✨AgentSPEX: An Agent SPecification and EXecution Language

📝 Summary:
AgentSPEX is a domain-specific language and framework for creating structured, modular, and interpretable large language model agent workflows with explicit control flow and state management. AI-gener...

🔹 Publication Date: Published on Apr 14

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.13346
• PDF: https://arxiv.org/pdf/2604.13346
• Project Page: https://agentspex.ai/
• Github: https://github.com/ScaleML/AgentSPEX

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✨CoInteract: Physically-Consistent Human-Object Interaction Video Synthesis via Spatially-Structured Co-Generation

📝 Summary:
CoInteract presents an end-to-end framework for human-object interaction video synthesis using a Diffusion Transformer backbone with specialized modules for structural stability and physical plausibil...

🔹 Publication Date: Published on Apr 21

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.19636
• PDF: https://arxiv.org/pdf/2604.19636
• Project Page: https://xinxiaozhe12345.github.io/CoInteract_Project/
• Github: https://github.com/luoxyhappy/CoInteract

🔹 Models citing this paper:
• https://huggingface.co/georgexin/cointeract

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✨PlayCoder: Making LLM-Generated GUI Code Playable

📝 Summary:
Large language models struggle to generate logically correct GUI applications, prompting the development of PlayEval benchmark and PlayCoder framework that uses multi-agent approaches to improve funct...

🔹 Publication Date: Published on Apr 21

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.19742
• PDF: https://arxiv.org/pdf/2604.19742
• Project Page: https://arxiv.org/abs/2604.19742
• Github: https://github.com/Tencent/PlayCoder

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✨Dual-View Training for Instruction-Following Information Retrieval

📝 Summary:
A dual-view data synthesis approach using polarity reversal enhances retrieval systems' ability to follow instructions by training models to distinguish between topic-relevant and instruction-complian...

🔹 Publication Date: Published on Apr 20

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.18845
• PDF: https://arxiv.org/pdf/2604.18845

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

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✨Code-Switching Information Retrieval: Benchmarks, Analysis, and the Limits of Current Retrievers

📝 Summary:
Code-switching poses significant challenges for information retrieval systems, revealing performance bottlenecks and embedding space divergences that current multilingual approaches cannot fully addre...

🔹 Publication Date: Published on Apr 19

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.17632
• PDF: https://arxiv.org/pdf/2604.17632
• Github: https://github.com/paddler2022/Code-Switching-Information-Retrieval

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✨LoopCTR: Unlocking the Loop Scaling Power for Click-Through Rate Prediction

📝 Summary:
LoopCTR introduces a loop scaling paradigm for CTR models that increases training computation through recursive layer reuse while maintaining efficient inference, achieving state-of-the-art performanc...

🔹 Publication Date: Published on Apr 21

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.19550
• PDF: https://arxiv.org/pdf/2604.19550

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

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✨Target-Oriented Pretraining Data Selection via Neuron-Activated Graph

📝 Summary:
A novel target-oriented language model pretraining framework uses neuron activation graphs to select informative data without additional training, demonstrating superior performance across multiple be...

🔹 Publication Date: Published on Apr 17

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.15706
• PDF: https://arxiv.org/pdf/2604.15706
• Project Page: https://asillycat.github.io/NAG-website/
• Github: https://github.com/asillycat/NAG

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✨Tstars-Tryon 1.0: Robust and Realistic Virtual Try-On for Diverse Fashion Items

📝 Summary:
A commercial-scale virtual try-on system achieves high success rates, photorealistic results, and real-time performance through integrated system design and multi-stage training. AI-generated summary ...

🔹 Publication Date: Published on Apr 21

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.19748
• PDF: https://arxiv.org/pdf/2604.19748
• Project Page: https://mpage.taobao.com/hd/download.html

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

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✨MM-JudgeBias: A Benchmark for Evaluating Compositional Biases in MLLM-as-a-Judge

📝 Summary:
Research identifies systematic biases in multimodal large language models used as automatic evaluators, revealing reliability issues and proposing a benchmark for measuring compositional bias through ...

🔹 Publication Date: Published on Apr 20

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.18164
• PDF: https://arxiv.org/pdf/2604.18164
• Project Page: https://mm-judgebias.github.io/
• Github: https://github.com/naver-ai/MM-JudgeBias

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✨ClawNet: Human-Symbiotic Agent Network for Cross-User Autonomous Cooperation

📝 Summary:
AI agents must evolve beyond individual task automation to enable secure, governed collaboration among multiple users through a human-symbiotic paradigm with identity-based governance mechanisms. AI-g...

🔹 Publication Date: Published on Apr 21

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.19211
• PDF: https://arxiv.org/pdf/2604.19211
• Project Page: https://www.clawnet.hk/

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

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✨UniMesh: Unifying 3D Mesh Understanding and Generation

📝 Summary:
UniMesh presents a unified framework that combines 3D generation and understanding tasks through novel components including a Mesh Head, Chain of Mesh for iterative editing, and a self-reflection mech...

🔹 Publication Date: Published on Apr 19

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.17472
• PDF: https://arxiv.org/pdf/2604.17472
• Project Page: https://aigeeksgroup.github.io/UniMesh/
• Github: https://github.com/AIGeeksGroup/UniMesh

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✨Evaluation-driven Scaling for Scientific Discovery

📝 Summary:
SimpleTES framework scales evaluation-driven discovery loops for scientific problems, achieving state-of-the-art results across multiple domains through parallel exploration and feedback-driven refine...

🔹 Publication Date: Published on Apr 21

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.19341
• PDF: https://arxiv.org/pdf/2604.19341
• Project Page: https://www.wizardquant.com/will/simpletes

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✨SPRITE: From Static Mockups to Engine-Ready Game UI

📝 Summary:
SPRITE enables automated conversion of game UI screenshots into editable engine assets by combining vision-language models with structured YAML representation to handle complex layouts and nesting. AI...

🔹 Publication Date: Published on Mar 18

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.18591
• PDF: https://arxiv.org/pdf/2604.18591
• Project Page: https://baiyunshu.github.io/sprite.github.io/

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

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✨Chat2Workflow: A Benchmark for Generating Executable Visual Workflows with Natural Language

📝 Summary:
Chat2Workflow presents a benchmark and agentic framework for automating executable visual workflow generation from natural language, revealing significant challenges in achieving industrial-grade auto...

🔹 Publication Date: Published on Apr 21

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.19667
• PDF: https://arxiv.org/pdf/2604.19667
• Github: https://github.com/zjunlp/Chat2Workflow

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✨Speculative Decoding for Autoregressive Video Generation

📝 Summary:
Speculative decoding is adapted to autoregressive video diffusion through a quality-based routing mechanism that maintains high visual quality while achieving significant speedup. AI-generated summary...

🔹 Publication Date: Published on Apr 19

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.17397
• PDF: https://arxiv.org/pdf/2604.17397

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

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✨Contrastive Attribution in the Wild: An Interpretability Analysis of LLM Failures on Realistic Benchmarks

📝 Summary:
Contrastive attribution methods for analyzing large language model failures show mixed effectiveness across different benchmarks and model sizes. AI-generated summary Interpretability tools are increa...

🔹 Publication Date: Published on Apr 20

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.17761
• PDF: https://arxiv.org/pdf/2604.17761
• Project Page: https://jzxycsjzy.github.io/Debug-XAI/
• Github: https://github.com/microsoft/Debug-XAI

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✨TEMPO: Scaling Test-time Training for Large Reasoning Models

📝 Summary:
TEMPO is a test-time training framework that alternates policy refinement with critic recalibration to sustain performance improvements in language models without diversity collapse. AI-generated summ...

🔹 Publication Date: Published on Apr 21

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.19295
• PDF: https://arxiv.org/pdf/2604.19295
• Project Page: https://qingyangzhang.github.io/tempo-homepage
• Github: https://github.com/QingyangZhang/TEMPO

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✨SmartPhotoCrafter: Unified Reasoning, Generation and Optimization for Automatic Photographic Image Editing

📝 Summary:
SmartPhotoCrafter automates photographic image editing by combining image quality comprehension with targeted enhancement, using a reasoning-to-generation approach that eliminates the need for explici...

🔹 Publication Date: Published on Apr 21

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.19587
• PDF: https://arxiv.org/pdf/2604.19587

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✨AnyRecon: Arbitrary-View 3D Reconstruction with Video Diffusion Model

📝 Summary:
AnyRecon enables scalable 3D reconstruction from arbitrary sparse inputs using diffusion models with persistent scene memory and geometry-aware conditioning for improved geometric consistency. AI-gene...

🔹 Publication Date: Published on Apr 21

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.19747
• PDF: https://arxiv.org/pdf/2604.19747

🔹 Models citing this paper:
• https://huggingface.co/Yutian10/AnyRecon

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✨Predicting integers from continuous parameters

📝 Summary:
Research examines direct modeling of integer-labeled data using discrete probability distributions with continuous parameters suitable for neural network training, evaluating Bitwise and discrete Lapl...

🔹 Publication Date: Published on Apr 13

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.10751
• PDF: https://arxiv.org/pdf/2602.10751

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✨UDM-GRPO: Stable and Efficient Group Relative Policy Optimization for Uniform Discrete Diffusion Models

📝 Summary:
UDM-GRPO integrates Uniform Discrete Diffusion Models with reinforcement learning, solving training instability issues. It optimizes using final samples as actions and reconstructed trajectories. This achieves state-of-the-art performance in text-to-image generation and OCR tasks.

🔹 Publication Date: Published on Apr 20

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.18518
• PDF: https://arxiv.org/pdf/2604.18518
• Project Page: https://yovecent.github.io/UDM-GRPO.github.io/
• Github: https://github.com/Yovecent/UDM-GRPO

🔹 Models citing this paper:
• https://huggingface.co/Yovecents/URSA-1.7B-IBQ512-UDMGRPO-GenEval
• https://huggingface.co/Yovecents/URSA-1.7B-IBQ512-UDMGRPO-PickScore

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