✨Gaia2: Benchmarking LLM Agents on Dynamic and Asynchronous Environments

📝 Summary:
Gaia2 presents a benchmark for evaluating large language model agents in asynchronous, dynamic environments with temporal constraints and multi-agent collaboration, featuring a write-action verifier f...

🔹 Publication Date: Published on Feb 12

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.11964
• PDF: https://arxiv.org/pdf/2602.11964

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✨MiniCPM-SALA: Hybridizing Sparse and Linear Attention for Efficient Long-Context Modeling

📝 Summary:
MiniCPM-SALA combines sparse and linear attention mechanisms in a hybrid architecture to enable efficient processing of ultra-long contexts while maintaining model performance and reducing training co...

🔹 Publication Date: Published on Feb 12

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.11761
• PDF: https://arxiv.org/pdf/2602.11761
• Github: https://github.com/OpenBMB/MiniCPM

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✨ABot-N0: Technical Report on the VLA Foundation Model for Versatile Embodied Navigation

📝 Summary:
A unified Vision-Language-Action model with a hierarchical architecture combining semantic reasoning and continuous trajectory generation achieves state-of-the-art performance across multiple embodied...

🔹 Publication Date: Published on Feb 12

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.11598
• PDF: https://arxiv.org/pdf/2602.11598
• Project Page: https://amap-cvlab.github.io/ABot-Navigation/ABot-N0/
• Github: https://github.com/amap-cvlab/ABot-Navigation/tree/ABot-N0

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✨Stroke of Surprise: Progressive Semantic Illusions in Vector Sketching

📝 Summary:
Progressive Semantic Illusions use a generative framework with dual-branch Score Distillation Sampling to create vector sketches that transform semantically through sequential stroke additions, achiev...

🔹 Publication Date: Published on Feb 12

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.12280
• PDF: https://arxiv.org/pdf/2602.12280
• Project Page: https://stroke-of-surprise.github.io/
• Github: https://github.com/stroke-of-surprise/Stroke-Of-Surprise

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✨MOSS-Audio-Tokenizer: Scaling Audio Tokenizers for Future Audio Foundation Models

📝 Summary:
A fully end-to-end Transformer-based audio tokenizer architecture achieves high-fidelity reconstruction across diverse audio domains and enables superior text-to-speech and automatic speech recognitio...

🔹 Publication Date: Published on Feb 11

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.10934
• PDF: https://arxiv.org/pdf/2602.10934
• Github: https://github.com/OpenMOSS/MOSS-Audio-Tokenizer

🔹 Models citing this paper:
• https://huggingface.co/OpenMOSS-Team/MOSS-Audio-Tokenizer

✨ Spaces citing this paper:
• https://huggingface.co/spaces/OpenMOSS-Team/MOSS-TTSD

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✨Sci-CoE: Co-evolving Scientific Reasoning LLMs via Geometric Consensus with Sparse Supervision

📝 Summary:
Sci-CoE is a two-stage scientific co-evolving framework that enables large language models to self-evolve as both solver and verifier through sparse-to-unsupervised learning transitions, improving sci...

🔹 Publication Date: Published on Feb 12

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.12164
• PDF: https://arxiv.org/pdf/2602.12164

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✨Pretraining A Large Language Model using Distributed GPUs: A Memory-Efficient Decentralized Paradigm

📝 Summary:
A memory-efficient decentralized framework for training mixture-of-experts language models using sparse expert synchronization and expert-merging warm-up strategies. AI-generated summary Pretraining l...

🔹 Publication Date: Published on Feb 12

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.11543
• PDF: https://arxiv.org/pdf/2602.11543
• Github: https://github.com/zjr2000/SPES

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✨Multimodal Fact-Level Attribution for Verifiable Reasoning

📝 Summary:
MuRGAt is a benchmark for evaluating fact-level multimodal attribution in complex reasoning tasks, requiring models to provide precise citations for their answers across video, audio, and other modali...

🔹 Publication Date: Published on Feb 12

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.11509
• PDF: https://arxiv.org/pdf/2602.11509
• Github: https://github.com/meetdavidwan/murgat

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✨NarraScore: Bridging Visual Narrative and Musical Dynamics via Hierarchical Affective Control

📝 Summary:
NarraScore is a hierarchical framework for long video soundtracks. It uses frozen Vision-Language Models as affective sensors to distill narrative emotion. A dual injection strategy combines global stability with local modulation for efficient, narratively aligned soundtracks.

🔹 Publication Date: Published on Feb 9

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.09070
• PDF: https://arxiv.org/pdf/2602.09070

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✨T3D: Few-Step Diffusion Language Models via Trajectory Self-Distillation with Direct Discriminative Optimization

📝 Summary:
A trajectory self-distillation framework with direct discriminative optimization improves few-step decoding efficiency in diffusion large language models while maintaining generation quality. AI-gener...

🔹 Publication Date: Published on Feb 12

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.12262
• PDF: https://arxiv.org/pdf/2602.12262
• Github: https://github.com/Tyrion58/T3D

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✨PISCO: Precise Video Instance Insertion with Sparse Control

📝 Summary:
Video diffusion model PISCO enables precise instance insertion with sparse keyframe control through variable-information guidance and distribution-preserving temporal masking. AI-generated summary The...

🔹 Publication Date: Published on Feb 9

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.08277
• PDF: https://arxiv.org/pdf/2602.08277
• Project Page: https://xiangbogaobarry.github.io/PISCO/
• Github: https://github.com/taco-group/PISCO

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✨DeepSight: An All-in-One LM Safety Toolkit

📝 Summary:
DeepSight is an open-source project that integrates safety evaluation and diagnosis for large language and multimodal models, enabling white-box insights through unified protocols and specialized tool...

🔹 Publication Date: Published on Feb 12

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.12092
• PDF: https://arxiv.org/pdf/2602.12092
• Project Page: https://github.com/AI45Lab/DeepScan/
• Github: https://github.com/AI45Lab/DeepSafe

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✨Think Longer to Explore Deeper: Learn to Explore In-Context via Length-Incentivized Reinforcement Learning

📝 Summary:
Models require in-context exploration capabilities to scale effectively at test time, but autoregressive generation faces exponential decay in sampling long sequences, which is addressed by a length-i...

🔹 Publication Date: Published on Feb 12

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.11748
• PDF: https://arxiv.org/pdf/2602.11748
• Github: https://github.com/LINs-lab/LIE

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✨DeepGen 1.0: A Lightweight Unified Multimodal Model for Advancing Image Generation and Editing

📝 Summary:
A lightweight 5B unified multimodal model achieves competitive performance through hierarchical feature extraction, learnable think tokens, and progressive training strategies including alignment pre-...

🔹 Publication Date: Published on Feb 12

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.12205
• PDF: https://arxiv.org/pdf/2602.12205
• Project Page: https://deepgenteam.github.io/
• Github: https://github.com/DeepGenTeam/DeepGen

🔹 Models citing this paper:
• https://huggingface.co/deepgenteam/DeepGen-1.0

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✨P-GenRM: Personalized Generative Reward Model with Test-time User-based Scaling

📝 Summary:
Personalized generative reward models address challenges in adapting language model responses to individual user preferences by using structured evaluation chains and dual-granularity scaling mechanis...

🔹 Publication Date: Published on Feb 12

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.12116
• PDF: https://arxiv.org/pdf/2602.12116
• Github: https://github.com/Tongyi-ConvAI/Qwen-Character/tree/main/Character-GenRM

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✨Detecting RLVR Training Data via Structural Convergence of Reasoning

📝 Summary:
RLVR training induces a detectable behavioral signature where seen prompts yield less diverse generations. A new black-box detector, Min-kNN Distance, quantifies this structural convergence to reliably detect RLVR training data, outperforming existing methods.

🔹 Publication Date: Published on Feb 12

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.11792
• PDF: https://arxiv.org/pdf/2602.11792
• Project Page: https://stevenzhb.github.io/detect-rlvr-data/
• Github: https://github.com/StevenZHB/Detect_RLVR_Data

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#AI #MachineLearning #GenerativeAI #LLMs #DataDetection

✨Thinking with Drafting: Optical Decompression via Logical Reconstruction

📝 Summary:
Current AI struggles with precise visual reasoning. We propose Thinking with Drafting TwD, a DSL-based approach to decompress visual tokens into logical structures. This generates verifiable visual proofs, making visual generation a logical verifier for robust reasoning.

🔹 Publication Date: Published on Feb 12

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.11731
• PDF: https://arxiv.org/pdf/2602.11731

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#AI #VisualReasoning #ComputerVision #Logic #RobustAI

✨MetaphorStar: Image Metaphor Understanding and Reasoning with End-to-End Visual Reinforcement Learning

📝 Summary:
MetaphorStar is an end-to-end visual reinforcement learning framework that solves AIs challenge in understanding image metaphors. It uses a new dataset, RL method, and benchmark. MetaphorStar achieves state-of-the-art performance, outperforming many MLLMs and improving general visual reasoning.

🔹 Publication Date: Published on Feb 11

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.10575
• PDF: https://arxiv.org/pdf/2602.10575
• Project Page: https://metaphorstar.github.io/
• Github: https://github.com/MING-ZCH/MetaphorStar

🔹 Models citing this paper:
• https://huggingface.co/MING-ZCH/MetaphorStar-32B
• https://huggingface.co/MING-ZCH/MetaphorStar-3B
• https://huggingface.co/MING-ZCH/MetaphorStar-7B

✨ Datasets citing this paper:
• https://huggingface.co/datasets/MING-ZCH/TFQ-Bench-Lite
• https://huggingface.co/datasets/MING-ZCH/TFQ-Bench-Full
• https://huggingface.co/datasets/MING-ZCH/TFQ-Data-Full

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✨Composition-RL: Compose Your Verifiable Prompts for Reinforcement Learning of Large Language Models

📝 Summary:
Composition-RL improves RL by composing multiple easy problems into new, verifiable questions. This enhances model reasoning capabilities, especially with curriculum learning and cross-domain applications.

🔹 Publication Date: Published on Feb 12

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.12036
• PDF: https://arxiv.org/pdf/2602.12036

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#ReinforcementLearning #LLMs #PromptEngineering #ArtificialIntelligence #MachineLearning