arXiv:2512.16270v1 Announce Type: cross Abstract: Text rendering has recently emerged as one of the most challenging frontiers in visual generation, drawing significant attention from large-scale diffusion and multimodal models. However, text editing within images remains largely unexplored, as it requires generating legible characters while preserving semantic, geometric, and contextual coherence. To fill this gap, we […]
Non-Resolution Reasoning (NRR): A Computational Framework for Contextual Identity and Ambiguity Preservation
arXiv:2512.13478v3 Announce Type: replace-cross Abstract: Current artificial intelligence systems, despite remarkable capabilities in text generation and pattern recognition, exhibit a fundamental architectural limitation: they resolve ambiguity prematurely. This premature semantic collapse — the tendency to collapse multiple valid interpretations into a single output — stems from classical identity assumptions embedded in standard neural architectures. We […]
Domain-Agnostic Causal-Aware Audio Transformer for Infant Cry Classification
arXiv:2512.16271v1 Announce Type: cross Abstract: Accurate and interpretable classification of infant cry paralinguistics is essential for early detection of neonatal distress and clinical decision support. However, many existing deep learning methods rely on correlation-driven acoustic representations, which makes them vulnerable to noise, spurious cues, and domain shifts across recording environments. We propose DACH-TIC, a Domain-Agnostic […]
Interpretable Deep Learning for Stock Returns: A Consensus-Bottleneck Asset Pricing Model
arXiv:2512.16251v1 Announce Type: cross Abstract: We introduce the textitConsensus-Bottleneck Asset Pricing Model (CB-APM), a partially interpretable neural network that replicates the reasoning processes of sell-side analysts by capturing how dispersed investor beliefs are compressed into asset prices through a consensus formation process. By modeling this “bottleneck” to summarize firm- and macro-level information, CB-APM not only […]
Coarse-to-Fine Open-Set Graph Node Classification with Large Language Models
arXiv:2512.16244v1 Announce Type: cross Abstract: Developing open-set classification methods capable of classifying in-distribution (ID) data while detecting out-of-distribution (OOD) samples is essential for deploying graph neural networks (GNNs) in open-world scenarios. Existing methods typically treat all OOD samples as a single class, despite real-world applications, especially high-stake settings such as fraud detection and medical diagnosis, […]
First, do NOHARM: towards clinically safe large language models
arXiv:2512.01241v2 Announce Type: replace-cross Abstract: Large language models (LLMs) are routinely used by physicians and patients for medical advice, yet their clinical safety profiles remain poorly characterized. We present NOHARM (Numerous Options Harm Assessment for Risk in Medicine), a benchmark using 100 real primary care-to-specialist consultation cases to measure frequency and severity of harm from […]
Artificial Intelligence for Microbiology and Microbiome Research
arXiv:2411.01098v2 Announce Type: replace Abstract: Advancements in artificial intelligence (AI) have transformed many scientific fields, with microbiology and microbiome research now experiencing significant breakthroughs through machine learning applications. This review provides a comprehensive overview of AI-driven approaches tailored for microbiology and microbiome studies, emphasizing both technical advancements and biological insights. We begin with an introduction […]
N2N: A Parallel Framework for Large-Scale MILP under Distributed Memory
arXiv:2511.18723v4 Announce Type: replace Abstract: Parallelization has emerged as a promising approach for accelerating MILP solving. However, the complexity of the branch-and-bound (B&B) framework and the numerous effective algorithm components in MILP solvers make it difficult to parallelize. In this study, a scalable parallel framework, N2N (a node-to-node framework that maps the B&B nodes to […]
Protecting Deep Neural Network Intellectual Property with Chaos-Based White-Box Watermarking
arXiv:2512.16658v1 Announce Type: cross Abstract: The rapid proliferation of deep neural networks (DNNs) across several domains has led to increasing concerns regarding intellectual property (IP) protection and model misuse. Trained DNNs represent valuable assets, often developed through significant investments. However, the ease with which models can be copied, redistributed, or repurposed highlights the urgent need […]
Meta-RL Induces Exploration in Language Agents
arXiv:2512.16848v1 Announce Type: cross Abstract: Reinforcement learning (RL) has enabled the training of large language model (LLM) agents to interact with the environment and to solve multi-turn long-horizon tasks. However, the RL-trained agents often struggle in tasks that require active exploration and fail to efficiently adapt from trial-and-error experiences. In this paper, we present LaMer, […]