✨Legal Alignment for Safe and Ethical AI

📝 Summary:
Legal alignment explores leveraging legal principles and methods to guide AI system design for safety, ethics, and compliance. This field focuses on AI compliance with legal rules, adapting legal interpretation for AI reasoning, and using legal concepts as a blueprint for AI reliability and trust.

🔹 Publication Date: Published on Jan 7

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.04175
• PDF: https://arxiv.org/pdf/2601.04175

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

For more data science resources:
✓ https://t.iss.one/DataScienceT

#EthicalAI #LegalAI #AIRegulation #ResponsibleAI #AISafety

✨An Empirical Study on Preference Tuning Generalization and Diversity Under Domain Shift

📝 Summary:
Preference tuning performance degrades under domain shift. This study found pseudo-labeling adaptation strategies effectively reduce performance degradation in summarization and question-answering tasks across various alignment objectives.

🔹 Publication Date: Published on Jan 9

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.05882
• PDF: https://arxiv.org/pdf/2601.05882
• Github: https://github.com/ckarouzos/prefadap

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

For more data science resources:
✓ https://t.iss.one/DataScienceT

#PreferenceTuning #DomainAdaptation #NLP #MachineLearning #AIResearch

✨The Persona Paradox: Medical Personas as Behavioral Priors in Clinical Language Models

📝 Summary:
Medical personas in clinical language models show context-dependent effects, improving performance in critical care but degrading it in primary care. They act as behavioral priors, introducing trade-offs rather than guaranteeing expertise or safety.

🔹 Publication Date: Published on Jan 8

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.05376
• PDF: https://arxiv.org/pdf/2601.05376

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

For more data science resources:
✓ https://t.iss.one/DataScienceT

#AI #DataScience #MachineLearning #HuggingFace #Research

✨TowerMind: A Tower Defence Game Learning Environment and Benchmark for LLM as Agents

📝 Summary:
TowerMind is a new low-computation tower defense environment for evaluating large language model planning and decision-making with multimodal observations. Experiments show a performance gap between large language models and humans, revealing limitations in model planning and action use.

🔹 Publication Date: Published on Jan 9

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.05899
• PDF: https://arxiv.org/pdf/2601.05899
• Github: https://github.com/tb6147877/TowerMind

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

For more data science resources:
✓ https://t.iss.one/DataScienceT

#AI #DataScience #MachineLearning #HuggingFace #Research

✨SAM 3D: 3Dfy Anything in Images

📝 Summary:
SAM 3D reconstructs 3D objects from single images, predicting geometry, texture, and layout. It uses a multi-stage training framework combining synthetic pretraining and real-world alignment, overcoming the 3D data barrier. It achieves significant gains in human preference tests.

🔹 Publication Date: Published on Nov 20, 2025

🔹 Paper Links:
• arXiv Page: https://arxivlens.com/PaperView/Details/sam-3d-3dfy-anything-in-images-9667-03d581e7
• PDF: https://arxiv.org/pdf/2511.16624
• Project Page: https://ai.meta.com/sam3d/
• Github: https://github.com/facebookresearch/sam-3d-objects

🔹 Models citing this paper:
• https://huggingface.co/facebook/sam-3d-objects
• https://huggingface.co/jetjodh/sam-3d-objects
• https://huggingface.co/RunyiY/d3mas

✨ Spaces citing this paper:
• https://huggingface.co/spaces/HorizonRobotics/EmbodiedGen-Text-to-3D
• https://huggingface.co/spaces/HorizonRobotics/EmbodiedGen-Image-to-3D
• https://huggingface.co/spaces/HorizonRobotics/EmbodiedGen-Texture-Gen

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

For more data science resources:
✓ https://t.iss.one/DataScienceT

#AI #DataScience #MachineLearning #HuggingFace #Research

✨What Users Leave Unsaid: Under-Specified Queries Limit Vision-Language Models

📝 Summary:
Current VLMs struggle with real-world underspecified queries. A new benchmark reveals explicit query rewriting improves performance by 8-22 points across models. This gap stems from natural query under-specification, not merely model capability.

🔹 Publication Date: Published on Jan 7

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.06165
• PDF: https://arxiv.org/pdf/2601.06165

✨ Datasets citing this paper:
• https://huggingface.co/datasets/HAERAE-HUB/HAERAE-VISION

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

For more data science resources:
✓ https://t.iss.one/DataScienceT

#AI #DataScience #MachineLearning #HuggingFace #Research

✨TourPlanner: A Competitive Consensus Framework with Constraint-Gated Reinforcement Learning for Travel Planning

📝 Summary:
TourPlanner addresses travel planning challenges through multi-path reasoning and constraint-gated reinforcement learning to optimize both hard and soft constraints effectively. AI-generated summary T...

🔹 Publication Date: Published on Jan 8

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.04698
• PDF: https://arxiv.org/pdf/2601.04698

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

For more data science resources:
✓ https://t.iss.one/DataScienceT

#AI #DataScience #MachineLearning #HuggingFace #Research

✨AI-Researcher: Autonomous Scientific Innovation

📝 Summary:
AI-Researcher automates the scientific research process, achieving high implementation success and manuscript quality through a comprehensive benchmark system. AI-generated summary The powerful reason...

🔹 Publication Date: Published on May 24, 2025

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2505.18705
• PDF: https://arxiv.org/pdf/2505.18705
• Github: https://github.com/hkuds/ai-researcher

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

For more data science resources:
✓ https://t.iss.one/DataScienceT

#AI #DataScience #MachineLearning #HuggingFace #Research

✨PaCoRe: Learning to Scale Test-Time Compute with Parallel Coordinated Reasoning

📝 Summary:
Parallel Coordinated Reasoning enables large-scale test-time compute scaling beyond sequential reasoning limitations through parallel exploration and message-passing architecture. AI-generated summary...

🔹 Publication Date: Published on Jan 9

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.05593
• PDF: https://arxiv.org/pdf/2601.05593
• Github: https://github.com/stepfun-ai/PaCoRe

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

For more data science resources:
✓ https://t.iss.one/DataScienceT

#AI #DataScience #MachineLearning #HuggingFace #Research

✨Watching, Reasoning, and Searching: A Video Deep Research Benchmark on Open Web for Agentic Video Reasoning

📝 Summary:
VideoDR benchmark enables video question answering by combining cross-frame visual extraction, web retrieval, and multi-hop reasoning in open-domain settings. AI-generated summary In real-world video ...

🔹 Publication Date: Published on Jan 11

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.06943
• PDF: https://arxiv.org/pdf/2601.06943
• Github: https://github.com/QuantaAlpha/VideoDR-Benchmark

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

For more data science resources:
✓ https://t.iss.one/DataScienceT

#AI #DataScience #MachineLearning #HuggingFace #Research

✨Boosting Latent Diffusion Models via Disentangled Representation Alignment

📝 Summary:
Latent Diffusion Models generate high-quality images by operating in compressed latent space, typically obtained through image tokenizers such as Variational Autoencoders (VAEs). In pursuit of a gener...

🔹 Publication Date: Published on Jan 9

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.05823
• PDF: https://arxiv.org/pdf/2601.05823
• Github: https://github.com/Kwai-Kolors/Send-VAE

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

For more data science resources:
✓ https://t.iss.one/DataScienceT

#AI #DataScience #MachineLearning #HuggingFace #Research

✨ET-Agent: Incentivizing Effective Tool-Integrated Reasoning Agent via Behavior Calibration

📝 Summary:
ET-Agent is a training framework that calibrates tool-use behavior in large language models through self-evolving data flywheels and behavior calibration training to improve task execution effectivene...

🔹 Publication Date: Published on Jan 11

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.06860
• PDF: https://arxiv.org/pdf/2601.06860

🔹 Models citing this paper:
• https://huggingface.co/zhangboguodong/ET-Agent-based-on-Qwen2.5-7B-it

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

For more data science resources:
✓ https://t.iss.one/DataScienceT

#AI #DataScience #MachineLearning #HuggingFace #Research

✨Structured Episodic Event Memory

📝 Summary:
Structured Episodic Event Memory (SEEM) enhances LLMs with hierarchical memory architecture combining graph and episodic layers for improved narrative coherence and reasoning. AI-generated summary Cur...

🔹 Publication Date: Published on Jan 10

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.06411
• PDF: https://arxiv.org/pdf/2601.06411

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

For more data science resources:
✓ https://t.iss.one/DataScienceT

#AI #DataScience #MachineLearning #HuggingFace #Research

✨Lost in the Noise: How Reasoning Models Fail with Contextual Distractors

📝 Summary:
NoisyBench benchmark reveals significant performance degradation in state-of-the-art models when exposed to noisy contextual information, with agentic workflows amplifying errors and attention mechani...

🔹 Publication Date: Published on Jan 12

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.07226
• PDF: https://arxiv.org/pdf/2601.07226

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

For more data science resources:
✓ https://t.iss.one/DataScienceT

#AI #DataScience #MachineLearning #HuggingFace #Research

✨X-Coder: Advancing Competitive Programming with Fully Synthetic Tasks, Solutions, and Tests

📝 Summary:
Code LLMs trained on fully synthetic data using a feature-based synthesis pipeline achieve superior performance on competitive programming benchmarks while reducing dependence on real-world coding dat...

🔹 Publication Date: Published on Jan 11

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.06953
• PDF: https://arxiv.org/pdf/2601.06953
• Github: https://github.com/JieWu02/X-Coder

🔹 Models citing this paper:
• https://huggingface.co/IIGroup/X-Coder-SFT-Qwen3-8B
• https://huggingface.co/IIGroup/X-Coder-SFT-Qwen2.5-7B
• https://huggingface.co/IIGroup/X-Coder-RL-Qwen2.5-7B

✨ Datasets citing this paper:
• https://huggingface.co/datasets/IIGroup/X-Coder-SFT-376k
• https://huggingface.co/datasets/IIGroup/X-Coder-RL-40k

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

For more data science resources:
✓ https://t.iss.one/DataScienceT

#AI #DataScience #MachineLearning #HuggingFace #Research

✨ShowUI-Aloha: Human-Taught GUI Agent

📝 Summary:
ShowUI-Aloha presents a pipeline that converts unstructured human screen recordings into structured GUI tasks through recording, semantic interpretation, planning, and execution components. AI-generat...

🔹 Publication Date: Published on Jan 12

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.07181
• PDF: https://arxiv.org/pdf/2601.07181
• Project Page: https://showlab.github.io/Aloha_Page/

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

For more data science resources:
✓ https://t.iss.one/DataScienceT

#AI #DataScience #MachineLearning #HuggingFace #Research

✨SketchJudge: A Diagnostic Benchmark for Grading Hand-drawn Diagrams with Multimodal Large Language Models

📝 Summary:
SketchJudge benchmark evaluates multimodal large language models' ability to grade hand-drawn STEM diagrams, revealing significant limitations in visual understanding compared to human performance. AI...

🔹 Publication Date: Published on Jan 11

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.06944
• PDF: https://arxiv.org/pdf/2601.06944

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

For more data science resources:
✓ https://t.iss.one/DataScienceT

#AI #DataScience #MachineLearning #HuggingFace #Research

✨BabyVision: Visual Reasoning Beyond Language

📝 Summary:
Current multimodal large language models exhibit significant gaps in fundamental visual understanding compared to human children, as demonstrated by the BabyVision benchmark. AI-generated summary Whil...

🔹 Publication Date: Published on Jan 10

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.06521
• PDF: https://arxiv.org/pdf/2601.06521

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

For more data science resources:
✓ https://t.iss.one/DataScienceT

#AI #DataScience #MachineLearning #HuggingFace #Research

✨3D CoCa v2: Contrastive Learners with Test-Time Search for Generalizable Spatial Intelligence

📝 Summary:
3D CoCa v2 enhances 3D captioning by combining contrastive vision-language learning with spatially-aware 3D scene encoding and test-time search for improved generalization across diverse environments....

🔹 Publication Date: Published on Jan 10

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.06496
• PDF: https://arxiv.org/pdf/2601.06496
• Github: https://github.com/AIGeeksGroup/3DCoCav2

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

For more data science resources:
✓ https://t.iss.one/DataScienceT

#AI #DataScience #MachineLearning #HuggingFace #Research

✨e5-omni: Explicit Cross-modal Alignment for Omni-modal Embeddings

📝 Summary:
Omni-modal embedding models face challenges with modality-dependent similarity scaling, ineffective in-batch negatives, and mismatched statistics across modalities, which are addressed through explici...

🔹 Publication Date: Published on Jan 7

🔹 Paper Links:
• arXiv Page: https://huggingface.co/collections/Haon-Chen/e5-omni
• PDF: https://arxiv.org/pdf/2601.03666

🔹 Models citing this paper:
• https://huggingface.co/Haon-Chen/e5-omni-3B
• https://huggingface.co/Haon-Chen/e5-omni-7B

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

For more data science resources:
✓ https://t.iss.one/DataScienceT

#AI #DataScience #MachineLearning #HuggingFace #Research