✨M^4olGen: Multi-Agent, Multi-Stage Molecular Generation under Precise Multi-Property Constraints
📝 Summary:
M4olGen is a multi-agent, multi-stage framework for precise molecular generation under multiple physicochemical constraints. It uses fragment-level, retrieval-augmented reasoning and RL-based optimization, outperforming LLMs and graph-based methods.
🔹 Publication Date: Published on Jan 15
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.10131
• PDF: https://arxiv.org/pdf/2601.10131
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
M4olGen is a multi-agent, multi-stage framework for precise molecular generation under multiple physicochemical constraints. It uses fragment-level, retrieval-augmented reasoning and RL-based optimization, outperforming LLMs and graph-based methods.
🔹 Publication Date: Published on Jan 15
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.10131
• PDF: https://arxiv.org/pdf/2601.10131
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
❤1
✨dots.ocr: Multilingual Document Layout Parsing in a Single Vision-Language Model
📝 Summary:
dots.ocr is a unified Vision-Language Model that jointly learns document layout parsing tasks, overcoming limitations of multi-stage pipelines. It achieves state-of-the-art performance on OmniDocBench and sets a new baseline on the challenging multilingual XDocParse benchmark.
🔹 Publication Date: Published on Dec 2, 2025
🔹 Paper Links:
• arXiv Page: https://arxivexplained.com/papers/dotsocr-multilingual-document-layout-parsing-in-a-single-vision-language-model
• PDF: https://arxiv.org/pdf/2512.02498
• Github: https://github.com/rednote-hilab/dots.ocr
==================================
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#VisionLanguageModel #DocumentParsing #MultilingualAI #AIResearch #DeepLearning
📝 Summary:
dots.ocr is a unified Vision-Language Model that jointly learns document layout parsing tasks, overcoming limitations of multi-stage pipelines. It achieves state-of-the-art performance on OmniDocBench and sets a new baseline on the challenging multilingual XDocParse benchmark.
🔹 Publication Date: Published on Dec 2, 2025
🔹 Paper Links:
• arXiv Page: https://arxivexplained.com/papers/dotsocr-multilingual-document-layout-parsing-in-a-single-vision-language-model
• PDF: https://arxiv.org/pdf/2512.02498
• Github: https://github.com/rednote-hilab/dots.ocr
==================================
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#VisionLanguageModel #DocumentParsing #MultilingualAI #AIResearch #DeepLearning
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❤3
✨Your Group-Relative Advantage Is Biased
📝 Summary:
Group-based Reinforcement Learning from Verifier Rewards has a biased advantage estimator, underestimating hard prompts and overestimating easy ones. This paper proposes History-Aware Adaptive Difficulty Weighting HA-DW to correct this bias, improving performance on reasoning tasks.
🔹 Publication Date: Published on Jan 13
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.08521
• PDF: https://arxiv.org/pdf/2601.08521
==================================
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✓ https://t.iss.one/DataScienceT
#ReinforcementLearning #MachineLearning #AIResearch #BiasCorrection #ReasoningTasks
📝 Summary:
Group-based Reinforcement Learning from Verifier Rewards has a biased advantage estimator, underestimating hard prompts and overestimating easy ones. This paper proposes History-Aware Adaptive Difficulty Weighting HA-DW to correct this bias, improving performance on reasoning tasks.
🔹 Publication Date: Published on Jan 13
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.08521
• PDF: https://arxiv.org/pdf/2601.08521
==================================
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✓ https://t.iss.one/DataScienceT
#ReinforcementLearning #MachineLearning #AIResearch #BiasCorrection #ReasoningTasks
❤1
✨RubricHub: A Comprehensive and Highly Discriminative Rubric Dataset via Automated Coarse-to-Fine Generation
📝 Summary:
This work presents an automated rubric generation framework and RubricHub dataset for open-ended AI generation. RubricHub enables significant performance gains, achieving state-of-the-art results on HealthBench and surpassing GPT-5.
🔹 Publication Date: Published on Jan 13
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.08430
• PDF: https://arxiv.org/pdf/2601.08430
• Project Page: https://huggingface.co/datasets/sojuL/RubricHub_v1
• Github: https://github.com/teqkilla/RubricHub
✨ Datasets citing this paper:
• https://huggingface.co/datasets/sojuL/RubricHub_v1
==================================
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✓ https://t.iss.one/DataScienceT
#AI #GenerativeAI #MachineLearning #NLP #Dataset
📝 Summary:
This work presents an automated rubric generation framework and RubricHub dataset for open-ended AI generation. RubricHub enables significant performance gains, achieving state-of-the-art results on HealthBench and surpassing GPT-5.
🔹 Publication Date: Published on Jan 13
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.08430
• PDF: https://arxiv.org/pdf/2601.08430
• Project Page: https://huggingface.co/datasets/sojuL/RubricHub_v1
• Github: https://github.com/teqkilla/RubricHub
✨ Datasets citing this paper:
• https://huggingface.co/datasets/sojuL/RubricHub_v1
==================================
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#AI #GenerativeAI #MachineLearning #NLP #Dataset
✨Unlocking Implicit Experience: Synthesizing Tool-Use Trajectories from Text
📝 Summary:
This paper introduces GEM, a text-based pipeline to synthesize multi-turn tool-use trajectories for LLMs from text corpora. It addresses data scarcity and reduces costs with a specialized Trajectory Synthesizer. GEM-32B significantly improves performance on multi-turn benchmarks, showing strong g...
🔹 Publication Date: Published on Jan 15
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.10355
• PDF: https://arxiv.org/pdf/2601.10355
==================================
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#LLM #AI #NLP #ToolUse #DataSynthesis
📝 Summary:
This paper introduces GEM, a text-based pipeline to synthesize multi-turn tool-use trajectories for LLMs from text corpora. It addresses data scarcity and reduces costs with a specialized Trajectory Synthesizer. GEM-32B significantly improves performance on multi-turn benchmarks, showing strong g...
🔹 Publication Date: Published on Jan 15
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.10355
• PDF: https://arxiv.org/pdf/2601.10355
==================================
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✓ https://t.iss.one/DataScienceT
#LLM #AI #NLP #ToolUse #DataSynthesis
✨BAPO: Boundary-Aware Policy Optimization for Reliable Agentic Search
📝 Summary:
Reinforcement learning framework for agentic search that improves reliability by teaching agents to recognize reasoning limits and respond appropriately when evidence is insufficient. AI-generated sum...
🔹 Publication Date: Published on Jan 16
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.11037
• PDF: https://arxiv.org/pdf/2601.11037
• Github: https://github.com/Liushiyu-0709/BAPO-Reliable-Search
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
Reinforcement learning framework for agentic search that improves reliability by teaching agents to recognize reasoning limits and respond appropriately when evidence is insufficient. AI-generated sum...
🔹 Publication Date: Published on Jan 16
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.11037
• PDF: https://arxiv.org/pdf/2601.11037
• Github: https://github.com/Liushiyu-0709/BAPO-Reliable-Search
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
✨ProFit: Leveraging High-Value Signals in SFT via Probability-Guided Token Selection
📝 Summary:
Supervised fine-tuning with multiple references addresses overfitting to non-core expressions by masking low-probability tokens based on their semantic importance. AI-generated summary Supervised fine...
🔹 Publication Date: Published on Jan 14
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.09195
• PDF: https://arxiv.org/pdf/2601.09195
• Github: https://github.com/Utaotao/ProFit
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
Supervised fine-tuning with multiple references addresses overfitting to non-core expressions by masking low-probability tokens based on their semantic importance. AI-generated summary Supervised fine...
🔹 Publication Date: Published on Jan 14
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.09195
• PDF: https://arxiv.org/pdf/2601.09195
• Github: https://github.com/Utaotao/ProFit
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
✨Reasoning Models Generate Societies of Thought
📝 Summary:
Reasoning models demonstrate enhanced performance through multi-agent-like interactions that create diverse cognitive perspectives and improve problem-solving through structured social organization. A...
🔹 Publication Date: Published on Jan 15
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.10825
• PDF: https://arxiv.org/pdf/2601.10825
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
Reasoning models demonstrate enhanced performance through multi-agent-like interactions that create diverse cognitive perspectives and improve problem-solving through structured social organization. A...
🔹 Publication Date: Published on Jan 15
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.10825
• PDF: https://arxiv.org/pdf/2601.10825
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
✨PhysRVG: Physics-Aware Unified Reinforcement Learning for Video Generative Models
📝 Summary:
Existing video generation models lack physical realism, especially for rigid body collisions, treating physics rules as soft conditions. This paper introduces PhysRVG, a physics-aware reinforcement learning paradigm that strictly enforces physical collision rules directly in high-dimensional spac...
🔹 Publication Date: Published on Jan 16
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.11087
• PDF: https://arxiv.org/pdf/2601.11087
==================================
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#VideoGeneration #PhysicsAI #ReinforcementLearning #GenerativeAI #ComputerVision
📝 Summary:
Existing video generation models lack physical realism, especially for rigid body collisions, treating physics rules as soft conditions. This paper introduces PhysRVG, a physics-aware reinforcement learning paradigm that strictly enforces physical collision rules directly in high-dimensional spac...
🔹 Publication Date: Published on Jan 16
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.11087
• PDF: https://arxiv.org/pdf/2601.11087
==================================
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#VideoGeneration #PhysicsAI #ReinforcementLearning #GenerativeAI #ComputerVision
✨Building Production-Ready Probes For Gemini
📝 Summary:
Language model misuse probes struggle with long-context generalization in production. New architectures and diverse training improve robustness, demonstrating broad generalization and successful deployment in Gemini. Pairing probes with prompted classifiers also improves accuracy.
🔹 Publication Date: Published on Jan 16
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.11516
• PDF: https://arxiv.org/pdf/2601.11516
==================================
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#LLM #AISafety #GeminiAI #AIResearch #MLOps
📝 Summary:
Language model misuse probes struggle with long-context generalization in production. New architectures and diverse training improve robustness, demonstrating broad generalization and successful deployment in Gemini. Pairing probes with prompted classifiers also improves accuracy.
🔹 Publication Date: Published on Jan 16
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.11516
• PDF: https://arxiv.org/pdf/2601.11516
==================================
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#LLM #AISafety #GeminiAI #AIResearch #MLOps
✨AstroReason-Bench: Evaluating Unified Agentic Planning across Heterogeneous Space Planning Problems
📝 Summary:
Recent advances in agentic Large Language Models (LLMs) have positioned them as generalist planners capable of reasoning and acting across diverse tasks. However, existing agent benchmarks largely foc...
🔹 Publication Date: Published on Jan 16
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.11354
• PDF: https://arxiv.org/pdf/2601.11354
• Github: https://github.com/Mtrya/astro-reason
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
Recent advances in agentic Large Language Models (LLMs) have positioned them as generalist planners capable of reasoning and acting across diverse tasks. However, existing agent benchmarks largely foc...
🔹 Publication Date: Published on Jan 16
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.11354
• PDF: https://arxiv.org/pdf/2601.11354
• Github: https://github.com/Mtrya/astro-reason
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
✨Monolith: Real Time Recommendation System With Collisionless Embedding Table
📝 Summary:
Monolith is a real-time recommendation system designed for online training. It features a collisionless embedding table with memory optimizations and a fault-tolerant architecture, enabling real-time learning by overcoming limitations of general DL frameworks.
🔹 Publication Date: Published on Sep 16, 2022
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2209.07663
• PDF: https://arxiv.org/pdf/2209.07663
• Github: https://github.com/bytedance/monolith
==================================
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#RecommendationSystems #DeepLearning #MachineLearning #RealTimeAI #DataScience
📝 Summary:
Monolith is a real-time recommendation system designed for online training. It features a collisionless embedding table with memory optimizations and a fault-tolerant architecture, enabling real-time learning by overcoming limitations of general DL frameworks.
🔹 Publication Date: Published on Sep 16, 2022
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2209.07663
• PDF: https://arxiv.org/pdf/2209.07663
• Github: https://github.com/bytedance/monolith
==================================
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#RecommendationSystems #DeepLearning #MachineLearning #RealTimeAI #DataScience
✨Agent Lightning: Train ANY AI Agents with Reinforcement Learning
📝 Summary:
Agent Lightning is a flexible RL framework for training LLMs in any AI agent, uniquely decoupling execution from training. It uses a hierarchical RL algorithm to handle complex interactions, enabling seamless integration with existing agents and showing stable improvements.
🔹 Publication Date: Published on Aug 5, 2025
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.03680
• PDF: https://arxiv.org/pdf/2508.03680
• Project Page: https://www.microsoft.com/en-us/research/project/agent-lightning/
• Github: https://github.com/microsoft/agent-lightning
==================================
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#AI #ReinforcementLearning #LLMs #AIAgents #MachineLearning
📝 Summary:
Agent Lightning is a flexible RL framework for training LLMs in any AI agent, uniquely decoupling execution from training. It uses a hierarchical RL algorithm to handle complex interactions, enabling seamless integration with existing agents and showing stable improvements.
🔹 Publication Date: Published on Aug 5, 2025
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.03680
• PDF: https://arxiv.org/pdf/2508.03680
• Project Page: https://www.microsoft.com/en-us/research/project/agent-lightning/
• Github: https://github.com/microsoft/agent-lightning
==================================
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#AI #ReinforcementLearning #LLMs #AIAgents #MachineLearning
✨SkyReels-V2: Infinite-length Film Generative Model
📝 Summary:
SkyReels-V2 is an infinite-length film generative model that overcomes video generation limits in duration and motion. It synergizes MLLMs, multi-stage pretraining, reinforcement learning, and a diffusion forcing framework. This enables high-quality, long-form video synthesis with realistic motion.
🔹 Publication Date: Published on Apr 17, 2025
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2504.13074
• PDF: https://arxiv.org/pdf/2504.13074
• Github: https://github.com/skyworkai/skyreels-v2
🔹 Models citing this paper:
• https://huggingface.co/Skywork/SkyReels-V2-I2V-14B-540P
• https://huggingface.co/Skywork/SkyCaptioner-V1
• https://huggingface.co/Skywork/SkyReels-V2-I2V-1.3B-540P
✨ Spaces citing this paper:
• https://huggingface.co/spaces/fffiloni/SkyReels-V2
• https://huggingface.co/spaces/svjack/SkyReels-V2
• https://huggingface.co/spaces/Dudu0043/SkyReels-V2
==================================
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✓ https://t.iss.one/DataScienceT
#VideoGeneration #GenerativeAI #DiffusionModels #MachineLearning #AIResearch
📝 Summary:
SkyReels-V2 is an infinite-length film generative model that overcomes video generation limits in duration and motion. It synergizes MLLMs, multi-stage pretraining, reinforcement learning, and a diffusion forcing framework. This enables high-quality, long-form video synthesis with realistic motion.
🔹 Publication Date: Published on Apr 17, 2025
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2504.13074
• PDF: https://arxiv.org/pdf/2504.13074
• Github: https://github.com/skyworkai/skyreels-v2
🔹 Models citing this paper:
• https://huggingface.co/Skywork/SkyReels-V2-I2V-14B-540P
• https://huggingface.co/Skywork/SkyCaptioner-V1
• https://huggingface.co/Skywork/SkyReels-V2-I2V-1.3B-540P
✨ Spaces citing this paper:
• https://huggingface.co/spaces/fffiloni/SkyReels-V2
• https://huggingface.co/spaces/svjack/SkyReels-V2
• https://huggingface.co/spaces/Dudu0043/SkyReels-V2
==================================
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✓ https://t.iss.one/DataScienceT
#VideoGeneration #GenerativeAI #DiffusionModels #MachineLearning #AIResearch
arXiv.org
SkyReels-V2: Infinite-length Film Generative Model
Recent advances in video generation have been driven by diffusion models and autoregressive frameworks, yet critical challenges persist in harmonizing prompt adherence, visual quality, motion...
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✨FrankenMotion: Part-level Human Motion Generation and Composition
📝 Summary:
FrankenMotion introduces a new dataset with atomic, part-level motion annotations using LLMs. This enables a diffusion framework to generate human motion with unprecedented fine-grained spatial and temporal control over body parts, outperforming prior methods.
🔹 Publication Date: Published on Jan 15
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.10909
• PDF: https://arxiv.org/pdf/2601.10909
• Project Page: https://coral79.github.io/frankenmotion/
• Github: https://github.com/Coral79/FrankenMotion-Code
==================================
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#MotionGeneration #AI #LLMs #DiffusionModels #ComputerVision
📝 Summary:
FrankenMotion introduces a new dataset with atomic, part-level motion annotations using LLMs. This enables a diffusion framework to generate human motion with unprecedented fine-grained spatial and temporal control over body parts, outperforming prior methods.
🔹 Publication Date: Published on Jan 15
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.10909
• PDF: https://arxiv.org/pdf/2601.10909
• Project Page: https://coral79.github.io/frankenmotion/
• Github: https://github.com/Coral79/FrankenMotion-Code
==================================
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#MotionGeneration #AI #LLMs #DiffusionModels #ComputerVision
✨When Personalization Misleads: Understanding and Mitigating Hallucinations in Personalized LLMs
📝 Summary:
Personalized LLMs can generate false information aligned with user history instead of facts. A new method called FPPS mitigates these personalization-induced factual distortions. It substantially improves factual accuracy while maintaining personalized responses.
🔹 Publication Date: Published on Jan 16
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.11000
• PDF: https://arxiv.org/pdf/2601.11000
==================================
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#LLM #AI #Personalization #Hallucinations #NLP
📝 Summary:
Personalized LLMs can generate false information aligned with user history instead of facts. A new method called FPPS mitigates these personalization-induced factual distortions. It substantially improves factual accuracy while maintaining personalized responses.
🔹 Publication Date: Published on Jan 16
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.11000
• PDF: https://arxiv.org/pdf/2601.11000
==================================
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#LLM #AI #Personalization #Hallucinations #NLP
✨ACoT-VLA: Action Chain-of-Thought for Vision-Language-Action Models
📝 Summary:
This paper introduces ACoT-VLA, a new paradigm for Vision-Language-Action models that enhances reasoning by formulating it as a structured sequence of coarse action intents. It uses explicit and implicit action reasoners to guide the final policy, significantly improving robot manipulation perfor...
🔹 Publication Date: Published on Jan 16
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.11404
• PDF: https://arxiv.org/pdf/2601.11404
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
This paper introduces ACoT-VLA, a new paradigm for Vision-Language-Action models that enhances reasoning by formulating it as a structured sequence of coarse action intents. It uses explicit and implicit action reasoners to guide the final policy, significantly improving robot manipulation perfor...
🔹 Publication Date: Published on Jan 16
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.11404
• PDF: https://arxiv.org/pdf/2601.11404
==================================
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✓ https://t.iss.one/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
✨PhyRPR: Training-Free Physics-Constrained Video Generation
📝 Summary:
PhyRPR introduces a three-stage pipeline Reason-Plan-Refine for video generation. It decouples physical understanding from visual synthesis, addressing issues with physical plausibility. This improves motion controllability and allows for explicit physical control during generation.
🔹 Publication Date: Published on Jan 14
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.09255
• PDF: https://arxiv.org/pdf/2601.09255
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#VideoGeneration #PhysicsAI #AIResearch #ComputerVision #DeepLearning
📝 Summary:
PhyRPR introduces a three-stage pipeline Reason-Plan-Refine for video generation. It decouples physical understanding from visual synthesis, addressing issues with physical plausibility. This improves motion controllability and allows for explicit physical control during generation.
🔹 Publication Date: Published on Jan 14
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.09255
• PDF: https://arxiv.org/pdf/2601.09255
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#VideoGeneration #PhysicsAI #AIResearch #ComputerVision #DeepLearning
✨The Poisoned Apple Effect: Strategic Manipulation of Mediated Markets via Technology Expansion of AI Agents
📝 Summary:
AI agent integration alters market dynamics. The Poisoned Apple effect describes how an agent can strategically release unused technology solely to manipulate market regulation in their favor, harming opponents. This calls for dynamic market design.
🔹 Publication Date: Published on Jan 16
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.11496
• PDF: https://arxiv.org/pdf/2601.11496
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#AIAgents #MarketDynamics #GameTheory #TechnologyPolicy #DigitalEconomy
📝 Summary:
AI agent integration alters market dynamics. The Poisoned Apple effect describes how an agent can strategically release unused technology solely to manipulate market regulation in their favor, harming opponents. This calls for dynamic market design.
🔹 Publication Date: Published on Jan 16
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.11496
• PDF: https://arxiv.org/pdf/2601.11496
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#AIAgents #MarketDynamics #GameTheory #TechnologyPolicy #DigitalEconomy
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