Author

S. Sekeh

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Preprint Aug 2026

SABRE: A Multi-Agent Approach for Selecting Out-of-Distribution Detectors Under a Budget

This work introduces SABRE (Selective Agentic Budgeted Reliability Ensemble), which replaces this fixed choice with per-regime selection at inference in post-hoc out-of-distribution detection for vision-language models, and shows reliability must be established at deployment rather than assumed from a benchmark.

M. Wisell, S. Sekeh · 0 citations

Preprint Jul 2026

C-GAP: Class-Aware and Online Prompting Improves Vision-Language Models on Imbalanced Classes

This work introduces C-GAP Caption-Guided Augmentation and Prompting), a detector-agnostic, annotation-free framework that operates in two phases, and establishes a composite caption baseline combining per-image scene descriptions with class-quantity context, which is shown to outperforms scene-description only or class-quantity-only prompts across multiple open-vocabulary architectures and benchmarks.

F. Fernández, A. Jahangiri, S. Sekeh · 0 citations