✨Pushing the Boundaries of Natural Reasoning: Interleaved Bonus from Formal-Logic Verification

📝 Summary:
This paper presents a framework that interleaves formal logic verification with natural language generation to improve LLM reasoning. It actively detects and corrects errors during the reasoning process. This method significantly outperforms state-of-the-art models on various reasoning benchmarks.

🔹 Publication Date: Published on Jan 30

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.22642
• PDF: https://arxiv.org/pdf/2601.22642

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨PaperBanana: Automating Academic Illustration for AI Scientists

📝 Summary:
_paperbanana is an agentic framework that automates the creation of publication-ready academic illustrations using advanced vision-language models and image generation techniques. AI-generated summary...

🔹 Publication Date: Published on Jan 30

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.23265
• PDF: https://arxiv.org/pdf/2601.23265
• Project Page: https://dwzhu-pku.github.io/PaperBanana/

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨ReGuLaR: Variational Latent Reasoning Guided by Rendered Chain-of-Thought

📝 Summary:
ReGuLaR introduces a variational auto-encoding framework that compresses reasoning processes into latent space while maintaining performance through image-rendered explicit reasoning chains for guidan...

🔹 Publication Date: Published on Jan 30

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.23184
• PDF: https://arxiv.org/pdf/2601.23184
• Github: https://github.com/FanmengWang/ReGuLaR

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨LMK > CLS: Landmark Pooling for Dense Embeddings

📝 Summary:
Landmark pooling improves long-context representation learning by partitioning sequences into chunks and using landmark tokens to preserve both global and local information more effectively than tradi...

🔹 Publication Date: Published on Jan 29

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.21525
• PDF: https://arxiv.org/pdf/2601.21525

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨DINO-SAE: DINO Spherical Autoencoder for High-Fidelity Image Reconstruction and Generation

📝 Summary:
A novel vision autoencoder framework combines semantic representation with pixel-level reconstruction using spherical latent space and Riemannian flow matching for improved fidelity and efficiency. AI...

🔹 Publication Date: Published on Jan 30

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.22904
• PDF: https://arxiv.org/pdf/2601.22904

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨NativeTok: Native Visual Tokenization for Improved Image Generation

📝 Summary:
NativeTok introduces a novel visual tokenization approach that enforces causal dependencies during image encoding, using a Meta Image Transformer and Mixture of Causal Expert Transformer for efficient...

🔹 Publication Date: Published on Jan 30

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.22837
• PDF: https://arxiv.org/pdf/2601.22837
• Github: https://github.com/wangbei1/Nativetok

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨DIFFA-2: A Practical Diffusion Large Language Model for General Audio Understanding

📝 Summary:
DIFFA-2, a diffusion-based large audio language model, achieves competitive audio understanding performance with improved efficiency over autoregressive counterparts through enhanced encoding, dual ad...

🔹 Publication Date: Published on Jan 30

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.23161
• PDF: https://arxiv.org/pdf/2601.23161
• Github: https://github.com/NKU-HLT/DIFFA

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨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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✨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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✨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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✨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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✨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