✨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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#AI #DataScience #MachineLearning #HuggingFace #Research

✨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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#AI #DataScience #MachineLearning #HuggingFace #Research

✨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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#DiffusionModels #ReinforcementLearning #GenerativeAI #TextToImage #DeepLearning

✨Mitigating Multimodal Hallucination via Phase-wise Self-reward

📝 Summary:
PSRD is a new self-rewarding framework that mitigates vision hallucination in LVLMs dynamically during inference. It uses phase-wise self-reward signals and a lightweight reward model for efficient online correction, significantly reducing hallucination rates.

🔹 Publication Date: Published on Apr 20

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.17982
• PDF: https://arxiv.org/pdf/2604.17982

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✨The Cognitive Penalty: Ablating System 1 and System 2 Reasoning in Edge-Native SLMs for Decentralized Consensus

📝 Summary:
Research on SLMs in decentralized organizations finds that System 1 reasoning is superior for robust adversarial governance. System 2 inference-time compute introduces catastrophic instability, high latency, and vulnerabilities, making intuitive reasoning more effective.

🔹 Publication Date: Published on Apr 18

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.16913
• PDF: https://arxiv.org/pdf/2604.16913
• Github: https://github.com/smarizvi110/sentinel-bench

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#SLMs #DecentralizedAI #CognitiveAI #AIGovernance #Blockchain

✨Chain-of-Thought Degrades Visual Spatial Reasoning Capabilities of Multimodal LLMs

📝 Summary:
Chain-of-Thought prompting in multimodal reasoning models degrades performance in visual spatial reasoning due to shortcut learning and hallucination of visual details from text alone. AI-generated su...

🔹 Publication Date: Published on Apr 17

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.16060
• PDF: https://arxiv.org/pdf/2604.16060

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✨Mind's Eye: A Benchmark of Visual Abstraction, Transformation and Composition for Multimodal LLMs

📝 Summary:
Multimodal large language models demonstrate significant limitations in visuospatial reasoning tasks compared to human performance, revealing deficiencies in visual attention, perceptual manipulation,...

🔹 Publication Date: Published on Apr 17

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.16054
• PDF: https://arxiv.org/pdf/2604.16054

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

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✨ShadowPEFT: Shadow Network for Parameter-Efficient Fine-Tuning

📝 Summary:
ShadowPEFT is a new parameter-efficient fine-tuning framework that uses a depth-shared shadow module for layer-level refinement. This shifts adaptation from distributed weight perturbations to a shared layer-space process, matching or outperforming LoRA with reduced overhead and increased flexibi...

🔹 Publication Date: Published on Apr 21

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.19254
• PDF: https://arxiv.org/pdf/2604.19254
• Project Page: https://github.com/ShadowLLM/shadow-peft
• Github: https://github.com/ShadowLLM/shadow-peft

🔹 Models citing this paper:
• https://huggingface.co/shadow-llm/Qwen3-4B-GSM8k-Shadow
• https://huggingface.co/shadow-llm/Qwen3-4B-SquadV2-Shadow
• https://huggingface.co/shadow-llm/Qwen3-4B-MMLU-Shadow

✨ Datasets citing this paper:
• https://huggingface.co/datasets/shadow-llm/robot-dog-skills

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#PEFT #FineTuning #MachineLearning #AI #LLMs

✨HP-Edit: A Human-Preference Post-Training Framework for Image Editing

📝 Summary:
A post-training framework called HP-Edit is introduced to align image editing models with human preferences using a novel automatic evaluator and a real-world dataset, improving editing quality throug...

🔹 Publication Date: Published on Apr 21

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.19406
• PDF: https://arxiv.org/pdf/2604.19406

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

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✨Understanding and Enforcing Weight Disentanglement in Task Arithmetic

📝 Summary:
Task arithmetic lacks theoretical explanation for its success, but the proposed OrthoReg method addresses this by promoting weight disentanglement through enforced orthogonality in weight updates duri...

🔹 Publication Date: Published on Apr 18

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.17078
• PDF: https://arxiv.org/pdf/2604.17078
• Github: https://github.com/RL-MIND/OrthoReg

🔹 Models citing this paper:
• https://huggingface.co/RL-MIND/OrthoReg_checkpoints
• https://huggingface.co/RL-MIND/OrthoReg

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✨AJ-Bench: Benchmarking Agent-as-a-Judge for Environment-Aware Evaluation

📝 Summary:
Agent-as-a-Judge benchmark evaluates automated verification capabilities across multiple domains with comprehensive task assessment. AI-generated summary As reinforcement learning continues to scale t...

🔹 Publication Date: Published on Apr 20

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.18240
• PDF: https://arxiv.org/pdf/2604.18240
• Project Page: https://aj-bench.github.io/
• Github: https://github.com/aj-bench/AJ-Bench

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✨Accurate and scalable exchange-correlation with deep learning

📝 Summary:
A deep learning approach to density functional theory achieves higher accuracy than traditional methods while maintaining computational efficiency by learning electronic structure representations dire...

🔹 Publication Date: Published on Apr 21

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2506.14665
• PDF: https://arxiv.org/pdf/2506.14665
• Project Page: https://aka.ms/dft
• Github: https://github.com/microsoft/skala

🔹 Models citing this paper:
• https://huggingface.co/microsoft/skala-1.0
• https://huggingface.co/microsoft/skala-1.1

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

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✨What Makes an LLM a Good Optimizer? A Trajectory Analysis of LLM-Guided Evolutionary Search

📝 Summary:
LLM-guided evolutionary search shows that optimization success depends on search trajectory characteristics rather than initial problem-solving ability alone, with strong optimizers refining locally w...

🔹 Publication Date: Published on Apr 21

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.19440
• PDF: https://arxiv.org/pdf/2604.19440
• Project Page: https://xinhao-zhang.github.io/traj_evo_search/
• Github: https://github.com/XINHAO-ZHANG/LLMEvo_Eval

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#LLM #Optimization #EvolutionaryAlgorithms #AI #MachineLearning