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TALON introduces a temporally aware longitudinal framework for radiology report generation that adaptively integrates variable-length patient histories using a dual-channel temporal fusion module to improve accuracy in detecting interval changes and persistent findings.
This paper introduces a temporal multi-signal fusion method for detecting token-level hallucinations in language models using sequence labeling over fused features, achieving improved cross-model performance without access to model internals.
This paper systematically studies how temporal metadata can be structurally embedded into named entity recognition (NER) models for historical texts. Experiments with absolute and relative temporal representations injected via early or late fusion mechanisms show that late fusion strategies yield more robust performance on French and German historical datasets.
Proposes the Multi-Stream Fraud Transformer (MSFT) for financial fraud detection, which independently encodes transaction, login, and risk event streams using Transformers and fuses them with time-aware positional encoding and gated fusion, achieving 0.9961 AUROC on a large dataset.