Parameter-Efficient Neural CDEs via Implicit Function Jacobians
arXiv:2512.20625v1 Announce Type: new
Abstract: Neural Controlled Differential Equations (Neural CDEs, NCDEs) are a unique branch of methods, specifically tailored for analysing temporal sequences. However, they come with drawbacks, the main one being the number of parameters, required for the meth...
Learning Evolving Latent Strategies for Multi-Agent Language Systems without Model Fine-Tuning
arXiv:2512.20629v1 Announce Type: new
Abstract: This study proposes a multi-agent language framework that enables continual strategy evolution without fine-tuning the language model's parameters. The core idea is to liberate the latent vectors of abstract concepts from traditional static semantic r...
Zero-Training Temporal Drift Detection for Transformer Sentiment Models: A Comprehensive Analysis on Authentic Social Media Streams
arXiv:2512.20631v1 Announce Type: new
Abstract: We present a comprehensive zero-training temporal drift analysis of transformer-based sentiment models validated on authentic social media data from major real-world events. Through systematic evaluation across three transformer architectures and rigo...
Real Time Detection and Quantitative Analysis of Spurious Forgetting in Continual Learning
arXiv:2512.20634v1 Announce Type: new
Abstract: Catastrophic forgetting remains a fundamental challenge in continual learning for large language models. Recent work revealed that performance degradation may stem from spurious forgetting caused by task alignment disruption rather than true knowledge...
BitRL-Light: 1-bit LLM Agents with Deep Reinforcement Learning for Energy-Efficient Smart Home Lighting Optimization
arXiv:2512.20623v1 Announce Type: new
Abstract: Smart home lighting systems consume 15-20% of residential energy but lack adaptive intelligence to optimize for user comfort and energy efficiency simultaneously. We present BitRL-Light, a novel framework combining 1-bit quantized Large Language Model...
Quantum-Inspired Multi Agent Reinforcement Learning for Exploration Exploitation Optimization in UAV-Assisted 6G Network Deployment
arXiv:2512.20624v1 Announce Type: new
Abstract: This study introduces a quantum inspired framework for optimizing the exploration exploitation tradeoff in multiagent reinforcement learning, applied to UAVassisted 6G network deployment. We consider a cooperative scenario where ten intelligent UAVs a...
arXiv:2512.20626v1 Announce Type: new
Abstract: Retrieval-augmented generation (RAG) enables large language models (LLMs) to dynamically access external information, which is powerful for answering questions over previously unseen documents. Nonetheless, they struggle with high-level conceptual und...
MicroProbe: Efficient Reliability Assessment for Foundation Models with Minimal Data
arXiv:2512.20630v1 Announce Type: new
Abstract: Foundation model reliability assessment typically requires thousands of evaluation examples, making it computationally expensive and time-consuming for real-world deployment. We introduce microprobe, a novel approach that achieves comprehensive reliab...
Large Language Models for EDA Cloud Job Resource and Lifetime Prediction
arXiv:2512.19701v1 Announce Type: new
Abstract: The rapid growth of cloud computing in the Electronic Design Automation (EDA) industry has created a critical need for resource and job lifetime prediction to achieve optimal scheduling. Traditional machine learning methods often struggle with the com...
Reducing Label Dependency in Human Activity Recognition with Wearables: From Supervised Learning to Novel Weakly Self-Supervised Approaches
arXiv:2512.19713v1 Announce Type: new
Abstract: Human activity recognition (HAR) using wearable sensors has advanced through various machine learning paradigms, each with inherent trade-offs between performance and labeling requirements. While fully supervised techniques achieve high accuracy, they...
Development and external validation of a multimodal artificial intelligence mortality prediction model of critically ill patients using multicenter data
arXiv:2512.19716v1 Announce Type: new
Abstract: Early prediction of in-hospital mortality in critically ill patients can aid clinicians in optimizing treatment. The objective was to develop a multimodal deep learning model, using structured and unstructured clinical data, to predict in-hospital mor...
Thermodynamic Focusing for Inference-Time Search: Practical Methods for Target-Conditioned Sampling and Prompted Inference
arXiv:2512.19717v1 Announce Type: new
Abstract: Finding rare but useful solutions in very large candidate spaces is a recurring practical challenge across language generation, planning, and reinforcement learning. We present a practical framework, \emph{Inverted Causality Focusing Algorithm} (ICFA)...
Synthetic Data Blueprint (SDB): A modular framework for the statistical, structural, and graph-based evaluation of synthetic tabular data
arXiv:2512.19718v1 Announce Type: new
Abstract: In the rapidly evolving era of Artificial Intelligence (AI), synthetic data are widely used to accelerate innovation while preserving privacy and enabling broader data accessibility. However, the evaluation of synthetic data remains fragmented across ...
PhysMaster: Building an Autonomous AI Physicist for Theoretical and Computational Physics Research
arXiv:2512.19799v1 Announce Type: new
Abstract: Advances in LLMs have produced agents with knowledge and operational capabilities comparable to human scientists, suggesting potential to assist, accelerate, and automate research. However, existing studies mainly evaluate such systems on well-defined...
A Branch-and-Price Algorithm for Fast and Equitable Last-Mile Relief Aid Distribution
arXiv:2512.19882v1 Announce Type: new
Abstract: The distribution of relief supplies to shelters is a critical aspect of post-disaster humanitarian logistics. In major disasters, prepositioned supplies often fall short of meeting all demands. We address the problem of planning vehicle routes from a ...
Interpolative Decoding: Exploring the Spectrum of Personality Traits in LLMs
arXiv:2512.19937v1 Announce Type: new
Abstract: Recent research has explored using very large language models (LLMs) as proxies for humans in tasks such as simulation, surveys, and studies. While LLMs do not possess a human psychology, they often can emulate human behaviors with sufficiently high f...
Zero-Shot Segmentation through Prototype-Guidance for Multi-Label Plant Species Identification
arXiv:2512.19957v1 Announce Type: new
Abstract: This paper presents an approach developed to address the PlantClef 2025 challenge, which consists of a fine-grained multi-label species identification, over high-resolution images. Our solution focused on employing class prototypes obtained from the t...
FGDCC: Fine-Grained Deep Cluster Categorization -- A Framework for Intra-Class Variability Problems in Plant Classification
arXiv:2512.19960v1 Announce Type: new
Abstract: Intra-class variability is given according to the significance in the degree of dissimilarity between images within a class. In that sense, depending on its intensity, intra-class variability can hinder the learning process for DL models, specially wh...
Comparative Evaluation of Explainable Machine Learning Versus Linear Regression for Predicting County-Level Lung Cancer Mortality Rate in the United States
arXiv:2512.17934v1 Announce Type: new
Abstract: Lung cancer (LC) is a leading cause of cancer-related mortality in the United States. Accurate prediction of LC mortality rates is crucial for guiding targeted interventions and addressing health disparities. Although traditional regression-based mode...
What's the Price of Monotonicity? A Multi-Dataset Benchmark of Monotone-Constrained Gradient Boosting for Credit PD
arXiv:2512.17945v1 Announce Type: new
Abstract: Financial institutions face a trade-off between predictive accuracy and interpretability when deploying machine learning models for credit risk. Monotonicity constraints align model behavior with domain knowledge, but their performance cost - the pric...
Convolutional-neural-operator-based transfer learning for solving PDEs
arXiv:2512.17969v1 Announce Type: new
Abstract: Convolutional neural operator is a CNN-based architecture recently proposed to enforce structure-preserving continuous-discrete equivalence and enable the genuine, alias-free learning of solution operators of PDEs. This neural operator was demonstrate...
CodeGEMM: A Codebook-Centric Approach to Efficient GEMM in Quantized LLMs
arXiv:2512.17970v1 Announce Type: new
Abstract: Weight-only quantization is widely used to mitigate the memory-bound nature of LLM inference. Codebook-based methods extend this trend by achieving strong accuracy in the extremely low-bit regime (e.g., 2-bit). However, current kernels rely on dequant...
Parameter-Efficient Fine-Tuning for HAR: Integrating LoRA and QLoRA into Transformer Models
arXiv:2512.17983v1 Announce Type: new
Abstract: Human Activity Recognition is a foundational task in pervasive computing. While recent advances in self-supervised learning and transformer-based architectures have significantly improved HAR performance, adapting large pretrained models to new domain...