✨TAPE: Tool-Guided Adaptive Planning and Constrained Execution in Language Model Agents

📝 Summary:
TAPE framework improves language model agent performance in complex environments through enhanced planning and constrained execution strategies. AI-generated summary Language Model (LM) agents have de...

🔹 Publication Date: Published on Feb 23

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.19633
• PDF: https://arxiv.org/pdf/2602.19633
• Github: https://github.com/UW-Madison-Lee-Lab/TAPE

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✨Benchmark Test-Time Scaling of General LLM Agents

📝 Summary:
General AgentBench evaluates large language model agents across multiple domains and scaling methods, revealing performance degradation and fundamental limitations in sequential and parallel scaling a...

🔹 Publication Date: Published on Feb 22

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.18998
• PDF: https://arxiv.org/pdf/2602.18998
• Project Page: https://general-agentbench.github.io/
• Github: https://github.com/cxcscmu/General-AgentBench

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✨Learning to Detect Language Model Training Data via Active Reconstruction

📝 Summary:
Active Data Reconstruction Attack uses reinforcement learning to identify training data by measuring the reconstructibility of text from model behavior, outperforming existing membership inference att...

🔹 Publication Date: Published on Feb 22

🔹 Paper Links:
• arXiv Page: https://arxiv.org/pdf/2602.19020
• PDF: https://arxiv.org/pdf/2602.19020
• Project Page: https://huggingface.co/ADRA-RL
• Github: https://github.com/oseyosey/MIA-RL

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✨SIMSPINE: A Biomechanics-Aware Simulation Framework for 3D Spine Motion Annotation and Benchmarking

📝 Summary:
The SIMSPINE framework and dataset provide anatomically consistent 3D spinal annotations for natural human movements. This enables data-driven learning of vertebral kinematics and improves spine motion estimation accuracy, offering a benchmark for research.

🔹 Publication Date: Published on Feb 24

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.20792
• PDF: https://arxiv.org/pdf/2602.20792
• Project Page: https://saifkhichi.com/research/simspine
• Github: https://github.com/dfki-av/simspine

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✨RankEvolve: Automating the Discovery of Retrieval Algorithms via LLM-Driven Evolution

📝 Summary:
Large language models guided by evaluators and evolutionary search can automatically discover improved lexical retrieval algorithms through program evolution techniques. AI-generated summary Retrieval...

🔹 Publication Date: Published on Feb 18

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.16932
• PDF: https://arxiv.org/pdf/2602.16932
• Github: https://github.com/fangchenli/ranking-evolved

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✨JavisDiT++: Unified Modeling and Optimization for Joint Audio-Video Generation

📝 Summary:
JavisDiT++ presents a unified framework for high-quality, synchronized joint audio-video generation. It uses modality-specific Mixture-of-Experts, temporal-aligned RoPE for frame-level sync, and audio-video direct preference optimization. This achieves state-of-the-art performance with limited tr...

🔹 Publication Date: Published on Feb 22

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.19163
• PDF: https://arxiv.org/pdf/2602.19163
• Project Page: https://javisverse.github.io/JavisDiT2-page/
• Github: https://javisverse.github.io/JavisDiT2-page/

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✨HyTRec: A Hybrid Temporal-Aware Attention Architecture for Long Behavior Sequential Recommendation

📝 Summary:
HyTRec addresses the challenge of modeling long user behavior sequences by combining linear and softmax attention mechanisms with a temporal-aware delta network to balance efficiency and retrieval pre...

🔹 Publication Date: Published on Feb 20

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.18283
• PDF: https://arxiv.org/pdf/2602.18283

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✨ARLArena: A Unified Framework for Stable Agentic Reinforcement Learning

📝 Summary:
ARLArena framework analyzes training stability in agentic reinforcement learning and proposes SAMPO method for stable policy optimization across diverse tasks. AI-generated summary Agentic reinforceme...

🔹 Publication Date: Published on Feb 25

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.21534
• PDF: https://arxiv.org/pdf/2602.21534
• Github: https://github.com/WillDreamer/ARL-Arena

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✨SkyReels-V4: Multi-modal Video-Audio Generation, Inpainting and Editing model

📝 Summary:
SkyReels V4 is a unified multimodal video foundation model that generates, edits, and inpaints video and audio simultaneously using a dual-stream architecture with shared text encoding and efficient h...

🔹 Publication Date: Published on Feb 25

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.21818
• PDF: https://arxiv.org/pdf/2602.21818

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✨World Guidance: World Modeling in Condition Space for Action Generation

📝 Summary:
World Guidance framework enhances Vision-Language-Action models by mapping future observations into compact conditions for improved action generation and generalization. AI-generated summary Leveragin...

🔹 Publication Date: Published on Feb 25

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.22010
• PDF: https://arxiv.org/pdf/2602.22010
• Project Page: https://selen-suyue.github.io/WoGNet/

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✨JAEGER: Joint 3D Audio-Visual Grounding and Reasoning in Simulated Physical Environments

📝 Summary:
JAEGER extends audio-visual large language models to 3D space by integrating RGB-D observations and multi-channel audio to improve spatial reasoning and source localization. AI-generated summary Curre...

🔹 Publication Date: Published on Feb 20

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.18527
• PDF: https://arxiv.org/pdf/2602.18527

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✨Model Context Protocol (MCP) Tool Descriptions Are Smelly! Towards Improving AI Agent Efficiency with Augmented MCP Tool Descriptions

📝 Summary:
Foundation model agents rely on natural language tool descriptions for effective interaction with external systems, but poor description quality significantly impacts performance and efficiency. AI-ge...

🔹 Publication Date: Published on Feb 16

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.14878
• PDF: https://arxiv.org/pdf/2602.14878

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✨UniVBench: Towards Unified Evaluation for Video Foundation Models

📝 Summary:
UniVBench introduces a comprehensive benchmark for evaluating video foundation models across multiple capabilities including understanding, generation, editing, and reconstruction using high-quality, ...

🔹 Publication Date: Published on Feb 25

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.21835
• PDF: https://arxiv.org/pdf/2602.21835
• Github: https://github.com/JianhuiWei7/UniVBench

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✨The Design Space of Tri-Modal Masked Diffusion Models

📝 Summary:
A large-scale study of tri-modal discrete diffusion models demonstrates improved performance across text, image, and speech generation tasks through systematic analysis of scaling laws and optimized i...

🔹 Publication Date: Published on Feb 25

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.21472
• PDF: https://arxiv.org/pdf/2602.21472

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✨Solaris: Building a Multiplayer Video World Model in Minecraft

📝 Summary:
Solaris is a multiplayer video world model that simulates consistent multi-view observations through a novel data collection system and staged training approach. AI-generated summary Existing action-c...

🔹 Publication Date: Published on Feb 25

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.22208
• PDF: https://arxiv.org/pdf/2602.22208
• Project Page: https://solaris-wm.github.io/
• Github: https://github.com/solaris-wm/solaris

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✨DreamID-Omni: Unified Framework for Controllable Human-Centric Audio-Video Generation

📝 Summary:
DreamID-Omni is a unified framework for controllable human-centric audio-video generation that uses a symmetric conditional diffusion transformer with dual-level disentanglement and multi-task progres...

🔹 Publication Date: Published on Feb 12

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.12160
• PDF: https://arxiv.org/pdf/2602.12160
• Project Page: https://guoxu1233.github.io/DreamID-Omni/
• Github: https://github.com/Guoxu1233/DreamID-Omni

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✨GUI-Libra: Training Native GUI Agents to Reason and Act with Action-aware Supervision and Partially Verifiable RL

📝 Summary:
GUI-Libra addresses limitations in open-source GUI agents through specialized training methods that improve reasoning-grounding alignment and reinforcement learning under partial verifiability, demons...

🔹 Publication Date: Published on Feb 25

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.22190
• PDF: https://arxiv.org/pdf/2602.22190
• Project Page: https://gui-libra.github.io
• Github: https://github.com/GUI-Libra/GUI-Libra

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✨NoLan: Mitigating Object Hallucinations in Large Vision-Language Models via Dynamic Suppression of Language Priors

📝 Summary:
Object hallucinations in LVLMs are primarily caused by language decoder priors, leading to the development of a training-free framework that suppresses these priors to reduce hallucinations. AI-genera...

🔹 Publication Date: Published on Feb 25

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.22144
• PDF: https://arxiv.org/pdf/2602.22144
• Github: https://github.com/lingfengren/NoLan

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✨MoBind: Motion Binding for Fine-Grained IMU-Video Pose Alignment

📝 Summary:
MoBind learns joint representations between IMU signals and 2D pose sequences through hierarchical contrastive learning to achieve cross-modal retrieval, temporal synchronization, and action recogniti...

🔹 Publication Date: Published on Feb 22

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.19004
• PDF: https://arxiv.org/pdf/2602.19004
• Github: https://github.com/bbvisual/MoBind

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✨NanoKnow: How to Know What Your Language Model Knows

📝 Summary:
NanoKnow is a benchmark using open pre-training data to analyze how LLMs acquire knowledge. It shows accuracy relies on pre-training frequency, which external evidence can mitigate, and that parametric and external knowledge are complementary, but irrelevant data is harmful.

🔹 Publication Date: Published on Feb 23

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.20122
• PDF: https://arxiv.org/pdf/2602.20122
• Github: https://github.com/castorini/NanoKnow/tree/main

✨ Datasets citing this paper:
• https://huggingface.co/datasets/LingweiGu/NanoKnow-Fineweb-Edu-Index
• https://huggingface.co/datasets/LingweiGu/NanoKnow_Benchmark

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✨Image Generation with a Sphere Encoder

📝 Summary:
The Sphere Encoder is an efficient generative model that maps images to a spherical latent space. It produces high-quality images in a single pass, matching diffusion models at a fraction of the inference cost.

🔹 Publication Date: Published on Feb 16

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.15030
• PDF: https://arxiv.org/pdf/2602.15030
• Project Page: https://sphere-encoder.github.io

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