arXiv:2506.09362v2 Announce Type: replace-cross Abstract: Peer support plays a vital role in expanding access to mental health care by providing empathetic, community-based support outside formal clinical systems. As digital platforms increasingly mediate such support, the design and impact of these technologies remain under-examined, particularly in Asian contexts. This paper presents findings from an interview study […]
Do AI Models Dream of Faster Code? An Empirical Study on LLM-Proposed Performance Improvements in Real-World Software
arXiv:2510.15494v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) can generate code, but can they generate fast code for complex, real-world software systems? In this study, we investigate this question using a dataset of 65 tasks mined from performance-critical open-source Java projects. Unlike prior studies, which focused on algorithmic puzzles, we conduct experiments on actual […]
M-ArtAgent: Evidence-Based Multimodal Agent for Implicit Art Influence Discovery
arXiv:2604.07468v1 Announce Type: new Abstract: Implicit artistic influence, although visually plausible, is often undocumented and thus poses a historically constrained attribution problem: resemblance is necessary but not sufficient evidence. Most prior systems reduce influence discovery to embedding similarity or label-driven graph completion, while recent multimodal large language models (LLMs) remain vulnerable to temporal inconsistency and […]
Munkres’ General Topology Autoformalized in Isabelle/HOL
arXiv:2604.07455v1 Announce Type: new Abstract: We describe an experiment in LLM-assisted autoformalization that produced over 85,000 lines of Isabelle/HOL code covering all 39 sections of Munkres’ Topology (general topology, Chapters 2–8), from topological spaces through dimension theory. The LLM-based coding agents (initially ChatGPT 5.2 and then Claude Opus 4.6) used 24 active days for that. […]
Position Paper: From Edge AI to Adaptive Edge AI
arXiv:2604.07360v1 Announce Type: cross Abstract: Edge AI is often framed as model compression and deployment under tight constraints. We argue a stronger operational thesis: Edge AI in realistic deployments is necessarily adaptive. In long-horizon operation, a fixed (non-adaptive) configuration faces a fundamental failure mode: as data and operating conditions evolve and change in time, it […]
Adversarial Evasion Attacks on Computer Vision using SHAP Values
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 […]
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 […]