✨Streaming Structured Inference with Flash-SemiCRF
📝 Summary:
Semi-Markov Conditional Random Fields are enhanced through efficient memory management techniques that enable exact inference on long sequences and large label sets by using on-the-fly computation and...
🔹 Publication Date: Published on Apr 20
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.18780
• PDF: https://arxiv.org/pdf/2604.18780
• Github: https://github.com/biobenkj/flash-semicrf
==================================
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📝 Summary:
Semi-Markov Conditional Random Fields are enhanced through efficient memory management techniques that enable exact inference on long sequences and large label sets by using on-the-fly computation and...
🔹 Publication Date: Published on Apr 20
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.18780
• PDF: https://arxiv.org/pdf/2604.18780
• Github: https://github.com/biobenkj/flash-semicrf
==================================
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✨Benign Fine-Tuning Breaks Safety Alignment in Audio LLMs
📝 Summary:
Audio LLM safety degradation through benign fine-tuning occurs due to proximity to harmful content in embedding space, with vulnerability patterns varying by model architecture and modality. AI-genera...
🔹 Publication Date: Published on Apr 17
🔹 Paper Links:
• arXiv Page: https://arxiv.org/pdf/2604.16659v1
• PDF: https://arxiv.org/pdf/2604.16659
• Project Page: https://huggingface.co/papers?q=projector
==================================
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📝 Summary:
Audio LLM safety degradation through benign fine-tuning occurs due to proximity to harmful content in embedding space, with vulnerability patterns varying by model architecture and modality. AI-genera...
🔹 Publication Date: Published on Apr 17
🔹 Paper Links:
• arXiv Page: https://arxiv.org/pdf/2604.16659v1
• PDF: https://arxiv.org/pdf/2604.16659
• Project Page: https://huggingface.co/papers?q=projector
==================================
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✨COMPASS: COntinual Multilingual PEFT with Adaptive Semantic Sampling
📝 Summary:
COMPASS is a data-centric framework for multilingual LLM adaptation. It uses PEFT with adaptive semantic sampling to train language-specific adapters, prioritizing under-represented semantic clusters. This maximizes positive cross-lingual transfer, outperforming baselines and preventing interfere...
🔹 Publication Date: Published on Apr 22
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.20720
• PDF: https://arxiv.org/pdf/2604.20720
==================================
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#MultilingualLLM #PEFT #NLP #DataCentricAI #MachineLearning
📝 Summary:
COMPASS is a data-centric framework for multilingual LLM adaptation. It uses PEFT with adaptive semantic sampling to train language-specific adapters, prioritizing under-represented semantic clusters. This maximizes positive cross-lingual transfer, outperforming baselines and preventing interfere...
🔹 Publication Date: Published on Apr 22
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.20720
• PDF: https://arxiv.org/pdf/2604.20720
==================================
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✨C-GenReg: Training-Free 3D Point Cloud Registration by Multi-View-Consistent Geometry-to-Image Generation with Probabilistic Modalities Fusion
📝 Summary:
C-GenReg is a training-free 3D point cloud registration framework that uses generative priors and Vision Foundation Models to transfer matching problems to an image domain for improved cross-domain ge...
🔹 Publication Date: Published on Apr 17
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.16680
• PDF: https://arxiv.org/pdf/2604.16680
• Project Page: https://yuvalh9.github.io/CGenReg/
• Github: https://github.com/yuvalH9/CGenReg
==================================
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📝 Summary:
C-GenReg is a training-free 3D point cloud registration framework that uses generative priors and Vision Foundation Models to transfer matching problems to an image domain for improved cross-domain ge...
🔹 Publication Date: Published on Apr 17
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.16680
• PDF: https://arxiv.org/pdf/2604.16680
• Project Page: https://yuvalh9.github.io/CGenReg/
• Github: https://github.com/yuvalH9/CGenReg
==================================
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arXiv.org
C-GenReg: Training-Free 3D Point Cloud Registration by...
We introduce C-GenReg, a training-free framework for 3D point cloud registration that leverages the complementary strengths of world-scale generative priors and registration-oriented Vision...
✨Expert Upcycling: Shifting the Compute-Efficient Frontier of Mixture-of-Experts
📝 Summary:
Expert upcycling expands Mixture-of-Experts capacity during continued pre-training by duplicating experts and extending routers while maintaining fixed inference cost, achieving better training effici...
🔹 Publication Date: Published on Apr 21
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.19835
• PDF: https://arxiv.org/pdf/2604.19835
• Github: https://github.com/amazon-science/expert-upcycling
==================================
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📝 Summary:
Expert upcycling expands Mixture-of-Experts capacity during continued pre-training by duplicating experts and extending routers while maintaining fixed inference cost, achieving better training effici...
🔹 Publication Date: Published on Apr 21
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.19835
• PDF: https://arxiv.org/pdf/2604.19835
• Github: https://github.com/amazon-science/expert-upcycling
==================================
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✨Chasing the Public Score: User Pressure and Evaluation Exploitation in Coding Agent Workflows
📝 Summary:
Research examines how user pressure in coding agent workflows leads to score manipulation without genuine performance improvement, finding that stronger models exploit more frequently and that prompts...
🔹 Publication Date: Published on Apr 22
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.20200
• PDF: https://arxiv.org/pdf/2604.20200
• Project Page: https://ucsc-vlaa.github.io/AgentPressureBench
• Github: https://github.com/ucsc-vlaa/AgentPressureBench
==================================
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📝 Summary:
Research examines how user pressure in coding agent workflows leads to score manipulation without genuine performance improvement, finding that stronger models exploit more frequently and that prompts...
🔹 Publication Date: Published on Apr 22
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.20200
• PDF: https://arxiv.org/pdf/2604.20200
• Project Page: https://ucsc-vlaa.github.io/AgentPressureBench
• Github: https://github.com/ucsc-vlaa/AgentPressureBench
==================================
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arXiv.org
Chasing the Public Score: User Pressure and Evaluation...
Frontier coding agents are increasingly used in workflows where users supervise progress primarily through repeated improvement of a public score, namely the reported score on a public evaluation...
✨Test-Time Adaptation for EEG Foundation Models: A Systematic Study under Real-World Distribution Shifts
📝 Summary:
Test-time adaptation for EEG foundation models shows inconsistent performance across distribution shifts. Optimization-free methods are more stable and reliable, while gradient-based approaches often degrade performance. This highlights limitations and the need for domain-specific EEG adaptation ...
🔹 Publication Date: Published on Apr 18
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.16926
• PDF: https://arxiv.org/pdf/2604.16926
==================================
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📝 Summary:
Test-time adaptation for EEG foundation models shows inconsistent performance across distribution shifts. Optimization-free methods are more stable and reliable, while gradient-based approaches often degrade performance. This highlights limitations and the need for domain-specific EEG adaptation ...
🔹 Publication Date: Published on Apr 18
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.16926
• PDF: https://arxiv.org/pdf/2604.16926
==================================
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arXiv.org
Test-Time Adaptation for EEG Foundation Models: A Systematic Study...
Electroencephalography (EEG) foundation models have shown strong potential for learning generalizable representations from large-scale neural data, yet their clinical deployment is hindered by...
✨UniT: Toward a Unified Physical Language for Human-to-Humanoid Policy Learning and World Modeling
📝 Summary:
UniT creates a unified physical language for human-to-humanoid transfer using cross-reconstruction and shared latent spaces. This approach effectively bridges kinematic differences, enabling scalable policy learning and world modeling with human data for humanoid robots.
🔹 Publication Date: Published on Apr 21
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.19734
• PDF: https://arxiv.org/pdf/2604.19734
• Project Page: https://xpeng-robotics.github.io/unit/
• Github: https://github.com/xpeng-robotics/UniT
==================================
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📝 Summary:
UniT creates a unified physical language for human-to-humanoid transfer using cross-reconstruction and shared latent spaces. This approach effectively bridges kinematic differences, enabling scalable policy learning and world modeling with human data for humanoid robots.
🔹 Publication Date: Published on Apr 21
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.19734
• PDF: https://arxiv.org/pdf/2604.19734
• Project Page: https://xpeng-robotics.github.io/unit/
• Github: https://github.com/xpeng-robotics/UniT
==================================
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arXiv.org
UniT: Toward a Unified Physical Language for Human-to-Humanoid...
Scaling humanoid foundation models is bottlenecked by the scarcity of robotic data. While massive egocentric human data offers a scalable alternative, bridging the cross-embodiment chasm remains a...
✨StyleID: A Perception-Aware Dataset and Metric for Stylization-Agnostic Facial Identity Recognition
📝 Summary:
StyleID presents a human perception-aware dataset and evaluation framework for facial identity preservation under stylization, featuring two datasets derived from psychometric experiments and calibrat...
🔹 Publication Date: Published on Apr 23
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.21689
• PDF: https://arxiv.org/pdf/2604.21689
• Project Page: https://kwanyun.github.io/StyleID_page/
• Github: https://github.com/kwanyun/StyleID
🔹 Models citing this paper:
• https://huggingface.co/kwanY/styleid
==================================
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📝 Summary:
StyleID presents a human perception-aware dataset and evaluation framework for facial identity preservation under stylization, featuring two datasets derived from psychometric experiments and calibrat...
🔹 Publication Date: Published on Apr 23
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.21689
• PDF: https://arxiv.org/pdf/2604.21689
• Project Page: https://kwanyun.github.io/StyleID_page/
• Github: https://github.com/kwanyun/StyleID
🔹 Models citing this paper:
• https://huggingface.co/kwanY/styleid
==================================
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arXiv.org
StyleID: A Perception-Aware Dataset and Metric for...
Creative face stylization aims to render portraits in diverse visual idioms such as cartoons, sketches, and paintings while retaining recognizable identity. However, current identity encoders,...
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✨Seeing Fast and Slow: Learning the Flow of Time in Videos
📝 Summary:
Video speed manipulation and perception models are developed through self-supervised temporal reasoning, enabling speed detection, slow-motion video generation, and temporal super-resolution from in-t...
🔹 Publication Date: Published on Apr 23
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.21931
• PDF: https://arxiv.org/pdf/2604.21931
• Project Page: https://seeing-fast-and-slow.github.io/
==================================
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📝 Summary:
Video speed manipulation and perception models are developed through self-supervised temporal reasoning, enabling speed detection, slow-motion video generation, and temporal super-resolution from in-t...
🔹 Publication Date: Published on Apr 23
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.21931
• PDF: https://arxiv.org/pdf/2604.21931
• Project Page: https://seeing-fast-and-slow.github.io/
==================================
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✨WorldMark: A Unified Benchmark Suite for Interactive Video World Models
📝 Summary:
WorldMark establishes a standardized benchmark for evaluating interactive video generation models with unified controls, identical scenarios, and comprehensive evaluation metrics across multiple model...
🔹 Publication Date: Published on Apr 23
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.21686
• PDF: https://arxiv.org/pdf/2604.21686
• Project Page: https://alaya-studio.github.io/WorldMark/
==================================
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📝 Summary:
WorldMark establishes a standardized benchmark for evaluating interactive video generation models with unified controls, identical scenarios, and comprehensive evaluation metrics across multiple model...
🔹 Publication Date: Published on Apr 23
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.21686
• PDF: https://arxiv.org/pdf/2604.21686
• Project Page: https://alaya-studio.github.io/WorldMark/
==================================
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✨Context Unrolling in Omni Models
📝 Summary:
Omni is a unified multimodal model trained on diverse data types that enables context unrolling for improved reasoning across heterogeneous modalities. AI-generated summary We present Omni, a unified ...
🔹 Publication Date: Published on Apr 23
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.21921
• PDF: https://arxiv.org/pdf/2604.21921
• Project Page: https://omni-model.com/
==================================
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📝 Summary:
Omni is a unified multimodal model trained on diverse data types that enables context unrolling for improved reasoning across heterogeneous modalities. AI-generated summary We present Omni, a unified ...
🔹 Publication Date: Published on Apr 23
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.21921
• PDF: https://arxiv.org/pdf/2604.21921
• Project Page: https://omni-model.com/
==================================
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arXiv.org
Context Unrolling in Omni Models
We present Omni, a unified multimodal model natively trained on diverse modalities, including text, images, videos, 3D geometry, and hidden representations. We find that such training enables...
✨UniGenDet: A Unified Generative-Discriminative Framework for Co-Evolutionary Image Generation and Generated Image Detection
📝 Summary:
A unified generative-discriminative framework is proposed that enables co-evolutionary image generation and detection through symbiotic attention mechanisms and unified fine-tuning algorithms. AI-gene...
🔹 Publication Date: Published on Apr 23
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.21904
• PDF: https://arxiv.org/pdf/2604.21904
• Project Page: https://ivg-yanranzhang.github.io/UniGenDet/
• Github: https://github.com/Zhangyr2022/UniGenDet
==================================
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📝 Summary:
A unified generative-discriminative framework is proposed that enables co-evolutionary image generation and detection through symbiotic attention mechanisms and unified fine-tuning algorithms. AI-gene...
🔹 Publication Date: Published on Apr 23
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.21904
• PDF: https://arxiv.org/pdf/2604.21904
• Project Page: https://ivg-yanranzhang.github.io/UniGenDet/
• Github: https://github.com/Zhangyr2022/UniGenDet
==================================
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arXiv.org
UniGenDet: A Unified Generative-Discriminative Framework for...
In recent years, significant progress has been made in both image generation and generated image detection. Despite their rapid, yet largely independent, development, these two fields have evolved...
✨VLAA-GUI: Knowing When to Stop, Recover, and Search, A Modular Framework for GUI Automation
📝 Summary:
VLAA-GUI is a modular GUI agent framework that addresses early stopping and repetitive loop issues through integrated components for verification, loop breaking, and search capabilities. AI-generated ...
🔹 Publication Date: Published on Apr 23
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.21375
• PDF: https://arxiv.org/pdf/2604.21375
• Project Page: https://ucsc-vlaa.github.io/VLAA-GUI/
• Github: https://github.com/UCSC-VLAA/VLAA-GUI
==================================
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📝 Summary:
VLAA-GUI is a modular GUI agent framework that addresses early stopping and repetitive loop issues through integrated components for verification, loop breaking, and search capabilities. AI-generated ...
🔹 Publication Date: Published on Apr 23
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.21375
• PDF: https://arxiv.org/pdf/2604.21375
• Project Page: https://ucsc-vlaa.github.io/VLAA-GUI/
• Github: https://github.com/UCSC-VLAA/VLAA-GUI
==================================
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arXiv.org
VLAA-GUI: Knowing When to Stop, Recover, and Search, A Modular...
Autonomous GUI agents face two fundamental challenges: early stopping, where agents prematurely declare success without verifiable evidence, and repetitive loops, where agents cycle through the...
✨TingIS: Real-time Risk Event Discovery from Noisy Customer Incidents at Enterprise Scale
📝 Summary:
TingIS is an enterprise-grade incident discovery system that uses multi-stage event linking with LLMs, cascaded routing, and noise reduction to efficiently identify critical issues from high-volume, n...
🔹 Publication Date: Published on Apr 23
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.21889
• PDF: https://arxiv.org/pdf/2604.21889
==================================
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📝 Summary:
TingIS is an enterprise-grade incident discovery system that uses multi-stage event linking with LLMs, cascaded routing, and noise reduction to efficiently identify critical issues from high-volume, n...
🔹 Publication Date: Published on Apr 23
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.21889
• PDF: https://arxiv.org/pdf/2604.21889
==================================
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arXiv.org
TingIS: Real-time Risk Event Discovery from Noisy Customer...
Real-time detection and mitigation of technical anomalies are critical for large-scale cloud-native services, where even minutes of downtime can result in massive financial losses and diminished...
✨Trust but Verify: Introducing DAVinCI -- A Framework for Dual Attribution and Verification in Claim Inference for Language Models
📝 Summary:
DAVinCI is a dual attribution and verification framework that enhances factual reliability and interpretability of large language models by attributing claims to internal components and external sourc...
🔹 Publication Date: Published on Apr 23
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.21193
• PDF: https://arxiv.org/pdf/2604.21193
• Github: https://github.com/vr25/davinci
==================================
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📝 Summary:
DAVinCI is a dual attribution and verification framework that enhances factual reliability and interpretability of large language models by attributing claims to internal components and external sourc...
🔹 Publication Date: Published on Apr 23
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.21193
• PDF: https://arxiv.org/pdf/2604.21193
• Github: https://github.com/vr25/davinci
==================================
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arXiv.org
Trust but Verify: Introducing DAVinCI -- A Framework for Dual...
Large Language Models (LLMs) have demonstrated remarkable fluency and versatility across a wide range of NLP tasks, yet they remain prone to factual inaccuracies and hallucinations. This...
✨Explainable Disentangled Representation Learning for Generalizable Authorship Attribution in the Era of Generative AI
📝 Summary:
A novel variational autoencoder framework with supervised contrastive learning and discriminative disentanglement achieves superior performance in authorship attribution and AI-generated text detectio...
🔹 Publication Date: Published on Apr 23
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.21300
• PDF: https://arxiv.org/pdf/2604.21300
• Github: https://github.com/hieum98/avae
==================================
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📝 Summary:
A novel variational autoencoder framework with supervised contrastive learning and discriminative disentanglement achieves superior performance in authorship attribution and AI-generated text detectio...
🔹 Publication Date: Published on Apr 23
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.21300
• PDF: https://arxiv.org/pdf/2604.21300
• Github: https://github.com/hieum98/avae
==================================
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arXiv.org
Explainable Disentangled Representation Learning for Generalizable...
Learning robust representations of authorial style is crucial for authorship attribution and AI-generated text detection. However, existing methods often struggle with content-style entanglement,...
✨Co-Evolving LLM Decision and Skill Bank Agents for Long-Horizon Tasks
📝 Summary:
A co-evolution framework enables large language models to discover, retain, and reuse structured skills across episodes in long-horizon interactive environments through a learnable skill bank and skil...
🔹 Publication Date: Published on Apr 22
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.20987
• PDF: https://arxiv.org/pdf/2604.20987
• Project Page: https://wuxiyang1996.github.io/COSPLAY_page/
• Github: https://wuxiyang1996.github.io/COSPLAY_page/
🔹 Models citing this paper:
• https://huggingface.co/IntelligenceLab/COS-PLAY
✨ Datasets citing this paper:
• https://huggingface.co/datasets/IntelligenceLab/Cos-Play-Cold-Start
==================================
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📝 Summary:
A co-evolution framework enables large language models to discover, retain, and reuse structured skills across episodes in long-horizon interactive environments through a learnable skill bank and skil...
🔹 Publication Date: Published on Apr 22
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.20987
• PDF: https://arxiv.org/pdf/2604.20987
• Project Page: https://wuxiyang1996.github.io/COSPLAY_page/
• Github: https://wuxiyang1996.github.io/COSPLAY_page/
🔹 Models citing this paper:
• https://huggingface.co/IntelligenceLab/COS-PLAY
✨ Datasets citing this paper:
• https://huggingface.co/datasets/IntelligenceLab/Cos-Play-Cold-Start
==================================
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arXiv.org
Co-Evolving LLM Decision and Skill Bank Agents for Long-Horizon Tasks
Long horizon interactive environments are a testbed for evaluating agents skill usage abilities. These environments demand multi step reasoning, the chaining of multiple skills over many...
✨Hybrid Policy Distillation for LLMs
📝 Summary:
Hybrid Policy Distillation combines forward and reverse KL divergence approaches to improve knowledge distillation stability and efficiency across different model sizes and tasks. AI-generated summary...
🔹 Publication Date: Published on Apr 22
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.20244
• PDF: https://arxiv.org/pdf/2604.20244
• Github: https://github.com/zwhong714/Hybrid-Policy-Distillation
🔹 Models citing this paper:
• https://huggingface.co/wh-zhu/Qwen2.5-7B-PSFT-RL-DAPO-90
• https://huggingface.co/wh-zhu/qwen2.5-1.5B-longcot-reasoning-HPD
==================================
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📝 Summary:
Hybrid Policy Distillation combines forward and reverse KL divergence approaches to improve knowledge distillation stability and efficiency across different model sizes and tasks. AI-generated summary...
🔹 Publication Date: Published on Apr 22
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.20244
• PDF: https://arxiv.org/pdf/2604.20244
• Github: https://github.com/zwhong714/Hybrid-Policy-Distillation
🔹 Models citing this paper:
• https://huggingface.co/wh-zhu/Qwen2.5-7B-PSFT-RL-DAPO-90
• https://huggingface.co/wh-zhu/qwen2.5-1.5B-longcot-reasoning-HPD
==================================
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arXiv.org
Hybrid Policy Distillation for LLMs
Knowledge distillation (KD) is a powerful paradigm for compressing large language models (LLMs), whose effectiveness depends on intertwined choices of divergence direction, optimization strategy,...
✨WebGen-R1: Incentivizing Large Language Models to Generate Functional and Aesthetic Websites with Reinforcement Learning
📝 Summary:
WebGen-R1 is a reinforcement learning framework enabling small language models to generate functional and aesthetically pleasing multi-page websites. It uses structured generation and a novel cascaded multimodal reward for structural integrity, functional feedback, and aesthetic supervision. WebG...
🔹 Publication Date: Published on Apr 22
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.20398
• PDF: https://arxiv.org/pdf/2604.20398
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For more data science resources:
✓ https://t.iss.one/DataScienceT
#ReinforcementLearning #LLMs #WebsiteGeneration #AI #WebDevelopment
📝 Summary:
WebGen-R1 is a reinforcement learning framework enabling small language models to generate functional and aesthetically pleasing multi-page websites. It uses structured generation and a novel cascaded multimodal reward for structural integrity, functional feedback, and aesthetic supervision. WebG...
🔹 Publication Date: Published on Apr 22
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.20398
• PDF: https://arxiv.org/pdf/2604.20398
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#ReinforcementLearning #LLMs #WebsiteGeneration #AI #WebDevelopment
arXiv.org
WebGen-R1: Incentivizing Large Language Models to Generate...
While Large Language Models (LLMs) excel at function-level code generation, project-level tasks such as generating functional and visually aesthetic multi-page websites remain highly challenging....
ML Research Hub
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