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.
AI Quantum Intelligence & Pic of the week (2025&12&26)
When prompted for a creative image that was to be published on Boxing Day - Dec 26, 2025 - this is what ChatGPT came up with. We can start to see how the various AI models and tools differ in there interpretations (literal versus figurative) of what an image request conveys.
A New Year’s Message for 2026: To the Builders of the Future
This is the first 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 fu...
Implementing Vibe Proving with Reinforcement Learning
How to make LLMs reason with verifiable, step-by-step logic (Part 2)
The post Implementing Vibe Proving with Reinforcement Learning appeared first on Towards Data Science.
DebitMyData Lays the Foundation for the Human Energy Grid
As AI accelerates beyond human employment capacity and public trust erodes under massive data center expansion, DebitMyData, Inc. is building the missing bridge — the human and digital foundation for the next economy. Before governments acted, before the Genesis Executive Order ignited a national pu...
Physics-Informed Neural Solvers for Periodic Quantum Eigenproblems
arXiv:2512.21349v1 Announce Type: new
Abstract: This thesis presents a physics-informed machine learning framework for solving the Floquet-Bloch eigenvalue problem associated with particles in two-dimensional periodic potentials, with a focus on honeycomb lattice geometry, due to its distinctive ba...
A Reinforcement Learning Approach to Synthetic Data Generation
arXiv:2512.21395v1 Announce Type: new
Abstract: Synthetic data generation (SDG) is a promising approach for enabling data sharing in biomedical studies while preserving patient privacy. Yet, state-of-the-art generative models often require large datasets and complex training procedures, limiting th...
kooplearn: A Scikit-Learn Compatible Library of Algorithms for Evolution Operator Learning
arXiv:2512.21409v1 Announce Type: new
Abstract: kooplearn is a machine-learning library that implements linear, kernel, and deep-learning estimators of dynamical operators and their spectral decompositions. kooplearn can model both discrete-time evolution operators (Koopman/Transfer) and continuous...
DeepCQ: General-Purpose Deep-Surrogate Framework for Lossy Compression Quality Prediction
arXiv:2512.21433v1 Announce Type: new
Abstract: Error-bounded lossy compression techniques have become vital for scientific data management and analytics, given the ever-increasing volume of data generated by modern scientific simulations and instruments. Nevertheless, assessing data quality post-c...
From Visual Perception to Deep Empathy: An Automated Assessment Framework for House-Tree-Person Drawings Using Multimodal LLMs and Multi-Agent Collaboration
arXiv:2512.21360v1 Announce Type: new
Abstract: Background: The House-Tree-Person (HTP) drawing test, introduced by John Buck in 1948, remains a widely used projective technique in clinical psychology. However, it has long faced challenges such as heterogeneous scoring standards, reliance on examin...
Three-way conflict analysis based on alliance and conflict functions
arXiv:2512.21419v1 Announce Type: new
Abstract: Trisecting agents, issues, and agent pairs are essential topics of three-way conflict analysis. They have been commonly studied based on either a rating or an auxiliary function. A rating function defines the positive, negative, or neutral ratings of ...
Feasible strategies in three-way conflict analysis with three-valued ratings
arXiv:2512.21420v1 Announce Type: new
Abstract: Most existing work on three-way conflict analysis has focused on trisecting agent pairs, agents, or issues, which contributes to understanding the nature of conflicts but falls short in addressing their resolution. Specifically, the formulation of fea...