✨In-Context Reinforcement Learning for Tool Use in Large Language Models
📝 Summary:
In-Context Reinforcement Learning ICRL is an RL-only framework for LLMs to use external tools, eliminating costly supervised fine-tuning. It teaches tool use through in-context examples during training, gradually reducing them. ICRL proves to be a scalable, data-efficient, and state-of-the-art ap...
🔹 Publication Date: Published on Mar 9
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.08068
• PDF: https://arxiv.org/pdf/2603.08068
• Github: https://github.com/applese233/ICRL
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
In-Context Reinforcement Learning ICRL is an RL-only framework for LLMs to use external tools, eliminating costly supervised fine-tuning. It teaches tool use through in-context examples during training, gradually reducing them. ICRL proves to be a scalable, data-efficient, and state-of-the-art ap...
🔹 Publication Date: Published on Mar 9
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.08068
• PDF: https://arxiv.org/pdf/2603.08068
• Github: https://github.com/applese233/ICRL
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
✨Hindsight Credit Assignment for Long-Horizon LLM Agents
📝 Summary:
HCAPO improves credit assignment in long-horizon LLM agents by using hindsight reasoning to refine Q-values and a multi-scale advantage mechanism. It significantly outperforms state-of-the-art methods, boosting success rates on benchmarks like WebShop and ALFWorld. This enhances exploration and c...
🔹 Publication Date: Published on Mar 7
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.08754
• PDF: https://arxiv.org/pdf/2603.08754
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#LLMAgents #ReinforcementLearning #AI #MachineLearning #HindsightReasoning
📝 Summary:
HCAPO improves credit assignment in long-horizon LLM agents by using hindsight reasoning to refine Q-values and a multi-scale advantage mechanism. It significantly outperforms state-of-the-art methods, boosting success rates on benchmarks like WebShop and ALFWorld. This enhances exploration and c...
🔹 Publication Date: Published on Mar 7
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.08754
• PDF: https://arxiv.org/pdf/2603.08754
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#LLMAgents #ReinforcementLearning #AI #MachineLearning #HindsightReasoning
❤1
✨UniCom: Unified Multimodal Modeling via Compressed Continuous Semantic Representations
📝 Summary:
UniCom unifies multimodal understanding and generation via compressed continuous semantic representations. It resolves issues with discrete tokenizers and unstable continuous modeling by efficiently reducing channel dimensions. This yields state-of-the-art generation, superior controllability, an...
🔹 Publication Date: Published on Mar 11
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.10702
• PDF: https://arxiv.org/pdf/2603.10702
• Project Page: https://miazhao7708.github.io/UniComPage/
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#MultimodalAI #GenerativeAI #DeepLearning #AIResearch #SemanticRepresentations
📝 Summary:
UniCom unifies multimodal understanding and generation via compressed continuous semantic representations. It resolves issues with discrete tokenizers and unstable continuous modeling by efficiently reducing channel dimensions. This yields state-of-the-art generation, superior controllability, an...
🔹 Publication Date: Published on Mar 11
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.10702
• PDF: https://arxiv.org/pdf/2603.10702
• Project Page: https://miazhao7708.github.io/UniComPage/
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#MultimodalAI #GenerativeAI #DeepLearning #AIResearch #SemanticRepresentations
✨Lost in Backpropagation: The LM Head is a Gradient Bottleneck
📝 Summary:
The softmax bottleneck in neural LMs is a critical optimization bottleneck, not just an expressivity issue. The rank-D output layer suppresses 95-99% of gradient norm, leading to suboptimal updates and inefficient training. This necessitates new LM head designs.
🔹 Publication Date: Published on Mar 10
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.10145
• PDF: https://arxiv.org/pdf/2603.10145
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#LLM #DeepLearning #Optimization #NeuralNetworks #GradientBottleneck
📝 Summary:
The softmax bottleneck in neural LMs is a critical optimization bottleneck, not just an expressivity issue. The rank-D output layer suppresses 95-99% of gradient norm, leading to suboptimal updates and inefficient training. This necessitates new LM head designs.
🔹 Publication Date: Published on Mar 10
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.10145
• PDF: https://arxiv.org/pdf/2603.10145
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#LLM #DeepLearning #Optimization #NeuralNetworks #GradientBottleneck
✨StyleVLA: Driving Style-Aware Vision Language Action Model for Autonomous Driving
📝 Summary:
StyleVLA is a physics-informed VLA model that generates diverse, style-aware, and kinematically plausible driving trajectories. It uses a hybrid loss and a large dataset, outperforming proprietary models like Gemini-3-Pro on specialized driving tasks.
🔹 Publication Date: Published on Mar 10
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.09482
• PDF: https://arxiv.org/pdf/2603.09482
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#AutonomousDriving #VLA #AI #DeepLearning #Robotics
📝 Summary:
StyleVLA is a physics-informed VLA model that generates diverse, style-aware, and kinematically plausible driving trajectories. It uses a hybrid loss and a large dataset, outperforming proprietary models like Gemini-3-Pro on specialized driving tasks.
🔹 Publication Date: Published on Mar 10
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.09482
• PDF: https://arxiv.org/pdf/2603.09482
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#AutonomousDriving #VLA #AI #DeepLearning #Robotics
✨Causal Concept Graphs in LLM Latent Space for Stepwise Reasoning
📝 Summary:
Causal Concept Graphs identify causal relationships between concepts in LLMs using sparse autoencoders and differentiable structure learning. This method significantly improves causal fidelity for multi-step reasoning over prior techniques, yielding sparse and stable graphs.
🔹 Publication Date: Published on Mar 11
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.10377
• PDF: https://arxiv.org/pdf/2603.10377
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#CausalAI #LLMs #MachineLearning #GraphLearning #ExplainableAI
📝 Summary:
Causal Concept Graphs identify causal relationships between concepts in LLMs using sparse autoencoders and differentiable structure learning. This method significantly improves causal fidelity for multi-step reasoning over prior techniques, yielding sparse and stable graphs.
🔹 Publication Date: Published on Mar 11
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.10377
• PDF: https://arxiv.org/pdf/2603.10377
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#CausalAI #LLMs #MachineLearning #GraphLearning #ExplainableAI
✨According to Me: Long-Term Personalized Referential Memory QA
📝 Summary:
ATM-Bench is a new benchmark for multimodal multi-source personalized referential memory QA, addressing limitations of existing dialogue-focused benchmarks. It includes 4 years of personal data and introduces Schema-Guided Memory SGM. Current AI systems perform poorly under 20 percent on hard set...
🔹 Publication Date: Published on Mar 2
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.01990
• PDF: https://arxiv.org/pdf/2603.01990
• Project Page: https://atmbench.github.io/
• Github: https://github.com/JingbiaoMei/ATM-Bench
✨ Datasets citing this paper:
• https://huggingface.co/datasets/Jingbiao/ATM-Bench
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#AI #QuestionAnswering #LongTermMemory #MachineLearning #Benchmark
📝 Summary:
ATM-Bench is a new benchmark for multimodal multi-source personalized referential memory QA, addressing limitations of existing dialogue-focused benchmarks. It includes 4 years of personal data and introduces Schema-Guided Memory SGM. Current AI systems perform poorly under 20 percent on hard set...
🔹 Publication Date: Published on Mar 2
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.01990
• PDF: https://arxiv.org/pdf/2603.01990
• Project Page: https://atmbench.github.io/
• Github: https://github.com/JingbiaoMei/ATM-Bench
✨ Datasets citing this paper:
• https://huggingface.co/datasets/Jingbiao/ATM-Bench
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#AI #QuestionAnswering #LongTermMemory #MachineLearning #Benchmark
✨ID-LoRA: Identity-Driven Audio-Video Personalization with In-Context LoRA
📝 Summary:
ID-LoRA jointly generates visual appearance and voice with a single model, improving personalization. It uses in-context LoRA adaptation and identity guidance to preserve speaker characteristics. This outperforms existing methods in human preference for voice and style similarity.
🔹 Publication Date: Published on Mar 10
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.10256
• PDF: https://arxiv.org/pdf/2603.10256
• Project Page: https://id-lora.github.io/
• Github: https://github.com/ID-LoRA/ID-LoRA
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#GenerativeAI #AudioVisual #LoRA #Personalization #DeepLearning
📝 Summary:
ID-LoRA jointly generates visual appearance and voice with a single model, improving personalization. It uses in-context LoRA adaptation and identity guidance to preserve speaker characteristics. This outperforms existing methods in human preference for voice and style similarity.
🔹 Publication Date: Published on Mar 10
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.10256
• PDF: https://arxiv.org/pdf/2603.10256
• Project Page: https://id-lora.github.io/
• Github: https://github.com/ID-LoRA/ID-LoRA
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#GenerativeAI #AudioVisual #LoRA #Personalization #DeepLearning
✨COMIC: Agentic Sketch Comedy Generation
📝 Summary:
An AI system generates comedic videos similar to sketch shows. It employs agent-based optimization and LLM critics, trained on YouTube comedy, to evaluate humor. This system produces content approaching professional sketch quality.
🔹 Publication Date: Published on Mar 11
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.11048
• PDF: https://arxiv.org/pdf/2603.11048
• Project Page: https://susunghong.github.io/COMIC/
• Github: https://susunghong.github.io/COMIC/
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#AI #GenerativeAI #SketchComedy #LLMs #ComputationalCreativity
📝 Summary:
An AI system generates comedic videos similar to sketch shows. It employs agent-based optimization and LLM critics, trained on YouTube comedy, to evaluate humor. This system produces content approaching professional sketch quality.
🔹 Publication Date: Published on Mar 11
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.11048
• PDF: https://arxiv.org/pdf/2603.11048
• Project Page: https://susunghong.github.io/COMIC/
• Github: https://susunghong.github.io/COMIC/
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#AI #GenerativeAI #SketchComedy #LLMs #ComputationalCreativity
Forwarded from Machine Learning with Python
Follow the Machine Learning with Python channel on WhatsApp: https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
✨Meissa: Multi-modal Medical Agentic Intelligence
📝 Summary:
Meissa is a lightweight 4B medical MM-LLM that achieves offline agentic capabilities by distilling trajectories from frontier models. It resolves high cost, latency, and privacy issues, matching or exceeding proprietary agents on 13 medical benchmarks with 25x fewer parameters and 22x lower latency.
🔹 Publication Date: Published on Mar 9
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.09018
• PDF: https://arxiv.org/pdf/2603.09018
• Github: https://github.com/Schuture/Meissa
🔹 Models citing this paper:
• https://huggingface.co/CYX1998/Meissa-4B
✨ Datasets citing this paper:
• https://huggingface.co/datasets/CYX1998/Meissa-SFT
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#MedicalAI #LLM #AIagents #MultiModalAI #Healthcare
📝 Summary:
Meissa is a lightweight 4B medical MM-LLM that achieves offline agentic capabilities by distilling trajectories from frontier models. It resolves high cost, latency, and privacy issues, matching or exceeding proprietary agents on 13 medical benchmarks with 25x fewer parameters and 22x lower latency.
🔹 Publication Date: Published on Mar 9
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.09018
• PDF: https://arxiv.org/pdf/2603.09018
• Github: https://github.com/Schuture/Meissa
🔹 Models citing this paper:
• https://huggingface.co/CYX1998/Meissa-4B
✨ Datasets citing this paper:
• https://huggingface.co/datasets/CYX1998/Meissa-SFT
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#MedicalAI #LLM #AIagents #MultiModalAI #Healthcare
✨SVG-EAR: Parameter-Free Linear Compensation for Sparse Video Generation via Error-aware Routing
📝 Summary:
SVG-EAR introduces a parameter-free method for video diffusion transformers to reduce quadratic attention cost. It recovers missing contributions via centroid approximation and uses error-aware routing to prioritize high-error blocks. This improves efficiency and quality, achieving significant sp...
🔹 Publication Date: Published on Mar 9
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.08982
• PDF: https://arxiv.org/pdf/2603.08982
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#VideoGeneration #DiffusionModels #Transformers #AIResearch #MachineLearning
📝 Summary:
SVG-EAR introduces a parameter-free method for video diffusion transformers to reduce quadratic attention cost. It recovers missing contributions via centroid approximation and uses error-aware routing to prioritize high-error blocks. This improves efficiency and quality, achieving significant sp...
🔹 Publication Date: Published on Mar 9
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.08982
• PDF: https://arxiv.org/pdf/2603.08982
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#VideoGeneration #DiffusionModels #Transformers #AIResearch #MachineLearning
✨Coarse-Guided Visual Generation via Weighted h-Transform Sampling
📝 Summary:
This paper presents a novel training-free method for coarse-guided visual generation using h-transform to guide diffusion models. It modifies sampling transition probabilities with a drift function and employs a noise-level-aware schedule. This balances guidance adherence and high-quality synthes...
🔹 Publication Date: Published on Mar 12
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.12057
• PDF: https://arxiv.org/pdf/2603.12057
• Github: https://github.com/HKUST-LongGroup/Coarse-guided-Gen
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
This paper presents a novel training-free method for coarse-guided visual generation using h-transform to guide diffusion models. It modifies sampling transition probabilities with a drift function and employs a noise-level-aware schedule. This balances guidance adherence and high-quality synthes...
🔹 Publication Date: Published on Mar 12
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.12057
• PDF: https://arxiv.org/pdf/2603.12057
• Github: https://github.com/HKUST-LongGroup/Coarse-guided-Gen
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
✨NerVE: Nonlinear Eigenspectrum Dynamics in LLM Feed-Forward Networks
📝 Summary:
NerVE provides a unified framework for analyzing feed-forward network dynamics in large language models through spectral analysis metrics that reveal information flow organization and optimization imp...
🔹 Publication Date: Published on Mar 6
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.06922
• PDF: https://arxiv.org/pdf/2603.06922
• Project Page: https://nerve-eigenspectrum.github.io
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
NerVE provides a unified framework for analyzing feed-forward network dynamics in large language models through spectral analysis metrics that reveal information flow organization and optimization imp...
🔹 Publication Date: Published on Mar 6
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.06922
• PDF: https://arxiv.org/pdf/2603.06922
• Project Page: https://nerve-eigenspectrum.github.io
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
Media is too big
VIEW IN TELEGRAM
✨ShotVerse: Advancing Cinematic Camera Control for Text-Driven Multi-Shot Video Creation
📝 Summary:
ShotVerse introduces a plan-then-control framework for text-driven cinematic multi-shot video generation. It uses a VLM-based planner to generate camera trajectories and a controller for rendering them into video. Supported by a new calibrated dataset, ShotVerse-Bench, it achieves precise, consis...
🔹 Publication Date: Published on Mar 12
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.11421
• PDF: https://arxiv.org/pdf/2603.11421
• Project Page: https://shotverse.github.io/
• Github: https://github.com/Songlin1998/ShotVerse
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
ShotVerse introduces a plan-then-control framework for text-driven cinematic multi-shot video generation. It uses a VLM-based planner to generate camera trajectories and a controller for rendering them into video. Supported by a new calibrated dataset, ShotVerse-Bench, it achieves precise, consis...
🔹 Publication Date: Published on Mar 12
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.11421
• PDF: https://arxiv.org/pdf/2603.11421
• Project Page: https://shotverse.github.io/
• Github: https://github.com/Songlin1998/ShotVerse
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
✨WeEdit: A Dataset, Benchmark and Glyph-Guided Framework for Text-centric Image Editing
📝 Summary:
WeEdit presents a systematic approach for text-centric image editing with a scalable data pipeline, multi-language benchmarks, and a two-stage training strategy combining supervised fine-tuning and re...
🔹 Publication Date: Published on Mar 12
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.11593
• PDF: https://arxiv.org/pdf/2603.11593
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
WeEdit presents a systematic approach for text-centric image editing with a scalable data pipeline, multi-language benchmarks, and a two-stage training strategy combining supervised fine-tuning and re...
🔹 Publication Date: Published on Mar 12
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.11593
• PDF: https://arxiv.org/pdf/2603.11593
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
✨OmniStream: Mastering Perception, Reconstruction and Action in Continuous Streams
📝 Summary:
OmniStream is a unified visual backbone that processes streaming video data through causal spatiotemporal attention and 3D rotary positional embeddings, enabling general-purpose visual understanding a...
🔹 Publication Date: Published on Mar 12
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.12265
• PDF: https://arxiv.org/pdf/2603.12265
• Project Page: https://go2heart.github.io/omnistream/
• Github: https://github.com/Go2Heart/OmniStream
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
OmniStream is a unified visual backbone that processes streaming video data through causal spatiotemporal attention and 3D rotary positional embeddings, enabling general-purpose visual understanding a...
🔹 Publication Date: Published on Mar 12
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.12265
• PDF: https://arxiv.org/pdf/2603.12265
• Project Page: https://go2heart.github.io/omnistream/
• Github: https://github.com/Go2Heart/OmniStream
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
✨Trust Your Critic: Robust Reward Modeling and Reinforcement Learning for Faithful Image Editing and Generation
📝 Summary:
Reinforcement learning framework with novel reward modeling and benchmarking approaches improves fidelity and instruction adherence in image editing and text-to-image generation. AI-generated summary ...
🔹 Publication Date: Published on Mar 12
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.12247
• PDF: https://arxiv.org/pdf/2603.12247
• Project Page: https://firm-reward.github.io/
• Github: https://github.com/VisionXLab/FIRM-Reward
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
Reinforcement learning framework with novel reward modeling and benchmarking approaches improves fidelity and instruction adherence in image editing and text-to-image generation. AI-generated summary ...
🔹 Publication Date: Published on Mar 12
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.12247
• PDF: https://arxiv.org/pdf/2603.12247
• Project Page: https://firm-reward.github.io/
• Github: https://github.com/VisionXLab/FIRM-Reward
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
✨Strategic Navigation or Stochastic Search? How Agents and Humans Reason Over Document Collections
📝 Summary:
MADQA benchmark evaluates multimodal agents' strategic reasoning capabilities through diverse PDF document questions, revealing gaps between human-level accuracy and efficient reasoning performance. A...
🔹 Publication Date: Published on Mar 12
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.12180
• PDF: https://arxiv.org/pdf/2603.12180
• Project Page: https://huggingface.co/spaces/Snowflake/MADQA-Leaderboard
• Github: https://github.com/OxRML/MADQA
✨ Datasets citing this paper:
• https://huggingface.co/datasets/OxRML/MADQA
✨ Spaces citing this paper:
• https://huggingface.co/spaces/Snowflake/MADQA-Leaderboard
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
MADQA benchmark evaluates multimodal agents' strategic reasoning capabilities through diverse PDF document questions, revealing gaps between human-level accuracy and efficient reasoning performance. A...
🔹 Publication Date: Published on Mar 12
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.12180
• PDF: https://arxiv.org/pdf/2603.12180
• Project Page: https://huggingface.co/spaces/Snowflake/MADQA-Leaderboard
• Github: https://github.com/OxRML/MADQA
✨ Datasets citing this paper:
• https://huggingface.co/datasets/OxRML/MADQA
✨ Spaces citing this paper:
• https://huggingface.co/spaces/Snowflake/MADQA-Leaderboard
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
❤1
✨GRADE: Benchmarking Discipline-Informed Reasoning in Image Editing
📝 Summary:
GRADE is introduced as the first benchmark for assessing discipline-informed knowledge and reasoning in image editing, revealing significant limitations in current models under knowledge-intensive edi...
🔹 Publication Date: Published on Mar 12
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.12264
• PDF: https://arxiv.org/pdf/2603.12264
• Project Page: https://grade-bench.github.io/
• Github: https://github.com/VisionXLab/GRADE
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
GRADE is introduced as the first benchmark for assessing discipline-informed knowledge and reasoning in image editing, revealing significant limitations in current models under knowledge-intensive edi...
🔹 Publication Date: Published on Mar 12
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.12264
• PDF: https://arxiv.org/pdf/2603.12264
• Project Page: https://grade-bench.github.io/
• Github: https://github.com/VisionXLab/GRADE
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
✨EndoCoT: Scaling Endogenous Chain-of-Thought Reasoning in Diffusion Models
📝 Summary:
A novel framework called Endogenous Chain-of-Thought is proposed to enhance multimodal large language models' reasoning capabilities in diffusion frameworks by enabling iterative thought refinement an...
🔹 Publication Date: Published on Mar 12
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.12252
• PDF: https://arxiv.org/pdf/2603.12252
• Project Page: https://internlm.github.io/EndoCoT/
• Github: https://github.com/InternLM/EndoCoT
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
A novel framework called Endogenous Chain-of-Thought is proposed to enhance multimodal large language models' reasoning capabilities in diffusion frameworks by enabling iterative thought refinement an...
🔹 Publication Date: Published on Mar 12
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.12252
• PDF: https://arxiv.org/pdf/2603.12252
• Project Page: https://internlm.github.io/EndoCoT/
• Github: https://github.com/InternLM/EndoCoT
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research