✨MomaGraph: State-Aware Unified Scene Graphs with Vision-Language Model for Embodied Task Planning

📝 Summary:
MomaGraph-R1, a vision-language model trained with reinforcement learning, achieves state-of-the-art performance in predicting task-oriented scene graphs and zero-shot task planning in household envir...

🔹 Publication Date: Published on Dec 18

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.16909
• PDF: https://arxiv.org/pdf/2512.16909
• Github: https://hybridrobotics.github.io/MomaGraph/

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#VisionLanguageModel #EmbodiedAI #ReinforcementLearning #SceneGraphs #Robotics

✨Sharing State Between Prompts and Programs

📝 Summary:
Nightjar programming system introduces shared program state abstraction to facilitate interoperability between natural language code and Python, enhancing task accuracy and reducing code size. AI-gene...

🔹 Publication Date: Published on Dec 16

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.14805
• PDF: https://arxiv.org/pdf/2512.14805
• Github: https://github.com/psg-mit/nightjarpy/

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨ModelTables: A Corpus of Tables about Models

📝 Summary:
ModelTables is a new benchmark corpus of 90K structured performance and configuration tables about AI models, linking them to their context. Its evaluation for table search reveals a clear need for improved methods in understanding structured model knowledge.

🔹 Publication Date: Published on Dec 18

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.16106
• PDF: https://arxiv.org/pdf/2512.16106
• Github: https://github.com/RJMillerLab/ModelTables

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#AI #Datasets #MachineLearning #StructuredData #TableSearch

✨Improving Recursive Transformers with Mixture of LoRAs

📝 Summary:
This paper proposes Mixture of LoRAs MoL to restore expressivity in parameter-shared recursive transformers. MoL uses token-conditional weight modulation in a shared feed-forward network, achieving state-of-the-art performance with compact models. An expert-merging procedure further enables effic...

🔹 Publication Date: Published on Dec 14

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.12880
• PDF: https://arxiv.org/pdf/2512.12880

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨Reasoning Within the Mind: Dynamic Multimodal Interleaving in Latent Space

📝 Summary:
DMLR is a new framework inspired by human cognition, dynamically interleaving reasoning and perception in latent space. It uses confidence-guided optimization for latent think tokens and injects relevant visual features, improving cross-modal reasoning and perception efficiently.

🔹 Publication Date: Published on Dec 14

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.12623
• PDF: https://arxiv.org/pdf/2512.12623
• Project Page: https://mllm-dmlr.github.io/
• Github: https://mllm-dmlr.github.io

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#AI #DataScience #MachineLearning #HuggingFace #Research

🔥 NEW YEAR 2026 – PREMIUM SCIENTIFIC PAPER WRITING OFFER 🔥
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✨When Reasoning Meets Its Laws

📝 Summary:
The Laws of Reasoning LoRe framework defines desired reasoning for Large Reasoning Models, focusing on compute and accuracy. A benchmark, LoRe-Bench, reveals models often lack compositionality, which a finetuning method improves for better performance.

🔹 Publication Date: Published on Dec 19

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.17901
• PDF: https://arxiv.org/pdf/2512.17901
• Project Page: https://lore-project.github.io/
• Github: https://github.com/ASTRAL-Group/LoRe

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#AI #LargeLanguageModels #Reasoning #MachineLearning #NLP

✨Seed-Prover 1.5: Mastering Undergraduate-Level Theorem Proving via Learning from Experience

📝 Summary:
Seed-Prover 1.5 is a formal theorem-proving model that uses agentic reinforcement learning and an efficient scaling workflow. It achieves superior performance in solving undergraduate, graduate, and PhD-level math problems with reduced computational resources. This demonstrates the potential of l...

🔹 Publication Date: Published on Dec 19

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.17260
• PDF: https://arxiv.org/pdf/2512.17260
• Github: https://github.com/ByteDance-Seed/Seed-Prover

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#TheoremProving #ReinforcementLearning #AI #Mathematics #AI4Math

✨SWE-Bench++: A Framework for the Scalable Generation of Software Engineering Benchmarks from Open-Source Repositories

📝 Summary:
SWE-Bench++ is an automated framework generating scalable, multilingual, repository-level coding tasks from live GitHub pull requests. It overcomes manual curation limits and static datasets, offering a benchmark to evaluate and improve code generation models across 11 languages.

🔹 Publication Date: Published on Dec 19

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.17419
• PDF: https://arxiv.org/pdf/2512.17419
• Project Page: https://research.turing.com/swebench
• Github: https://huggingface.co/papers?q=GitHub%20pull%20requests

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#SoftwareEngineering #CodeGeneration #AIBenchmarking #MachineLearning #OpenSource

✨4D-RGPT: Toward Region-level 4D Understanding via Perceptual Distillation

📝 Summary:
4D-RGPT, a specialized multimodal LLM, enhances 4D perception in video inputs through Perceptual 4D Distillation and is evaluated on R4D-Bench, a new benchmark for depth-aware dynamic scenes. AI-gener...

🔹 Publication Date: Published on Dec 18

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.17012
• PDF: https://arxiv.org/pdf/2512.17012
• Project Page: https://ca-joe-yang.github.io/resource/projects/4D_RGPT

==================================

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨Probing Scientific General Intelligence of LLMs with Scientist-Aligned Workflows

📝 Summary:
A framework for Scientific General Intelligence (SGI) is presented, evaluated using SGI-Bench, and improved with Test-Time Reinforcement Learning, highlighting gaps in existing models' scientific capa...

🔹 Publication Date: Published on Dec 18

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.16969
• PDF: https://arxiv.org/pdf/2512.16969
• Project Page: https://internscience.github.io/SGI-Page/
• Github: https://github.com/InternScience/SGI-Bench

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨Animate Any Character in Any World

📝 Summary:
AniX extends controllable-entity models to enable diverse, user-defined character interactions in static 3D environments via natural language. It synthesizes temporally coherent videos through conditional autoregressive video generation, allowing characters to perform open-ended actions.

🔹 Publication Date: Published on Dec 18

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.17796
• PDF: https://arxiv.org/pdf/2512.17796
• Project Page: https://snowflakewang.github.io/AniX/
• Github: https://github.com/snowflakewang/AniX

==================================

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#GenerativeAI #VideoGeneration #CharacterAnimation #NLP #3D

✨Are We on the Right Way to Assessing LLM-as-a-Judge?

📝 Summary:
Sage is a human-free evaluation suite assessing LLM-as-a-Judge consistency using rational choice theory. It reveals significant reliability problems in current top LLM judges, even in difficult cases. The study suggests finetuning, explicit rubrics, and panel judging can boost consistency.

🔹 Publication Date: Published on Dec 17

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.16041
• PDF: https://arxiv.org/pdf/2512.16041

==================================

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#LLMEvaluation #LLMReliability #AIResearch #GenAI #NLP

✨Physics of Language Models: Part 4.1, Architecture Design and the Magic of Canon Layers

📝 Summary:
Canon layers are lightweight architectural components that enhance language model reasoning depth and breadth by promoting horizontal information flow. They improve performance across various architectures, validated in synthetic tasks and real-world pretraining.

🔹 Publication Date: Published on Dec 19

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.17351
• PDF: https://arxiv.org/pdf/2512.17351
• Project Page: https://physics.allen-zhu.com/part-4-architecture-design/part-4-1
• Github: https://github.com/facebookresearch/PhysicsLM4

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#LanguageModels #LLM #AIArchitecture #DeepLearning #NLP

✨Turn-PPO: Turn-Level Advantage Estimation with PPO for Improved Multi-Turn RL in Agentic LLMs

📝 Summary:
Turn-PPO improves multi-turn reinforcement learning for LLM agents by using a turn-level MDP for advantage estimation. This PPO variant outperforms GRPO and standard PPO, addressing limitations in long-horizon reasoning. It demonstrates effectiveness on WebShop and Sokoban datasets.

🔹 Publication Date: Published on Dec 18

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.17008
• PDF: https://arxiv.org/pdf/2512.17008

==================================

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#LLM #ReinforcementLearning #AI #MachineLearning #AgenticAI

✨Robust-R1: Degradation-Aware Reasoning for Robust Visual Understanding

📝 Summary:
A novel framework, Robust-R1, enhances multimodal large language models' robustness to visual degradations through explicit modeling, supervised fine-tuning, reward-driven alignment, and dynamic reaso...

🔹 Publication Date: Published on Dec 19

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.17532
• PDF: https://arxiv.org/pdf/2512.17532
• Project Page: https://jqt.me/index.html

==================================

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨PhysBrain: Human Egocentric Data as a Bridge from Vision Language Models to Physical Intelligence

📝 Summary:
Proposed Egocentric2Embodiment pipeline translates human egocentric videos into structured training data for robots, enhancing their egocentric understanding and task performance. AI-generated summary...

🔹 Publication Date: Published on Dec 18

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.16793
• PDF: https://arxiv.org/pdf/2512.16793
• Project Page: https://zgc-embodyai.github.io/PhysBrain/

==================================

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨StageVAR: Stage-Aware Acceleration for Visual Autoregressive Models

📝 Summary:
StageVAR accelerates visual autoregressive models by recognizing early stages are critical while later detail-refinement stages can be pruned or approximated. This plug-and-play framework achieves up to 3.4x speedup with minimal quality loss, outperforming existing methods.

🔹 Publication Date: Published on Dec 18

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.16483
• PDF: https://arxiv.org/pdf/2512.16483
• Github: https://github.com/sen-mao/StageVAR

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#ComputerVision #DeepLearning #ModelAcceleration #AI #NeuralNetworks