CASCADE: Cumulative Agentic Skill Creation through Autonomous Development and Evolution
arXiv:2512.23880v1 Announce Type: new
Abstract: Large language model (LLM) agents currently depend on predefined tools or brittle tool generation, constraining their capability and adaptability to complex scientific tasks. We introduce CASCADE, a self-evolving agentic framework representing an earl...
A Proof-of-Concept for Explainable Disease Diagnosis Using Large Language Models and Answer Set Programming
arXiv:2512.23932v1 Announce Type: new
Abstract: Accurate disease prediction is vital for timely intervention, effective treatment, and reducing medical complications. While symbolic AI has been applied in healthcare, its adoption remains limited due to the effort required for constructing high-qual...
SPARK: Search Personalization via Agent-Driven Retrieval and Knowledge-sharing
arXiv:2512.24008v1 Announce Type: new
Abstract: Personalized search demands the ability to model users' evolving, multi-dimensional information needs; a challenge for systems constrained by static profiles or monolithic retrieval pipelines. We present SPARK (Search Personalization via Agent-Driven ...
ROAD: Reflective Optimization via Automated Debugging for Zero-Shot Agent Alignment
arXiv:2512.24040v1 Announce Type: new
Abstract: Automatic Prompt Optimization (APO) has emerged as a critical technique for enhancing Large Language Model (LLM) performance, yet current state-of-the-art methods typically rely on large, labeled gold-standard development sets to compute fitness score...
Coordinate Matrix Machine: A Human-level Concept Learning to Classify Very Similar Documents
arXiv:2512.23749v1 Announce Type: new
Abstract: Human-level concept learning argues that humans typically learn new concepts from a single example, whereas machine learning algorithms typically require hundreds of samples to learn a single concept. Our brain subconsciously identifies important feat...
arXiv:2512.23752v1 Announce Type: new
Abstract: Recent work has shown that small transformers trained in controlled "wind-tunnel'' settings can implement exact Bayesian inference, and that their training dynamics produce a geometric substrate -- low-dimensional value manifolds and progressively ort...
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...
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...
Eating more vitamin C can physically change your skin
Vitamin C doesn’t just belong in skincare products—it works even better when you eat it. Scientists discovered that vitamin C from food travels through the bloodstream into every layer of the skin, boosting collagen and skin renewal. People who ate two vitamin C–packed kiwifruit daily showed thicker...