✨Patient-Similarity Cohort Reasoning in Clinical Text-to-SQL
📝 Summary:
CLINSQL is a new benchmark for evaluating text-to-SQL models on complex clinical tasks, including patient similarity, using real EHR data. Current models achieve moderate execution scores but remain far from clinical reliability for real-world EHR analytics.
🔹 Publication Date: Published on Jan 14
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.09876
• PDF: https://arxiv.org/pdf/2601.09876
• Github: https://github.com/Barryshen1/ClinSQL
✨ Datasets citing this paper:
• https://huggingface.co/datasets/yifeis02/ClinSQL
==================================
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📝 Summary:
CLINSQL is a new benchmark for evaluating text-to-SQL models on complex clinical tasks, including patient similarity, using real EHR data. Current models achieve moderate execution scores but remain far from clinical reliability for real-world EHR analytics.
🔹 Publication Date: Published on Jan 14
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.09876
• PDF: https://arxiv.org/pdf/2601.09876
• Github: https://github.com/Barryshen1/ClinSQL
✨ Datasets citing this paper:
• https://huggingface.co/datasets/yifeis02/ClinSQL
==================================
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✨V-DPM: 4D Video Reconstruction with Dynamic Point Maps
📝 Summary:
Dynamic Point Maps extended to video input through V-DPM framework achieve state-of-the-art 3D and 4D reconstruction by recovering both dynamic depth and full 3D motion of scene points. AI-generated s...
🔹 Publication Date: Published on Jan 14
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.09499
• PDF: https://arxiv.org/pdf/2601.09499
• Project Page: https://www.robots.ox.ac.uk/~vgg/research/vdpm/
• Github: https://github.com/eldar/vdpm
==================================
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📝 Summary:
Dynamic Point Maps extended to video input through V-DPM framework achieve state-of-the-art 3D and 4D reconstruction by recovering both dynamic depth and full 3D motion of scene points. AI-generated s...
🔹 Publication Date: Published on Jan 14
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.09499
• PDF: https://arxiv.org/pdf/2601.09499
• Project Page: https://www.robots.ox.ac.uk/~vgg/research/vdpm/
• Github: https://github.com/eldar/vdpm
==================================
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❤1
✨PACEvolve: Enabling Long-Horizon Progress-Aware Consistent Evolution
📝 Summary:
PACEvolve framework addresses key failure modes in LLM evolutionary search through hierarchical context management, momentum-based backtracking, and adaptive sampling policies for improved self-improv...
🔹 Publication Date: Published on Jan 15
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.10657
• PDF: https://arxiv.org/pdf/2601.10657
==================================
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📝 Summary:
PACEvolve framework addresses key failure modes in LLM evolutionary search through hierarchical context management, momentum-based backtracking, and adaptive sampling policies for improved self-improv...
🔹 Publication Date: Published on Jan 15
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.10657
• PDF: https://arxiv.org/pdf/2601.10657
==================================
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✨RigMo: Unifying Rig and Motion Learning for Generative Animation
📝 Summary:
RigMo unifies rig and motion learning directly from raw mesh sequences, encoding deformations into compact latent spaces. This framework generates interpretable, plausible 3D animation, offering superior reconstruction and generalization over baselines.
🔹 Publication Date: Published on Jan 10
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.06378
• PDF: https://arxiv.org/pdf/2601.06378
• Project Page: https://rigmo-page.github.io/
• Github: https://rigmo-page.github.io
==================================
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📝 Summary:
RigMo unifies rig and motion learning directly from raw mesh sequences, encoding deformations into compact latent spaces. This framework generates interpretable, plausible 3D animation, offering superior reconstruction and generalization over baselines.
🔹 Publication Date: Published on Jan 10
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.06378
• PDF: https://arxiv.org/pdf/2601.06378
• Project Page: https://rigmo-page.github.io/
• Github: https://rigmo-page.github.io
==================================
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✨Demystifying the Slash Pattern in Attention: The Role of RoPE
📝 Summary:
Slash-Dominant Heads in LLMs emerge when queries and keys are almost rank-one and Rotary Position Embedding has dominant medium-high frequencies. Theoretical proof shows these conditions, combined with gradient descent, explain their emergence.
🔹 Publication Date: Published on Jan 13
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.08297
• PDF: https://arxiv.org/pdf/2601.08297
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
Slash-Dominant Heads in LLMs emerge when queries and keys are almost rank-one and Rotary Position Embedding has dominant medium-high frequencies. Theoretical proof shows these conditions, combined with gradient descent, explain their emergence.
🔹 Publication Date: Published on Jan 13
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.08297
• PDF: https://arxiv.org/pdf/2601.08297
==================================
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✨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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📝 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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❤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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🚀 Master Data Science & Programming!
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✨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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#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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#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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#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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#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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📝 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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✨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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📝 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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✨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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✨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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✨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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