Author

Sepideh Kharaghani

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#artificial intelligence Preprint Sep 2026

Text, Pixels, or Both? Evaluating Input Representations for Multimodal Document QA

Every document QA system begins with a choice that is rarely studied on its own: whether to feed the model page images, extracted text, or both. We isolate this choice, holding the prompt, judge, and scoring pipeline fixed, across four commercial model endpoints, two corpora, and two context regimes (gold evidence page...

Nikhil Reddy Pottanigari, Sepideh Kharaghani, Saverio Vadacchino et al. · 0 citations

#artificial intelligence Preprint Sep 2026

Splitting Documents at Lower Cost: Multi-Split Boundary Decisions for LLM-Based Page Stream Segmentation

It is shown that multi-boundary prediction can make zero-shot page stream segmentation more efficient when the window size is selected for the target corpus, and provided the strongest overall accuracy--efficiency trade-off.

Nikhil Reddy Pottanigari, Sepideh Kharaghani, Saverio Vadacchino et al. · 0 citations

#artificial intelligence Preprint Sep 2026

Preserving What Matters: Semantic Scaffolds Beyond Saturation in Summarization Evaluation

Summarization ships in countless production systems, making model selection a routine decision that depends on measuring summary quality. Existing metrics struggle to support this: ROUGE captures only surface overlap, while LLM-as-judge scores saturate to near-identical values that fail to rank models effectively. We o...

Nikhil Reddy Pottanigari, Ramin Fahimi, Noah Bolger et al. · 0 citations