✨HY-World 2.0: A Multi-Modal World Model for Reconstructing, Generating, and Simulating 3D Worlds

📝 Summary:
HY-World 2.0 is a multi-modal world model framework that generates high-fidelity 3D Gaussian Splatting scenes from diverse inputs using specialized modules for panorama generation, trajectory planning...

🔹 Publication Date: Published on Apr 15

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.14268
• PDF: https://arxiv.org/pdf/2604.14268
• Project Page: https://3d-models.hunyuan.tencent.com/world/
• Github: https://github.com/Tencent-Hunyuan/HY-World-2.0

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✨How to Fine-Tune a Reasoning Model? A Teacher-Student Cooperation Framework to Synthesize Student-Consistent SFT Data

📝 Summary:
Teacher-student cooperation data synthesis framework addresses stylistic divergence in synthetic data for improved model fine-tuning performance. AI-generated summary A widely adopted strategy for mod...

🔹 Publication Date: Published on Mar 23

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.14164
• PDF: https://arxiv.org/pdf/2604.14164
• Github: https://github.com/CoopReason/TESSY

✨ Datasets citing this paper:
• https://huggingface.co/datasets/CoopReason/TESSY-Code-80K

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✨LongAct: Harnessing Intrinsic Activation Patterns for Long-Context Reinforcement Learning

📝 Summary:
LongAct improves long-context reasoning in LLMs by implementing saliency-guided sparse updates based on high-magnitude activation patterns in query and key vectors. AI-generated summary Reinforcement ...

🔹 Publication Date: Published on Apr 16

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.14922
• PDF: https://arxiv.org/pdf/2604.14922

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✨UniDoc-RL: Coarse-to-Fine Visual RAG with Hierarchical Actions and Dense Rewards

📝 Summary:
UniDoc-RL introduces a reinforcement learning framework for LVLMs that jointly optimizes retrieval, reranking, visual perception, and reasoning through hierarchical decision-making and dense multi-rew...

🔹 Publication Date: Published on Apr 16

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.14967
• PDF: https://arxiv.org/pdf/2604.14967
• Github: https://github.com/deepglint/UniDoc-RL

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✨C2: Scalable Rubric-Augmented Reward Modeling from Binary Preferences

📝 Summary:
Cooperative yet Critical reward modeling (C2) enhances reward model reliability by enabling critical collaboration between a reward model and a rubric generator trained exclusively from binary prefere...

🔹 Publication Date: Published on Apr 15

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.13618
• PDF: https://arxiv.org/pdf/2604.13618
• Github: https://github.com/asahi-research/C2

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✨LeapAlign: Post-Training Flow Matching Models at Any Generation Step by Building Two-Step Trajectories

📝 Summary:
LeapAlign improves flow matching model fine-tuning by reducing computational costs and enabling stable gradient propagation through shortened trajectory steps while maintaining alignment with human pr...

🔹 Publication Date: Published on Apr 16

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.15311
• PDF: https://arxiv.org/pdf/2604.15311
• Project Page: https://rockeycoss.github.io/leapalign/

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✨DR^{3}-Eval: Towards Realistic and Reproducible Deep Research Evaluation

📝 Summary:
DR$^{3}$-Eval is a benchmark for evaluating deep research agents on multimodal, multi-file report generation, featuring a realistic simulation of web environments and a comprehensive evaluation framew...

🔹 Publication Date: Published on Apr 16

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.14683
• PDF: https://arxiv.org/pdf/2604.14683
• Github: https://github.com/NJU-LINK/DR3-Eval

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✨Dive into Claude Code: The Design Space of Today's and Future AI Agent Systems

📝 Summary:
The study analyzes Claude Code's architecture, identifying five motivating human values and tracing them through thirteen design principles to specific implementation choices, including a core while-l...

🔹 Publication Date: Published on Apr 14

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.14228
• PDF: https://arxiv.org/pdf/2604.14228
• Github: https://github.com/VILA-Lab/Dive-into-Claude-Code

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✨KV Packet: Recomputation-Free Context-Independent KV Caching for LLMs

📝 Summary:
KV Packet is a cache reuse framework that eliminates recomputation overhead in large language models by treating cached documents as immutable packets with trainable soft-token adapters. AI-generated ...

🔹 Publication Date: Published on Apr 14

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.13226
• PDF: https://arxiv.org/pdf/2604.13226
• Github: https://github.com/ChuangtaoChen-TUM/KVPacket

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✨RAD-2: Scaling Reinforcement Learning in a Generator-Discriminator Framework

📝 Summary:
A unified generator-discriminator framework for autonomous driving motion planning that improves stability and performance through diffusion-based trajectory generation and reinforcement learning opti...

🔹 Publication Date: Published on Apr 16

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.15308
• PDF: https://arxiv.org/pdf/2604.15308
• Project Page: https://hgao-cv.github.io/RAD-2/

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✨HiVLA: A Visual-Grounded-Centric Hierarchical Embodied Manipulation System

📝 Summary:
HiVLA presents a hierarchical vision-language-action framework that decouples semantic planning from motor control using a diffusion transformer action expert with cascaded cross-attention for improve...

🔹 Publication Date: Published on Apr 15

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.14125
• PDF: https://arxiv.org/pdf/2604.14125
• Project Page: https://tianshuoy.github.io/HiVLA-page/

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✨GlobalSplat: Efficient Feed-Forward 3D Gaussian Splatting via Global Scene Tokens

📝 Summary:
GlobalSplat introduces a global scene representation framework that achieves compact, consistent 3D Gaussian splatting with reduced computational overhead and improved inference speed. AI-generated su...

🔹 Publication Date: Published on Apr 16

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.15284
• PDF: https://arxiv.org/pdf/2604.15284
• Project Page: https://r-itk.github.io/globalsplat/

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✨Model Capability Dominates: Inference-Time Optimization Lessons from AIMO 3

📝 Summary:
Majority voting improves mathematical reasoning but is limited by correlated errors; diverse reasoning strategies and model capability are more impactful than prompt engineering. AI-generated summary ...

🔹 Publication Date: Published on Apr 16

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.27844
• PDF: https://arxiv.org/pdf/2603.27844
• Project Page: https://www.kaggle.com/code/natnitarach/aimo-3-model-capability-dominate
• Github: https://github.com/nat-nischw/model-capability-dominates-lessons-aimo3

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✨SuperLocalMemory V3.3: The Living Brain -- Biologically-Inspired Forgetting, Cognitive Quantization, and Multi-Channel Retrieval for Zero-LLM Agent Memory Systems

📝 Summary:
A new local-first agent memory system implements comprehensive cognitive memory processes with enhanced retrieval and forgetting mechanisms, achieving superior performance in zero-LLM settings. AI-gen...

🔹 Publication Date: Published on Apr 6

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.04514
• PDF: https://arxiv.org/pdf/2604.04514
• Project Page: https://superlocalmemory.com/
• Github: https://github.com/qualixar/superlocalmemory

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✨TRACER: Trace-Based Adaptive Cost-Efficient Routing for LLM Classification

📝 Summary:
TRACER trains ML surrogates using LLM classification production traces. These cost-efficient surrogates activate only if they agree with the original LLM above a threshold, saving significant costs. TRACER also provides interpretability for its routing decisions and achieves high coverage.

🔹 Publication Date: Published on Apr 16

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.14531
• PDF: https://arxiv.org/pdf/2604.14531

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✨OneHOI: Unifying Human-Object Interaction Generation and Editing

📝 Summary:
OneHOI is a unified diffusion transformer framework that consolidates human-object interaction generation and editing into a single conditional denoising process. It uses structured interaction representations to overcome limitations of prior approaches, achieving state-of-the-art results across ...

🔹 Publication Date: Published on Apr 15

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.14062
• PDF: https://arxiv.org/pdf/2604.14062
• Project Page: https://jiuntian.github.io/OneHOI/
• Github: https://github.com/jiuntian/OneHOI

✨ Datasets citing this paper:
• https://huggingface.co/datasets/jiuntian/hoiedit44k
• https://huggingface.co/datasets/jiuntian/IEBench

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✨Towards Autonomous Mechanistic Reasoning in Virtual Cells

📝 Summary:
Large language models are enhanced for biological research through a multi-agent framework that generates and validates mechanistic explanations using structured formalism and verified datasets. AI-ge...

🔹 Publication Date: Published on Apr 14

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.11661
• PDF: https://arxiv.org/pdf/2604.11661
• Project Page: https://valencelabs.substack.com/p/towards-reasoning-in-virtual-cells
• Github: https://github.com/valence-labs/VCR-Agent

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✨Switch-KD: Visual-Switch Knowledge Distillation for Vision-Language Models

📝 Summary:
Vision-language models face deployment challenges due to their large size, but knowledge distillation can improve efficiency while maintaining performance through a novel visual-switch framework that ...

🔹 Publication Date: Published on Apr 16

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.14629
• PDF: https://arxiv.org/pdf/2604.14629
• Project Page: https://haoyi199815.github.io/Switch-KD/
• Github: https://github.com/haoyi199815/Switch-KD

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✨Representations Before Pixels: Semantics-Guided Hierarchical Video Prediction

📝 Summary:
Re2Pix is a hierarchical video prediction framework that improves future video generation by first predicting semantic representations and then using them to guide photorealistic visual synthesis, add...

🔹 Publication Date: Published on Apr 13

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.11707
• PDF: https://arxiv.org/pdf/2604.11707
• Github: https://github.com/Sta8is/Re2Pix

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✨Don't Retrieve, Navigate: Distilling Enterprise Knowledge into Navigable Agent Skills for QA and RAG

📝 Summary:
Corpus2Skill structures document corpora into hierarchical skill directories for LLM agents. This allows agents to navigate, reason about information, and combine evidence more effectively than traditional RAG. It significantly outperforms other RAG methods on an enterprise benchmark.

🔹 Publication Date: Published on Apr 16

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.14572
• PDF: https://arxiv.org/pdf/2604.14572
• Github: https://github.com/dukesun99/Corpus2Skill

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✨RadAgent: A tool-using AI agent for stepwise interpretation of chest computed tomography

📝 Summary:
RadAgent, a tool-using AI agent, enhances chest CT report generation through interpretable step-by-step reasoning traces that improve clinical accuracy, robustness, and faithfulness compared to existi...

🔹 Publication Date: Published on Apr 16

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.15231
• PDF: https://arxiv.org/pdf/2604.15231
• Project Page: https://rad-agent.github.io

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