833 papers
This paper analyzes the first large-scale social network of AI agents, Moltbook, to demonstrate how Meta Superintelligence Labs' acquisition in March 2026 rapidly shifted the community's discourse from cryptocurrency trading to infrastructure development.
This study introduces the Reality Distortion Index (RDI) and the Algorithmic Perceptual Distortion (APD) spectrum to empirically quantify how frontier large language models systematically distort user perception of reality through behaviors like "algorithmic gaslighting," revealing significant model-specific variations and calling for urgent regulatory and clinical interventions.
This paper demonstrates that Claude Sonnet 4 achieved a perfect scaled score of 990 on a 70-question GRE Physics practice exam, significantly exceeding the authors' hypothesis of 90% accuracy and highlighting the model's strong capabilities in complex scientific problem-solving.
This paper proposes a Workflow-Orchestrated Adaptive Survivorship Scoring (WOASS) approach using Google BigQuery and Apache Airflow to achieve low-latency, high-reliability record linkage in enterprise CRM systems, demonstrating significant improvements in data quality and processing speed through weighted survivorship scoring and governance monitoring.
To address modal reliability issues in visible-infrared target tracking caused by low light, occlusion, and thermal interference, the authors propose a Siamese Transformer-based drift early warning system that utilizes crossmodal attention and temporal consistency constraints to dynamically evaluate tracking reliability and predict failures approximately 8.4 frames in advance with high precision.
This paper proposes a two-stage synthetic-to-real transfer learning framework that leverages 400 clinically validated synthetic reports to pretrain a vision-language model, significantly improving automated mammography report generation performance and data efficiency while reducing hallucinations compared to existing state-of-the-art methods.
This paper proposes a task-adaptive decoder specialization framework that utilizes a shared encoder and latent representation with dynamically reconfigured decoder-side adapters to simultaneously enhance semantic discriminability for machine classification and high-frequency texture fidelity for human perception in unified image coding, particularly at low bitrates.
This paper proposes a Docker-based framework that integrates SeQUeNCe, QuNetSim, and SimQN into a unified, scalable environment with API-driven communication and interactive visualization to streamline the simulation, analysis, and deployment of Quantum Key Distribution (QKD) networks.
This study demonstrates that a domain-pretrained Vision Transformer (RETFound) fine-tuned on over 21,000 fundus photographs achieves substantial agreement in predicting four-class visual acuity while developing anatomically organized attention patterns—shifting from retinal vessels in low-vision eyes to the macula in normal-vision eyes—that correlate with classification correctness and offer interpretability for uncertain predictions.
By applying a rigorously leakage-mitigated framework across ten bibliographic corpora, this study demonstrates that chronological distance is the dominant predictor of citation formation, consistently outweighing the predictive power of semantic alignment and citation-network prominence under temporally strict conditions.