ML Research Hub pinned «🔥 NEW YEAR 2026 – PREMIUM nature papers: 400$ Q1 and Q2 papers 300$ Q3 and Q4 papers 200$ Doctoral thesis (complete) 500$ M.S thesis 300$ paper simulation 150$ Contact me: @Omidyzd62»
✨LoPA: Scaling dLLM Inference via Lookahead Parallel Decoding
📝 Summary:
LoPA is a training-free algorithm enhancing dLLM inference parallelism by optimizing Token Filling Order. It achieves 10.1 tokens per forward pass for D2F-Dream, significantly boosting efficiency while maintaining performance. A multi-GPU system further accelerates throughput to 1073.9 tokens per...
🔹 Publication Date: Published on Dec 18
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.16229
• PDF: https://arxiv.org/pdf/2512.16229
• Github: https://zhijie-group.github.io/blogs/lopa
==================================
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#LLM #AI #Inference #ParallelDecoding #Performance
📝 Summary:
LoPA is a training-free algorithm enhancing dLLM inference parallelism by optimizing Token Filling Order. It achieves 10.1 tokens per forward pass for D2F-Dream, significantly boosting efficiency while maintaining performance. A multi-GPU system further accelerates throughput to 1073.9 tokens per...
🔹 Publication Date: Published on Dec 18
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.16229
• PDF: https://arxiv.org/pdf/2512.16229
• Github: https://zhijie-group.github.io/blogs/lopa
==================================
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#LLM #AI #Inference #ParallelDecoding #Performance
✨Does It Tie Out? Towards Autonomous Legal Agents in Venture Capital
📝 Summary:
Automating legal capitalization tie-out in venture capital is difficult for current AI. It requires multi-document reasoning and strict evidence traceability. This paper proposes a world model architecture for automation, advancing applied legal intelligence.
🔹 Publication Date: Published on Dec 21
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.18658
• PDF: https://arxiv.org/pdf/2512.18658
==================================
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#LegalAI #VentureCapital #AIAutomation #LegalTech #ArtificialIntelligence
📝 Summary:
Automating legal capitalization tie-out in venture capital is difficult for current AI. It requires multi-document reasoning and strict evidence traceability. This paper proposes a world model architecture for automation, advancing applied legal intelligence.
🔹 Publication Date: Published on Dec 21
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.18658
• PDF: https://arxiv.org/pdf/2512.18658
==================================
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#LegalAI #VentureCapital #AIAutomation #LegalTech #ArtificialIntelligence
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✨MatSpray: Fusing 2D Material World Knowledge on 3D Geometry
📝 Summary:
MatSpray integrates 2D PBR materials from diffusion models onto 3D Gaussian Splatting geometry. Using projection and neural refinement, it enables accurate relighting and photorealistic rendering from reconstructed scenes. This boosts asset creation efficiency.
🔹 Publication Date: Published on Dec 20
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.18314
• PDF: https://arxiv.org/pdf/2512.18314
• Project Page: https://matspray.jdihlmann.com/
• Github: https://github.com/cgtuebingen/MatSpray
==================================
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#MatSpray #GaussianSplatting #DiffusionModels #3DRendering #ComputerGraphics
📝 Summary:
MatSpray integrates 2D PBR materials from diffusion models onto 3D Gaussian Splatting geometry. Using projection and neural refinement, it enables accurate relighting and photorealistic rendering from reconstructed scenes. This boosts asset creation efficiency.
🔹 Publication Date: Published on Dec 20
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.18314
• PDF: https://arxiv.org/pdf/2512.18314
• Project Page: https://matspray.jdihlmann.com/
• Github: https://github.com/cgtuebingen/MatSpray
==================================
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#MatSpray #GaussianSplatting #DiffusionModels #3DRendering #ComputerGraphics
❤1
nature papers: 400$
Q1 and Q2 papers 300$
Q3 and Q4 papers 200$
Doctoral thesis (complete) 500$
M.S thesis 300$
paper simulation 150$
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✨CASA: Cross-Attention via Self-Attention for Efficient Vision-Language Fusion
📝 Summary:
CASA enhances cross-attention for vision-language models by adding local text-to-text interaction. This approach substantially reduces the performance gap with costly token insertion methods on detailed visual tasks. CASA maintains efficiency and scalability for long-context multimodal applicatio...
🔹 Publication Date: Published on Dec 22
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.19535
• PDF: https://arxiv.org/pdf/2512.19535
• Project Page: https://kyutai.org/casa
• Github: https://github.com/kyutai-labs/casa
🔹 Models citing this paper:
• https://huggingface.co/kyutai/CASA-Helium1-VL-2B
✨ Spaces citing this paper:
• https://huggingface.co/spaces/kyutai/casa-samples
==================================
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#VisionLanguage #MultimodalAI #AttentionMechanisms #EfficientAI #DeepLearning
📝 Summary:
CASA enhances cross-attention for vision-language models by adding local text-to-text interaction. This approach substantially reduces the performance gap with costly token insertion methods on detailed visual tasks. CASA maintains efficiency and scalability for long-context multimodal applicatio...
🔹 Publication Date: Published on Dec 22
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.19535
• PDF: https://arxiv.org/pdf/2512.19535
• Project Page: https://kyutai.org/casa
• Github: https://github.com/kyutai-labs/casa
🔹 Models citing this paper:
• https://huggingface.co/kyutai/CASA-Helium1-VL-2B
✨ Spaces citing this paper:
• https://huggingface.co/spaces/kyutai/casa-samples
==================================
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#VisionLanguage #MultimodalAI #AttentionMechanisms #EfficientAI #DeepLearning
❤3
✨Over++: Generative Video Compositing for Layer Interaction Effects
📝 Summary:
Over++ introduces augmented compositing, a framework that generates realistic, text-prompted environmental effects for videos. It synthesizes effects like shadows onto video layers while preserving the original scene, outperforming prior methods without dense annotations.
🔹 Publication Date: Published on Dec 22
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.19661
• PDF: https://arxiv.org/pdf/2512.19661
• Project Page: https://overplusplus.github.io/
==================================
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#GenerativeAI #VideoCompositing #VFX #ComputerGraphics #AIResearch
📝 Summary:
Over++ introduces augmented compositing, a framework that generates realistic, text-prompted environmental effects for videos. It synthesizes effects like shadows onto video layers while preserving the original scene, outperforming prior methods without dense annotations.
🔹 Publication Date: Published on Dec 22
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.19661
• PDF: https://arxiv.org/pdf/2512.19661
• Project Page: https://overplusplus.github.io/
==================================
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#GenerativeAI #VideoCompositing #VFX #ComputerGraphics #AIResearch
👍1
✨SecureCode v2.0: A Production-Grade Dataset for Training Security-Aware Code Generation Models
📝 Summary:
SecureCode v2.0 is a production-grade dataset of 1215 security-focused coding examples. It trains AI models to generate secure code by providing real-incident examples with vulnerable and secure implementations, attacks, defense, and operational security context across 11 languages, using a conve...
🔹 Publication Date: Published on Dec 20
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.18542
• PDF: https://arxiv.org/pdf/2512.18542
• Project Page: https://perfecxion.ai/
• Github: https://github.com/scthornton/securecode-v2
==================================
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#Cybersecurity #CodeSecurity #AI #CodeGeneration #Dataset
📝 Summary:
SecureCode v2.0 is a production-grade dataset of 1215 security-focused coding examples. It trains AI models to generate secure code by providing real-incident examples with vulnerable and secure implementations, attacks, defense, and operational security context across 11 languages, using a conve...
🔹 Publication Date: Published on Dec 20
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.18542
• PDF: https://arxiv.org/pdf/2512.18542
• Project Page: https://perfecxion.ai/
• Github: https://github.com/scthornton/securecode-v2
==================================
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#Cybersecurity #CodeSecurity #AI #CodeGeneration #Dataset
✨Step-DeepResearch Technical Report
📝 Summary:
Step-DeepResearch is an end-to-end agent for deep research, using a data synthesis strategy and progressive training. It achieves expert-level capabilities, outperforming existing models and rivaling SOTA closed-source models with cost-efficiency. It also introduces ADR-Bench for realistic Chines...
🔹 Publication Date: Published on Dec 23
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.20491
• PDF: https://arxiv.org/pdf/2512.20491
==================================
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#AI #MachineLearning #DeepResearch #AIagent #SOTA
📝 Summary:
Step-DeepResearch is an end-to-end agent for deep research, using a data synthesis strategy and progressive training. It achieves expert-level capabilities, outperforming existing models and rivaling SOTA closed-source models with cost-efficiency. It also introduces ADR-Bench for realistic Chines...
🔹 Publication Date: Published on Dec 23
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.20491
• PDF: https://arxiv.org/pdf/2512.20491
==================================
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#AI #MachineLearning #DeepResearch #AIagent #SOTA
✨Bottom-up Policy Optimization: Your Language Model Policy Secretly Contains Internal Policies
📝 Summary:
This paper decomposes LLM policies into internal layer and modular policies, revealing distinct reasoning patterns across layers. It finds early layers explore and top layers refine. Motivated by this, Bottom-up Policy Optimization BuPO is proposed to optimize internal layer policies for superior...
🔹 Publication Date: Published on Dec 22
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.19673
• PDF: https://arxiv.org/pdf/2512.19673
==================================
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#LLM #PolicyOptimization #DeepLearning #AIResearch #NLP
📝 Summary:
This paper decomposes LLM policies into internal layer and modular policies, revealing distinct reasoning patterns across layers. It finds early layers explore and top layers refine. Motivated by this, Bottom-up Policy Optimization BuPO is proposed to optimize internal layer policies for superior...
🔹 Publication Date: Published on Dec 22
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.19673
• PDF: https://arxiv.org/pdf/2512.19673
==================================
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#LLM #PolicyOptimization #DeepLearning #AIResearch #NLP
✨SAM Audio: Segment Anything in Audio
📝 Summary:
SAM Audio is a foundation model for general audio separation. It unifies text visual and temporal span prompts achieving state-of-the-art performance across diverse audio types. It also introduces a new real-world separation benchmark.
🔹 Publication Date: Published on Dec 19
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.18099
• PDF: https://arxiv.org/pdf/2512.18099
• Project Page: https://ai.meta.com/samaudio/
• Github: https://github.com/facebookresearch/sam-audio
🔹 Models citing this paper:
• https://huggingface.co/facebook/sam-audio-large
• https://huggingface.co/facebook/sam-audio-small
• https://huggingface.co/facebook/sam-audio-base
✨ Spaces citing this paper:
• https://huggingface.co/spaces/lpeterl/sam-audio-webui
• https://huggingface.co/spaces/Arrcttacsrks/SAM-Audio-Demo
• https://huggingface.co/spaces/chippie1/SAM-Audio-Demo
==================================
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#AudioSeparation #FoundationModels #AI #DeepLearning #SAMAudio
📝 Summary:
SAM Audio is a foundation model for general audio separation. It unifies text visual and temporal span prompts achieving state-of-the-art performance across diverse audio types. It also introduces a new real-world separation benchmark.
🔹 Publication Date: Published on Dec 19
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.18099
• PDF: https://arxiv.org/pdf/2512.18099
• Project Page: https://ai.meta.com/samaudio/
• Github: https://github.com/facebookresearch/sam-audio
🔹 Models citing this paper:
• https://huggingface.co/facebook/sam-audio-large
• https://huggingface.co/facebook/sam-audio-small
• https://huggingface.co/facebook/sam-audio-base
✨ Spaces citing this paper:
• https://huggingface.co/spaces/lpeterl/sam-audio-webui
• https://huggingface.co/spaces/Arrcttacsrks/SAM-Audio-Demo
• https://huggingface.co/spaces/chippie1/SAM-Audio-Demo
==================================
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#AudioSeparation #FoundationModels #AI #DeepLearning #SAMAudio
arXiv.org
SAM Audio: Segment Anything in Audio
General audio source separation is a key capability for multimodal AI systems that can perceive and reason about sound. Despite substantial progress in recent years, existing separation models are...
✨QuantiPhy: A Quantitative Benchmark Evaluating Physical Reasoning Abilities of Vision-Language Models
📝 Summary:
QuantiPhy is a benchmark that quantitatively assesses state-of-the-art vision perception models' ability to reason about physical properties such as size, velocity, and acceleration from video observa...
🔹 Publication Date: Published on Dec 22
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.19526
• PDF: https://arxiv.org/pdf/2512.19526
✨ Datasets citing this paper:
• https://huggingface.co/datasets/PaulineLi/QuantiPhy-validation
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
QuantiPhy is a benchmark that quantitatively assesses state-of-the-art vision perception models' ability to reason about physical properties such as size, velocity, and acceleration from video observa...
🔹 Publication Date: Published on Dec 22
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.19526
• PDF: https://arxiv.org/pdf/2512.19526
✨ Datasets citing this paper:
• https://huggingface.co/datasets/PaulineLi/QuantiPhy-validation
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
✨GLM-4.5: Agentic, Reasoning, and Coding (ARC) Foundation Models
📝 Summary:
GLM-4.5, a Mixture-of-Experts large language model with 355B parameters, achieves strong performance across agentic, reasoning, and coding tasks using multi-stage training and reinforcement learning. ...
🔹 Publication Date: Published on Aug 8
🔹 Paper Links:
• arXiv Page: https://arxivlens.com/PaperView/Details/glm-4-5-agentic-reasoning-and-coding-arc-foundation-models-126-7b914dd8
• PDF: https://arxiv.org/pdf/2508.06471
• Github: https://github.com/zai-org/GLM-4.5
🔹 Models citing this paper:
• https://huggingface.co/zai-org/GLM-4.5
• https://huggingface.co/zai-org/GLM-4.6
• https://huggingface.co/zai-org/GLM-4.5-Air
✨ Spaces citing this paper:
• https://huggingface.co/spaces/enzostvs/deepsite
• https://huggingface.co/spaces/akhaliq/anycoder
• https://huggingface.co/spaces/hadadxyz/ai
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
GLM-4.5, a Mixture-of-Experts large language model with 355B parameters, achieves strong performance across agentic, reasoning, and coding tasks using multi-stage training and reinforcement learning. ...
🔹 Publication Date: Published on Aug 8
🔹 Paper Links:
• arXiv Page: https://arxivlens.com/PaperView/Details/glm-4-5-agentic-reasoning-and-coding-arc-foundation-models-126-7b914dd8
• PDF: https://arxiv.org/pdf/2508.06471
• Github: https://github.com/zai-org/GLM-4.5
🔹 Models citing this paper:
• https://huggingface.co/zai-org/GLM-4.5
• https://huggingface.co/zai-org/GLM-4.6
• https://huggingface.co/zai-org/GLM-4.5-Air
✨ Spaces citing this paper:
• https://huggingface.co/spaces/enzostvs/deepsite
• https://huggingface.co/spaces/akhaliq/anycoder
• https://huggingface.co/spaces/hadadxyz/ai
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
Arxivlens
GLM-4.5: Agentic, Reasoning, and Coding (ARC) Foundation Models - AI Research Paper Analysis | ArxivLens
AI-powered analysis of 'GLM-4.5: Agentic, Reasoning, and Coding (ARC) Foundation Models'. We present GLM-4.5, an open-source Mixture-of-Experts (MoE) large language
model with 355B total parameters and 32B activated parameters, featuring a
... Explore with…
model with 355B total parameters and 32B activated parameters, featuring a
... Explore with…
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✨SpatialTree: How Spatial Abilities Branch Out in MLLMs
📝 Summary:
SpatialTree introduces a 4-level cognitive hierarchy and benchmark for evaluating MLLM spatial abilities. It reveals distinct skill dependencies and strong cross-level transfer from low to high-level abilities. A novel auto-think strategy consistently enhances performance across all spatial levels.
🔹 Publication Date: Published on Dec 23
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.20617
• PDF: https://arxiv.org/pdf/2512.20617
• Project Page: https://spatialtree.github.io/
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
SpatialTree introduces a 4-level cognitive hierarchy and benchmark for evaluating MLLM spatial abilities. It reveals distinct skill dependencies and strong cross-level transfer from low to high-level abilities. A novel auto-think strategy consistently enhances performance across all spatial levels.
🔹 Publication Date: Published on Dec 23
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.20617
• PDF: https://arxiv.org/pdf/2512.20617
• Project Page: https://spatialtree.github.io/
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
✨SemanticGen: Video Generation in Semantic Space
📝 Summary:
SemanticGen addresses slow convergence and computational costs in video generation by using a two-stage diffusion model approach that first generates semantic features and then VAE latents, leading to...
🔹 Publication Date: Published on Dec 23
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.20619
• PDF: https://arxiv.org/pdf/2512.20619
• Project Page: https://jianhongbai.github.io/SemanticGen/
• Github: https://jianhongbai.github.io/SemanticGen/
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
SemanticGen addresses slow convergence and computational costs in video generation by using a two-stage diffusion model approach that first generates semantic features and then VAE latents, leading to...
🔹 Publication Date: Published on Dec 23
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.20619
• PDF: https://arxiv.org/pdf/2512.20619
• Project Page: https://jianhongbai.github.io/SemanticGen/
• Github: https://jianhongbai.github.io/SemanticGen/
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
✨Reinforcement Learning for Self-Improving Agent with Skill Library
📝 Summary:
A novel RL framework, SAGE, enhances LLM-based agents' self-improvement capabilities by systematically incorporating skills from a skill library, leading to better performance and efficiency in new en...
🔹 Publication Date: Published on Dec 18
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.17102
• PDF: https://arxiv.org/pdf/2512.17102
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
A novel RL framework, SAGE, enhances LLM-based agents' self-improvement capabilities by systematically incorporating skills from a skill library, leading to better performance and efficiency in new en...
🔹 Publication Date: Published on Dec 18
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.17102
• PDF: https://arxiv.org/pdf/2512.17102
==================================
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✨Active Intelligence in Video Avatars via Closed-loop World Modeling
📝 Summary:
Video avatars currently lack agency for autonomous goal pursuit. ORCA introduces a framework for active intelligence, using a closed-loop Observe-Think-Act-Reflect cycle and a dual-system architecture for strategic reasoning and action. It enables robust, goal-directed task completion, transformi...
🔹 Publication Date: Published on Dec 23
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.20615
• PDF: https://arxiv.org/pdf/2512.20615
• Project Page: https://xuanhuahe.github.io/ORCA/
• Github: https://xuanhuahe.github.io/ORCA/
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
Video avatars currently lack agency for autonomous goal pursuit. ORCA introduces a framework for active intelligence, using a closed-loop Observe-Think-Act-Reflect cycle and a dual-system architecture for strategic reasoning and action. It enables robust, goal-directed task completion, transformi...
🔹 Publication Date: Published on Dec 23
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.20615
• PDF: https://arxiv.org/pdf/2512.20615
• Project Page: https://xuanhuahe.github.io/ORCA/
• Github: https://xuanhuahe.github.io/ORCA/
==================================
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✨FaithLens: Detecting and Explaining Faithfulness Hallucination
📝 Summary:
FaithLens is a cost-efficient model for detecting and explaining faithfulness hallucinations in LLM outputs. It uses synthesized training data and rule-based reinforcement learning. FaithLens outperforms advanced models like GPT-4.1 on 12 tasks while providing high-quality explanations.
🔹 Publication Date: Published on Dec 23
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.20182
• PDF: https://arxiv.org/pdf/2512.20182
• Github: https://github.com/S1s-Z/FaithLens
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
FaithLens is a cost-efficient model for detecting and explaining faithfulness hallucinations in LLM outputs. It uses synthesized training data and rule-based reinforcement learning. FaithLens outperforms advanced models like GPT-4.1 on 12 tasks while providing high-quality explanations.
🔹 Publication Date: Published on Dec 23
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.20182
• PDF: https://arxiv.org/pdf/2512.20182
• Github: https://github.com/S1s-Z/FaithLens
==================================
For more data science resources:
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#AI #DataScience #MachineLearning #HuggingFace #Research