Author

M. Eltahir

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#machine learning Preprint Sep 2026

Your Benchmark Is Not Saturated: Reviving Multiple-Choice Evaluation with Answer Pooling

Multiple-choice benchmarks are cheap to grade and are running out of room, and the standard remedy, writing harder items, is slow and repeated for every benchmark. A saturated benchmark still holds a harder task. Each question's wrong options are written for that question alone, so a model can score by eliminating a fe...

Mohamed Eltahir, Abobaker Ahmed, Nawaf Barebood et al. · 0 citations

#machine learning Preprint Sep 2026

AnswerMap: Faithful Spatial Interpretability of VLMs from Answer Posteriors

AnswerMap is introduced, a training-free, task-agnostic, black-box visual rationale constructed from the output head that offers a new lens on VLM interpretability and, through its read-outs, a new output interface for visual tasks beyond text tokens.

Mohamed Eltahir, Fardows Adam, Duaa M. Tahir et al. · 0 citations

#machine learning Preprint Sep 2026

The Model Knows When to Stop: Training-Free Early Stopping for Long-Context Reading

Answer-Convergence Stopping (ACS) is introduced, a training-free stopping rule that measures rather than asks, and reveals that by properly utilizing the output signals of frozen models, it can achieve favorable behaviors like adaptive stopping without the need for additional training.

Muath Alyobi, Mohamed Eltahir, Almoayyad Abuljdail et al. · 0 citations

Preprint Sep 2026

Generative Late-Interaction Embeddings For Visual Document Retrieval

Generative Late-Interaction Embeddings (GLIE): k<<N vectors per page learned from the normalized centroids to serve as both a lightweight index and a basis for regenerating the page's full embedding set, with the decoder as its main design surface.

Mohamed Eltahir, Talal Aloushan, Rose Khairoalsendi et al. · 0 citations

Jul 2026

When Does Small Data Work? Accuracy and Efficiency Trade-offs Between Tabular Foundation Models and Conventional Methods for Crowd-State Classification at Hajj and Umrah

A practical map of which approach to prefer under a given label budget and computational budget is summarized as a practical map of which approach to prefer under a given label budget and computational budget.

AlJawharh AlOtaibi, M. Eltahir, Jude AlSubaie · arXiv.org · 0 citations