arXiv:2601.10587v2 Announce Type: replace-cross Abstract: The paper introduces a white-box attack on computer vision models using SHAP values. It demonstrates how adversarial evasion attacks can compromise the performance of deep learning models by reducing output confidence or inducing misclassifications. Such attacks are particularly insidious as they can deceive the perception of an algorithm while eluding […]
How Much LLM Does a Self-Revising Agent Actually Need?
arXiv:2604.07236v2 Announce Type: replace Abstract: Recent LLM-based agents often place world modeling, planning, and reflection inside a single language model loop. This can produce capable behavior, but it makes a basic scientific question difficult to answer: which part of the agent’s competence actually comes from the LLM, and which part comes from explicit structure around […]
Auditing Black-Box LLM APIs with a Rank-Based Uniformity Test
arXiv:2506.06975v5 Announce Type: replace-cross Abstract: As API access becomes a primary interface to large language models (LLMs), users often interact with black-box systems that offer little transparency into the deployed model. To reduce costs or maliciously alter model behaviors, API providers may discreetly serve quantized or fine-tuned variants, which can degrade performance and compromise safety. […]
Transforming the Voice of the Customer: Large Language Models for Identifying Customer Needs
arXiv:2503.01870v2 Announce Type: replace-cross Abstract: Identifying customer needs (CNs) is fundamental to product innovation and marketing strategy. Yet for over thirty years, Voice-of-the-Customer (VOC) applications have relied on professional analysts to manually interpret qualitative data and formulate “jobs to be done.” This task is cognitively demanding, time-consuming, and difficult to scale. While current practice uses […]
When Personalization Tricks Detectors: The Feature-Inversion Trap in Machine-Generated Text Detection
arXiv:2510.12476v2 Announce Type: replace-cross Abstract: Large language models (LLMs) have grown more powerful in language generation, producing fluent text and even imitating personal style. Yet, this ability also heightens the risk of identity impersonation. To the best of our knowledge, no prior work has examined personalized machine-generated text (MGT) detection. In this paper, we introduce […]
FactorEngine: A Program-level Knowledge-Infused Factor Mining Framework for Quantitative Investment
arXiv:2603.16365v2 Announce Type: replace Abstract: We study alpha factor mining, the automated discovery of predictive signals from noisy, non-stationary market data-under a practical requirement that mined factors be directly executable and auditable, and that the discovery process remain computationally tractable at scale. Existing symbolic approaches are limited by bounded expressiveness, while neural forecasters often trade […]
Seeing Like an AI: How LLMs Apply (and Misapply) Wikipedia Neutrality Norms
arXiv:2407.04183v4 Announce Type: replace-cross Abstract: Large language models (LLMs) are trained on broad corpora and then used in communities with specialized norms. Is providing LLMs with community rules enough for models to follow these norms? We evaluate LLMs’ capacity to detect (Task 1) and correct (Task 2) biased Wikipedia edits according to Wikipedia’s Neutral Point […]
ReCellTy: Domain-Specific Knowledge Graph Retrieval-Augmented LLMs Reasoning Workflow for Single-Cell Annotation
arXiv:2505.00017v2 Announce Type: replace-cross Abstract: With the rapid development of large language models (LLMs), their application to cell type annotation has drawn increasing attention. However, general-purpose LLMs often face limitations in this specific task due to the lack of guidance from external domain knowledge. To enable more accurate and fully automated cell type annotation, we […]
Chunks as Arms: Multi-Armed Bandit-Guided Sampling for Long-Context LLM Preference Optimization
arXiv:2508.13993v2 Announce Type: replace-cross Abstract: Long-context modeling is critical for a wide range of real-world tasks, including long-context question answering, summarization, and complex reasoning tasks. Recent studies have explored fine-tuning Large Language Models (LLMs) with synthetic data to enhance their long-context capabilities. However, the effectiveness of such approaches is often limited by the low diversity […]
Action Without Interaction: Probing the Physical Foundations of Video LMMs via Contact-Release Detection
arXiv:2511.20162v2 Announce Type: replace-cross Abstract: Large multi-modal models (LMMs) show increasing performance in realistic visual tasks for images and, more recently, for videos. For example, given a video sequence, such models are able to describe in detail objects, the surroundings and dynamic actions. In this study, we explored the extent to which these models ground […]
Fast and Interpretable Protein Substructure Alignment via Optimal Transport
arXiv:2510.11752v2 Announce Type: replace Abstract: Proteins are essential biological macromolecules that execute life functions. Local structural motifs, such as active sites, are the most critical components for linking structure to function and are key to understanding protein evolution and enabling protein engineering. Existing computational methods struggle to identify and compare these local structures, which leaves […]
Agentic Copyright, Data Scraping & AI Governance: Toward a Coasean Bargain in the Era of Artificial Intelligence
arXiv:2604.07546v1 Announce Type: new Abstract: This paper examines how the rapid deployment of multi-agentic AI systems is reshaping the foundations of copyright law and creative markets. It argues that existing copyright frameworks are ill-equipped to govern AI agent-mediated interactions that occur at scale, speed, and with limited human oversight. The paper introduces the concept of […]