This is the second in a short series of articles from various AI models and how they view the upcoming year in 2026. A forward looking 2026 New Year’s message for AI, machine learning, robotics, and automation enthusiasts, celebrating bold innovation, responsible tech, and the builders shaping the f...
The Machine Learning “Advent Calendar” Bonus 1: AUC in Excel
AUC measures how well a model ranks positives above negatives, independent of any chosen threshold.
The post The Machine Learning “Advent Calendar” Bonus 1: AUC in Excel appeared first on Towards Data Science.
Foundation Models as the Backbone of Next-Gen AI Platforms
Foundation models as the backbone of next-gen AI platforms, reshaping enterprise AI architecture, governance, and scale. One signal breaks through the AI noise in 2026: most enterprise AI budgets are no longer allocated to applications. They are spent on platforms. The current industry forecasts hav...
As a developer, tell me if you relate to this – Docker commands are easy to understand but difficult to apply meaningfully. Out of the countless tutorials that I followed, most stopped at syntax, leaving me unsure about what to build next. (Here is an exception – A step-by-step Docker tutorial for c...
We’re in the midst of a global mental-health crisis. More than a billion people worldwide suffer from a mental-health condition, according to the World Health Organization. The prevalence of anxiety and depression is growing in many demographics, particularly young people, and suicide is claiming h...
Pendo Makes Agent Analytics GA for Smarter User Interaction Insights
Pendo’s Agent Analytics is Now GA, Helping Companies See and Optimise How Users Interact With Agents Pendo, the world’s first software experience management platform, announced the general availability of Agent Analytics, expanding access to the only solution in the market for measuring the perform...
Meta just bought Manus, an AI startup everyone has been talking about
Meta says it'll keep Manus running independently while weaving its agents into Facebook, Instagram, and WhatsApp, where Meta's own chatbot, Meta AI, is already available to users.
Pruning Graphs by Adversarial Robustness Evaluation to Strengthen GNN Defenses
arXiv:2512.22128v1 Announce Type: new
Abstract: Graph Neural Networks (GNNs) have emerged as a dominant paradigm for learning on graph-structured data, thanks to their ability to jointly exploit node features and relational information encoded in the graph topology. This joint modeling, however, al...
Towards Unsupervised Causal Representation Learning via Latent Additive Noise Model Causal Autoencoders
arXiv:2512.22150v1 Announce Type: new
Abstract: Unsupervised representation learning seeks to recover latent generative factors, yet standard methods relying on statistical independence often fail to capture causal dependencies. A central challenge is identifiability: as established in disentangled...
SoliReward: Mitigating Susceptibility to Reward Hacking and Annotation Noise in Video Generation Reward Models
arXiv:2512.22170v1 Announce Type: new
Abstract: Post-training alignment of video generation models with human preferences is a critical goal. Developing effective Reward Models (RMs) for this process faces significant methodological hurdles. Current data collection paradigms, reliant on in-prompt p...
Wireless Traffic Prediction with Large Language Model
arXiv:2512.22178v1 Announce Type: new
Abstract: The growing demand for intelligent, adaptive resource management in next-generation wireless networks has underscored the importance of accurate and scalable wireless traffic prediction. While recent advancements in deep learning and foundation models...
Latent Sculpting for Zero-Shot Generalization: A Manifold Learning Approach to Out-of-Distribution Anomaly Detection
arXiv:2512.22179v1 Announce Type: new
Abstract: A fundamental limitation of supervised deep learning in high-dimensional tabular domains is "Generalization Collapse": models learn precise decision boundaries for known distributions but fail catastrophically when facing Out-of-Distribution (OOD) dat...
Bidirectional RAG: Safe Self-Improving Retrieval-Augmented Generation Through Multi-Stage Validation
arXiv:2512.22199v1 Announce Type: new
Abstract: Retrieval-Augmented Generation RAG systems enhance large language models by grounding responses in external knowledge bases, but conventional RAG architectures operate with static corpora that cannot evolve from user interactions. We introduce Bidirec...
Emergent Persuasion: Will LLMs Persuade Without Being Prompted?
arXiv:2512.22201v1 Announce Type: new
Abstract: With the wide-scale adoption of conversational AI systems, AI are now able to exert unprecedented influence on human opinion and beliefs. Recent work has shown that many Large Language Models (LLMs) comply with requests to persuade users into harmful ...
GamiBench: Evaluating Spatial Reasoning and 2D-to-3D Planning Capabilities of MLLMs with Origami Folding Tasks
arXiv:2512.22207v1 Announce Type: new
Abstract: Multimodal large language models (MLLMs) are proficient in perception and instruction-following, but they still struggle with spatial reasoning: the ability to mentally track and manipulate objects across multiple views and over time. Spatial reasonin...
Toward Equitable Recovery: A Fairness-Aware AI Framework for Prioritizing Post-Flood Aid in Bangladesh
arXiv:2512.22210v1 Announce Type: new
Abstract: Post-disaster aid allocation in developing nations often suffers from systematic biases that disadvantage vulnerable regions, perpetuating historical inequities. This paper presents a fairness-aware artificial intelligence framework for prioritizing p...
With Great Capabilities Come Great Responsibilities: Introducing the Agentic Risk & Capability Framework for Governing Agentic AI Systems
arXiv:2512.22211v1 Announce Type: new
Abstract: Agentic AI systems present both significant opportunities and novel risks due to their capacity for autonomous action, encompassing tasks such as code execution, internet interaction, and file modification. This poses considerable challenges for effec...
Train Your Large Model on Multiple GPUs with Pipeline Parallelism
This article is divided into six parts; they are: • Pipeline Parallelism Overview • Model Preparation for Pipeline Parallelism • Stage and Pipeline Schedule • Training Loop • Distributed Checkpointing • Limitations of Pipeline Parallelism Pipeline parallelism means creating the model as a pipeline o...
AI’s early-2025 spending spree featured massive raises and trillion-dollar infrastructure promises. By year’s end, hype gave way to a vibe check, with growing scrutiny over sustainability, safety, and business models.