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Hasan Kurban

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#machine learning Preprint Oct 2026

The Gradient That Top-k Distillation Never Computes

Many deployed distillation pipelines show the student a truncated view of the teacher: keep the top-k probabilities, renormalize, and minimize a divergence on what remains. Renormalization is assumed to account for the missing mass. We prove that it removes the visibility of tail mass, not the freedom to move it. The r...

Rasul Khanbayov, Hasan Kurban · 0 citations
Open access Sep 2026

QuantumCanvas: a multimodal benchmark for learning two-body quantum interactions

Most molecular and materials machine-learning models fit correlations across whole molecules or crystals rather than learning the quantum interactions between atomic pairs. Yet bonding, charge redistribution, orbital hybridization, and electronic coupling all emerge from these two-body interactions. We introduce Quantu...

Can Polat, Mustafa Kurban, E. Serpedin et al. · 0 citations
Preprint Sep 2026

PhysWeep: Does a Video Generator Realize the Physics You Ask For?

Image-to-video generators are often credited with absorbing physical dynamics as implicit world models, a claim the community currently checks with plausibility scores that ask whether a clip looks consistent with real-world motion. Plausibility is the wrong test on its own, because a clip can look natural while encodi...

Rasul Khanbayov, Hasan Kurban · 2 citations
Preprint Sep 2026

Physics as the label for measuring and correcting materials reasoning in multimodal models

Vision-language and language models increasingly interpret materials data, yet benchmarks report that they hallucinate invalid properties and violate physical law. Evaluation matches final answers to scarce human labels, while discovery agents verify final proposals or density functional theory (DFT) execution. Neither...

Hasan Kurban, Rasul Khanbayov, Mustafa Kurban · 0 citations
#machine learning Preprint Aug 2026

The parity gap in crystal tensor prediction

The parity gap is derived, a group-theoretic metric quantifying the piezoelectric tensor freedom permitted by a crystal's proper rotation subgroup but eliminated by inversion symmetry in O(3)$, providing a unified framework to distinguish empirical error reduction from exact structural compliance with physical law.

Can Polat, Mustafa Kurban, E. Serpedin et al. · 0 citations
#artificial intelligence Preprint Aug 2026

Rank Reversal in Multilingual LLM Judges: A Label-Free Double-Centering Calibrator

The estimator is the standard two-way ANOVA interaction-recovery operation under sum-to-zero contrasts; its contribution is its application as a label-free post-hoc calibrator for multilingual LLM judges, an explicit finite-sample concentration bound, and an unbiasedness result that holds even under task-language missp...

Alhasan Mahmood, Samir Abdaljalil, Hasan Kurban · 0 citations
Preprint Aug 2026

ConfTriage: A Calibration-Aware LLM Triage Framework for Pulmonary Nodule Malignancy with Selective Specialist Deferral

Results demonstrate that clinically meaningful diagnostic information can be captured through structured radiological descriptions and leveraged by calibrated LLMs for selective referral, and suggests a practical pathway for combining generalist LLM prediction with specialist AI models in medical decision-support syste...

Md. Rabiul Islam, Samir Abdaljalil, E. Serpedin et al. · 0 citations
Preprint Sep 2026

Separating perception from reasoning in vision-language models: a model-free render ceiling for crystal structures

Multimodal evaluations cannot say whether a vision-language model misread an image or misreasoned about it, because every existing method for separating the two places a second model in the loop. We introduce the render ceiling, a model-free reference for benchmarks built by rendering known objects: inverting the froze...

Can Polat, Mustafa Kurban, E. Serpedin et al. · 0 citations
Preprint Aug 2026

When do machine-learned exchange-correlation improvements inherit into density-functional tight binding?

Machine-learned exchange-correlation functionals correct band gaps at near-semilocal cost, while density-functional tight binding reaches the $10^3$-$10^6$-atom regime; combining them assumes that a better parent yields a better parameterization, but we show it does not. Current-generation functionals are orbital-depen...

Can Polat, Mustafa Kurban, E. Serpedin et al. · 0 citations

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