arXiv:2511.17220v1 Announce Type: cross Abstract: This study presents PARROT (Persuasion and Agreement Robustness Rating of Output Truth), a robustness focused framework designed to measure the degradation in accuracy that occurs under social pressure exerted on users through authority and persuasion in large language models (LLMs) the phenomenon of sycophancy (excessive conformity). PARROT (i) isolates causal […]
Sex and age determination in European lobsters using AI-Enhanced bioacoustics
arXiv:2511.16848v1 Announce Type: cross Abstract: Monitoring aquatic species, especially elusive ones like lobsters, presents challenges. This study focuses on Homarus gammarus (European lobster), a key species for fisheries and aquaculture, and leverages non-invasive Passive Acoustic Monitoring (PAM). Understanding lobster habitats, welfare, reproduction, sex, and age is crucial for management and conservation. While bioacoustic emissions have […]
Double-Profile Intersection (DoPIo) Ultrasound: Pointwise Shear Elasticity Estimation using Paired Confocal Displacement Profiles
arXiv:2511.16878v1 Announce Type: cross Abstract: Current acoustic radiation force (ARF) based methods for quantifying tissue elasticity primarily rely on shear wave propagation. However, their spatial resolution is limited by the need for spatial averaging, and their accuracy is affected by shear wave guidance, out of plane reflections, and geometric dispersion, which reduce their applicability in […]
A Hybrid Computational Intelligence Framework for scRNA-seq Imputation: Integrating scRecover and Random Forests
arXiv:2511.16923v1 Announce Type: cross Abstract: Single-cell RNA sequencing (scRNA-seq) enables transcriptomic profiling at cellular resolution but suffers from pervasive dropout events that obscure biological signals. We present SCR-MF, a modular two-stage workflow that combines principled dropout detection using scRecover with robust non-parametric imputation via missForest. Across public and simulated datasets, SCR-MF achieves robust and interpretable […]
Optimizing PyTorch Inference with LLM-Based Multi-Agent Systems
arXiv:2511.16964v1 Announce Type: cross Abstract: Maximizing performance on available GPU hardware is an ongoing challenge for modern AI inference systems. Traditional approaches include writing custom GPU kernels and using specialized model compilers to tune high-level code for specific GPU targets. Recent work shows that LLM-based multi-agent systems can effectively perform such tuning, often outperforming existing […]
Parameter-Free Neural Lens Blur Rendering for High-Fidelity Composites
arXiv:2511.17014v1 Announce Type: cross Abstract: Consistent and natural camera lens blur is important for seamlessly blending 3D virtual objects into photographed real-scenes. Since lens blur typically varies with scene depth, the placement of virtual objects and their corresponding blur levels significantly affect the visual fidelity of mixed reality compositions. Existing pipelines often rely on camera […]
OmniPT: Unleashing the Potential of Large Vision Language Models for Pedestrian Tracking and Understanding
arXiv:2511.17053v1 Announce Type: cross Abstract: LVLMs have been shown to perform excellently in image-level tasks such as VQA and caption. However, in many instance-level tasks, such as visual grounding and object detection, LVLMs still show performance gaps compared to previous expert models. Meanwhile, although pedestrian tracking is a classical task, there have been a number […]
UI-CUBE: Enterprise-Grade Computer Use Agent Benchmarking Beyond Task Accuracy to Operational Reliability
arXiv:2511.17131v1 Announce Type: cross Abstract: While current Computer Use Agent (CUA) benchmarks measure task completion effectively, they provide limited assessment of enterprise deployment readiness, emphasizing functional correctness over the operational reliability required for production systems. We present UI-CUBE (UiPath Computer Use BEnchmark), a systematic benchmark comprising 226 tasks across two difficulty tiers designed to expose […]
Hallucinate Less by Thinking More: Aspect-Based Causal Abstention for Large Language Models
arXiv:2511.17170v1 Announce Type: cross Abstract: Large Language Models (LLMs) often produce fluent but factually incorrect responses, a phenomenon known as hallucination. Abstention, where the model chooses not to answer and instead outputs phrases such as “I don’t know”, is a common safeguard. However, existing abstention methods typically rely on post-generation signals, such as generation variations […]
Algorithmic design and implementation considerations of deep MPC
arXiv:2511.17233v1 Announce Type: cross Abstract: Deep Model Predictive Control (Deep MPC) is an evolving field that integrates model predictive control and deep learning. This manuscript is focused on a particular approach, which employs deep neural network in the loop with MPC. This class of approaches distributes control authority between a neural network and an MPC […]
Concept-Based Interpretability for Toxicity Detection
arXiv:2511.16689v1 Announce Type: cross Abstract: The rise of social networks has not only facilitated communication but also allowed the spread of harmful content. Although significant advances have been made in detecting toxic language in textual data, the exploration of concept-based explanations in toxicity detection remains limited. In this study, we leverage various subtype attributes present […]
MoleProLink-RL: geometric transport for domain-policy reinforcement learning in drug-target interaction prediction
npj Digital Medicine, Published online: 24 November 2025; doi:10.1038/s41746-025-02158-0 MoleProLink-RL: geometric transport for domain-policy reinforcement learning in drug-target interaction prediction