✨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

✨Drive-JEPA: Video JEPA Meets Multimodal Trajectory Distillation for End-to-End Driving

📝 Summary:
Drive-JEPA combines V-JEPA video pretraining with multimodal trajectory distillation to achieve state-of-the-art performance in end-to-end autonomous driving. AI-generated summary End-to-end autonomou...

🔹 Publication Date: Published on Jan 29

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.22032
• PDF: https://arxiv.org/pdf/2601.22032
• Project Page: https://github.com/linhanwang/Drive-JEPA
• Github: https://github.com/linhanwang/Drive-JEPA

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨Memorization Dynamics in Knowledge Distillation for Language Models

📝 Summary:
Knowledge distillation reduces training data memorization compared to standard fine-tuning while maintaining performance, with distinct memorization patterns and predictability based on input characte...

🔹 Publication Date: Published on Jan 21

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.15394
• PDF: https://arxiv.org/pdf/2601.15394

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨RAPTOR: Ridge-Adaptive Logistic Probes

📝 Summary:
RAPTOR is a ridge-adaptive logistic probe that accurately and stably estimates concept vectors for activation steering in frozen LLMs. It significantly reduces training costs while matching or exceeding baseline accuracy and stability. Theoretical analysis underpins its efficacy.

🔹 Publication Date: Published on Jan 29

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.00158
• PDF: https://arxiv.org/pdf/2602.00158

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨FS-Researcher: Test-Time Scaling for Long-Horizon Research Tasks with File-System-Based Agents

📝 Summary:
FS-Researcher is a dual-agent framework that scales LLM research tasks beyond context window limits. It uses a file system as persistent external memory, enabling a Context Builder and Report Writer to achieve state-of-the-art report quality and effective test-time scaling.

🔹 Publication Date: Published on Feb 2

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.01566
• PDF: https://arxiv.org/pdf/2602.01566

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨How Well Do Models Follow Visual Instructions? VIBE: A Systematic Benchmark for Visual Instruction-Driven Image Editing

📝 Summary:
Visual Instruction Benchmark for Image Editing introduces a three-level interaction hierarchy for evaluating visual instruction following capabilities in generative models. AI-generated summary Recent...

🔹 Publication Date: Published on Feb 2

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.01851
• PDF: https://arxiv.org/pdf/2602.01851
• Github: https://vibe-benchmark.github.io/

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨Ebisu: Benchmarking Large Language Models in Japanese Finance

📝 Summary:
A Japanese financial language understanding benchmark named Ebisu is introduced, featuring two expert-annotated tasks that evaluate implicit commitment recognition and hierarchical financial terminolo...

🔹 Publication Date: Published on Feb 1

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.01479
• PDF: https://arxiv.org/pdf/2602.01479

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

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#AI #DataScience #MachineLearning #HuggingFace #Research