✨Klear: Unified Multi-Task Audio-Video Joint Generation

📝 Summary:
Klear addresses audio-video joint generation challenges through a unified model architecture, progressive multitask training, and large-scale dense-caption data construction, achieving superior alignm...

🔹 Publication Date: Published on Jan 7

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.04151
• PDF: https://arxiv.org/pdf/2601.04151

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✨RedBench: A Universal Dataset for Comprehensive Red Teaming of Large Language Models

📝 Summary:
RedBench presents a unified dataset with standardized risk categorization for evaluating LLM vulnerabilities across multiple domains and attack types. AI-generated summary As large language models (LL...

🔹 Publication Date: Published on Jan 7

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.03699
• PDF: https://arxiv.org/pdf/2601.03699
• Github: https://github.com/knoveleng/redeval

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✨Entropy-Adaptive Fine-Tuning: Resolving Confident Conflicts to Mitigate Forgetting

📝 Summary:
Supervised Fine-Tuning causes catastrophic forgetting due to 'Confident Conflicts.' Entropy-Adaptive Fine-Tuning EAFT uses token-level entropy to distinguish uncertainty from knowledge conflict. EAFT suppresses conflicting gradients, mitigating forgetting while matching performance.

🔹 Publication Date: Published on Jan 5

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.02151
• PDF: https://arxiv.org/pdf/2601.02151
• Project Page: https://ymxyll.github.io/EAFT/
• Github: https://ymxyll.github.io/EAFT/

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✨Benchmark^2: Systematic Evaluation of LLM Benchmarks

📝 Summary:
Researchers developed Benchmark^2, a framework with three metrics to evaluate benchmark quality for large language models, revealing significant variations in existing benchmarks and enabling more eff...

🔹 Publication Date: Published on Jan 7

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.03986
• PDF: https://arxiv.org/pdf/2601.03986

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✨ThinkRL-Edit: Thinking in Reinforcement Learning for Reasoning-Centric Image Editing

📝 Summary:
ThinkRL-Edit enhances reasoning-centric image editing through reinforcement learning by expanding visual reasoning exploration beyond denoising stochasticity and using unbiased reward strategies. AI-g...

🔹 Publication Date: Published on Jan 6

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.03467
• PDF: https://arxiv.org/pdf/2601.03467

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✨Atlas: Orchestrating Heterogeneous Models and Tools for Multi-Domain Complex Reasoning

📝 Summary:
ATLAS is a dual-path framework that dynamically selects optimal model-tool combinations for complex reasoning. It uses cluster-based routing for domain-specific tasks and RL-based multi-step routing for generalization. ATLAS outperforms GPT-4o and other methods on diverse benchmarks.

🔹 Publication Date: Published on Jan 7

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.03872
• PDF: https://arxiv.org/pdf/2601.03872

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✨MAGMA: A Multi-Graph based Agentic Memory Architecture for AI Agents

📝 Summary:
MAGMA is a multi-graph memory architecture that improves AI agent long-context reasoning. It decouples memory representation from retrieval logic across semantic, temporal, causal, and entity graphs for query-adaptive selection, outperforming existing agentic memory systems.

🔹 Publication Date: Published on Jan 6

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.03236
• PDF: https://arxiv.org/pdf/2601.03236

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#AIAgents #MemoryArchitecture #LongContextReasoning #GraphAI #ArtificialIntelligence

✨SimpleMem: Efficient Lifelong Memory for LLM Agents

📝 Summary:
SimpleMem is an efficient memory framework for LLM agents that uses semantic lossless compression. It employs a three-stage pipeline to distill, consolidate, and retrieve historical experiences efficiently. SimpleMem significantly improves accuracy and reduces token consumption by up to 30-fold c...

🔹 Publication Date: Published on Jan 5

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.02553
• PDF: https://arxiv.org/pdf/2601.02553
• Project Page: https://aiming-lab.github.io/SimpleMem-Page/
• Github: https://aiming-lab.github.io/SimpleMem-Page/

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#LLM #AIAgents #LifelongLearning #AI #DeepLearning

✨RGS-SLAM: Robust Gaussian Splatting SLAM with One-Shot Dense Initialization

📝 Summary:
RGS-SLAM is a robust Gaussian-splatting SLAM framework that uses a one-shot, correspondence-to-Gaussian initialization with DINOv3 descriptors. This method improves stability, accelerates convergence, and yields higher rendering fidelity and accuracy compared to existing systems.

🔹 Publication Date: Published on Dec 28, 2025

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.00705
• PDF: https://arxiv.org/pdf/2601.00705

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#SLAM #GaussianSplatting #ComputerVision #Robotics #DeepLearning

✨Enhancing Linguistic Competence of Language Models through Pre-training with Language Learning Tasks

📝 Summary:
L2T, a new pre-training framework, integrates language learning tasks with next-token prediction. This enhances language models' linguistic competence and acquisition without sacrificing general reasoning.

🔹 Publication Date: Published on Jan 6

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.03448
• PDF: https://arxiv.org/pdf/2601.03448
• Project Page: https://huggingface.co/l2t-project/l2t-500m-disjoint-mix_75
• Github: https://github.com/gucci-j/l2t

🔹 Models citing this paper:
• https://huggingface.co/l2t-project/l2t-500m-disjoint
• https://huggingface.co/l2t-project/l2t-500m-disjoint-mix_0
• https://huggingface.co/l2t-project/l2t-500m-disjoint-mix_25

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

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#LLM #NLP #Pretraining #LanguageLearning #AI

✨Why LLMs Aren't Scientists Yet: Lessons from Four Autonomous Research Attempts

📝 Summary:
A case study of four LLM agent attempts to autonomously generate ML research papers reveals six recurring failure modes. Most attempts failed, though one was accepted to a special AI-first author venue, leading to proposed design principles for future AI-scientist systems.

🔹 Publication Date: Published on Jan 6

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.03315
• PDF: https://arxiv.org/pdf/2601.03315

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

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#LLMs #AIResearch #MachineLearning #AIAgents #AutonomousSystems

✨Gen3R: 3D Scene Generation Meets Feed-Forward Reconstruction

📝 Summary:
Gen3R combines reconstruction and video diffusion models to generate 3D scenes. It produces RGB videos and 3D geometry by aligning geometric and appearance latents. This achieves state-of-the-art results and improves reconstruction robustness.

🔹 Publication Date: Published on Jan 7

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.04090
• PDF: https://arxiv.org/pdf/2601.04090
• Project Page: https://xdimlab.github.io/Gen3R/
• Github: https://xdimlab.github.io/Gen3R/

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#3DGeneration #DiffusionModels #ComputerVision #3DReconstruction #DeepLearning

✨Evolving Programmatic Skill Networks

📝 Summary:
The Programmatic Skill Network PSN enables continual skill acquisition through executable symbolic programs that evolve via reflection, progressive optimization, and structural refactoring. This framework demonstrates robust skill reuse, rapid adaptation, and strong generalization in open-ended e...

🔹 Publication Date: Published on Jan 7

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.03509
• PDF: https://arxiv.org/pdf/2601.03509

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

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#ProgrammaticAI #SkillAcquisition #EvolutionaryAI #MachineLearning #AIResearch

✨Pearmut: Human Evaluation of Translation Made Trivial

📝 Summary:
Pearmut is a lightweight platform that simplifies complex human evaluation for multilingual NLP, particularly machine translation. It removes setup barriers by supporting various protocols, document context, and learning strategies. This makes reliable human evaluation a routine and practical par...

🔹 Publication Date: Published on Jan 6

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.02933
• PDF: https://arxiv.org/pdf/2601.02933
• Github: https://github.com/zouharvi/pearmut

✨ Datasets citing this paper:
• https://huggingface.co/datasets/zouharvi/hearing2translate-humeval

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

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✨ResTok: Learning Hierarchical Residuals in 1D Visual Tokenizers for Autoregressive Image Generation

📝 Summary:
A novel 1D visual tokenizer called Residual Tokenizer is introduced that incorporates hierarchical residuals to improve autoregressive image generation by leveraging vision-specific design principles ...

🔹 Publication Date: Published on Jan 7

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.03955
• PDF: https://arxiv.org/pdf/2601.03955

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✨ROI-Reasoning: Rational Optimization for Inference via Pre-Computation Meta-Cognition

📝 Summary:
ROI Reasoning enables large language models to strategically allocate computation under strict token budgets. It uses meta-cognition to predict costs and utilities, optimizing sequential decisions with reinforcement learning. This improves performance and reduces regret on budgeted reasoning tasks.

🔹 Publication Date: Published on Jan 7

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.03822
• PDF: https://arxiv.org/pdf/2601.03822

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✨RelayLLM: Efficient Reasoning via Collaborative Decoding

📝 Summary:
RelayLLM enables efficient collaborative reasoning by having a small language model dynamically invoke a large language model only for critical tokens. This token-level collaboration achieves high accuracy with minimal computational overhead. It reduces LLM invocation to just 1.07% of tokens, lea...

🔹 Publication Date: Published on Jan 8

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.05167
• PDF: https://arxiv.org/pdf/2601.05167

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✨VideoAuto-R1: Video Auto Reasoning via Thinking Once, Answering Twice

📝 Summary:
VideoAuto-R1 framework employs a reason-when-necessary strategy for video understanding, using a Thinking Once, Answering Twice training paradigm with verifiable rewards and confidence-based reasoning...

🔹 Publication Date: Published on Jan 8

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.05175
• PDF: https://arxiv.org/pdf/2601.05175
• Project Page: https://ivul-kaust.github.io/projects/videoauto-r1/

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✨VerseCrafter: Dynamic Realistic Video World Model with 4D Geometric Control

📝 Summary:
VerseCrafter is a 4D-aware video world model that enables unified control over camera and object dynamics through 4D geometric control representation and video diffusion models. AI-generated summary V...

🔹 Publication Date: Published on Jan 8

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.05138
• PDF: https://arxiv.org/pdf/2601.05138

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✨Agent-as-a-Judge

📝 Summary:
Large language models face limitations in evaluating complex, multi-step tasks, prompting the development of agent-based evaluation systems that utilize planning, tool-augmented verification, and mult...

🔹 Publication Date: Published on Jan 8

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.05111
• PDF: https://arxiv.org/pdf/2601.05111

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

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