arXiv:2508.06832v2 Announce Type: replace Abstract: The mainstream paradigm of remote sensing image interpretation has long been dominated by vision-centered models, which rely on visual features for semantic understanding. However, these models face inherent limitations in handling multi-modal reasoning, semantic abstraction, and interactive decision-making. While recent advances have introduced Large Language Models (LLMs) into remote sensing […]
Jenius Agent: Towards Experience-Driven Accuracy Optimization in Real-World Scenarios
arXiv:2601.01857v3 Announce Type: replace Abstract: As agent systems powered by large language models (LLMs) advance, improving performance in context understanding, tool usage, and long-horizon execution has become critical. However, existing agent frameworks and benchmarks provide limited visibility into execution-level behavior, making failures in tool invocation, state tracking, and context management difficult to diagnose. This paper […]
Epistemological Bias As a Means for the Automated Detection of Injustices in Text
arXiv:2407.06098v2 Announce Type: replace-cross Abstract: Injustices in text are often subtle since implicit biases or stereotypes frequently operate unconsciously due to the pervasive nature of prejudice in society. This makes automated detection of injustices more challenging which leads to them being often overlooked. We introduce a novel framework that combines knowledge from epistemology to enhance […]
Panoramic Distortion-Aware Tokenization for Person Detection and Localization in Overhead Fisheye Images
arXiv:2503.14228v4 Announce Type: replace-cross Abstract: Person detection in overhead fisheye images is challenging due to person rotation and small persons. Prior work has mainly addressed person rotation, leaving the small-person problem underexplored. We remap fisheye images to equirectangular panoramas to handle rotation and exploit panoramic geometry to handle small persons more effectively. Conventional detection methods […]
UQLM: A Python Package for Uncertainty Quantification in Large Language Models
arXiv:2507.06196v2 Announce Type: replace-cross Abstract: Hallucinations, defined as instances where Large Language Models (LLMs) generate false or misleading content, pose a significant challenge that impacts the safety and trust of downstream applications. We introduce UQLM, a Python package for LLM hallucination detection using state-of-the-art uncertainty quantification (UQ) techniques. This toolkit offers a suite of UQ-based […]
Reinforcement Learning Fine-Tuning Enhances Activation Intensity and Diversity in the Internal Circuitry of LLMs
arXiv:2509.21044v2 Announce Type: replace-cross Abstract: Large language models (LLMs) acquire extensive prior knowledge through large-scale pretraining and can be further enhanced via supervised fine-tuning (SFT) or reinforcement learning (RL)-based post-training. A growing body of evidence has shown that RL fine-tuning improves the capability of LLMs beyond what SFT alone achieves. However, the underlying mechanisms why […]
Epistemic-aware Vision-Language Foundation Model for Fetal Ultrasound Interpretation
arXiv:2510.12953v3 Announce Type: replace-cross Abstract: Recent medical vision-language models have shown promise on tasks such as VQA, report generation, and anomaly detection. However, most are adapted to structured adult imaging and underperform in fetal ultrasound, which poses challenges of multi-view image reasoning, numerous diseases, and image diversity. To bridge this gap, we introduce FetalMind, a […]
Imitation Game: Reproducing Deep Learning Bugs Leveraging an Intelligent Agent
arXiv:2512.14990v2 Announce Type: replace-cross Abstract: Despite their wide adoption in various domains (e.g., healthcare, finance, software engineering), Deep Learning (DL)-based applications suffer from many bugs, failures, and vulnerabilities. Reproducing these bugs is essential for their resolution, but it is extremely challenging due to the inherent nondeterminism of DL models and their tight coupling with hardware […]
Can professional translators identify machine-generated text?
arXiv:2601.15828v2 Announce Type: replace-cross Abstract: This study investigates whether professional translators can reliably identify short stories generated in Italian by artificial intelligence (AI) without prior specialized training. Sixty-nine translators took part in an in-person experiment, where they assessed three anonymized short stories – two written by ChatGPT-4o and one by a human author. For each […]
Reimagining Peer Review Process Through Multi-Agent Mechanism Design
arXiv:2601.19778v1 Announce Type: cross Abstract: The software engineering research community faces a systemic crisis: peer review is failing under growing submissions, misaligned incentives, and reviewer fatigue. Community surveys reveal that researchers perceive the process as “broken.” This position paper argues that these dysfunctions are mechanism design failures amenable to computational solutions. We propose modeling the […]
Classical JAK2V617F+ Myeloproliferative Neoplasms emergence and development based on real life incidence and mathematical modeling
arXiv:2406.06765v3 Announce Type: replace Abstract: Mathematical modeling allows us to better understand myeloproliferative neoplasms (MPN), a group of blood cancers, emergence and development. We test different mathematical models on an initial cohort to determine the emergence and evolution times before diagnosis of JAK2V617F+ classical MPN (Polycythemia Vera (PV) and Essential Thrombocythemia (ET)). We consider the […]
Uncertainty-Aware 3D Emotional Talking Face Synthesis with Emotion Prior Distillation
arXiv:2601.19112v1 Announce Type: new Abstract: Emotional Talking Face synthesis is pivotal in multimedia and signal processing, yet existing 3D methods suffer from two critical challenges: poor audio-vision emotion alignment, manifested as difficult audio emotion extraction and inadequate control over emotional micro-expressions; and a one-size-fits-all multi-view fusion strategy that overlooks uncertainty and feature quality differences, undermining […]