation engine, a comprehensive knowledge system, and cognitive memory to improve code over extended tasks, overcoming common coding agent failures.

🔹 Publication Date: Published on Jan 29

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.21526
• PDF: https://arxiv.org/pdf/2601.21526
• Github: https://github.com/Leeroo-AI/kapso

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

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#ProgramSynthesis #AI #CodeOptimization #KnowledgeAI #AIforCoding

✨Do Reasoning Models Enhance Embedding Models?

📝 Summary:
Embedding models from RLVR-tuned reasoning backbones show no performance advantage. HRSA explains this: RLVR reorganizes local geometry but preserves global geometry and linear readout, allowing manifold realignment during contrastive training.

🔹 Publication Date: Published on Jan 29

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.21192
• PDF: https://arxiv.org/pdf/2601.21192
• Github: https://github.com/HKUST-KnowComp/Reasoning-Embedding

🔹 Models citing this paper:
• https://huggingface.co/lucaswychan/Qwen2.5-1.5B-Reasoning-Embedding
• https://huggingface.co/lucaswychan/Qwen-2.5-1.5B-SimpleRL-Zoo-Reasoning-Embedding
• https://huggingface.co/lucaswychan/Qwen2.5-0.5B-Reasoning-Embedding

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨Causal World Modeling for Robot Control

📝 Summary:
Video world modeling enables robot learning through a unified framework that predicts frames and executes policies simultaneously using a shared latent space and closed-loop feedback mechanisms. AI-ge...

🔹 Publication Date: Published on Jan 29

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.21998
• PDF: https://arxiv.org/pdf/2601.21998

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨Visual Personalization Turing Test

📝 Summary:
A new evaluation framework called VPTT assesses contextual visual personalization through perceptual indistinguishability from human-created content, utilizing a benchmark, retrieval-augmented generat...

🔹 Publication Date: Published on Jan 30

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.22680
• PDF: https://arxiv.org/pdf/2601.22680

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨SONIC-O1: A Real-World Benchmark for Evaluating Multimodal Large Language Models on Audio-Video Understanding

📝 Summary:
A comprehensive benchmark for evaluating multimodal large language models on sequential audio-video data across real-world conversational domains with human-verified annotations and demographic metada...

🔹 Publication Date: Published on Jan 29

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.21666
• PDF: https://arxiv.org/pdf/2601.21666

✨ Datasets citing this paper:
• https://huggingface.co/datasets/vector-institute/sonic-o1

✨ Spaces citing this paper:
• https://huggingface.co/spaces/vector-institute/sonic-o1-leaderboard

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨Value-Based Pre-Training with Downstream Feedback

📝 Summary:
V-Pretraining reshapes foundation model pretraining objectives by using downstream task gradients. This method improves model capabilities and efficiency for tasks like language reasoning and vision segmentation, using minimal downstream feedback without direct label updates.

🔹 Publication Date: Published on Jan 29

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.22108
• PDF: https://arxiv.org/pdf/2601.22108

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

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#AI #DataScience #MachineLearning #HuggingFace #Research