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Bounding Retraining Equivalence and the Deletion Floor in Materials Machine Unlearning
Can Polat, Mustafa Kurban, Erchin Serpedin +1
In materials machine learning, closely related retained structures can sustain accurate property predictions even after removing a specific record, rendering post-deletion predicti…
Grounded verification of chemical and materials reasoning: detection is the bottleneck
Can Polat, Mustafa Kurban, Erchin Serpedin +1
Language models are moving into chemistry and materials discovery workflows, where a wrong molecular formula, space group, or formation energy can silently propagate into downstrea…
Consistency Has a Computable Blind Spot: A Commutation Theory of Label-Free Reliability for Vision-Language Figure Reading
Rasul Khanbayov, Hasan Kurban
Label-free reliability for vision-language models rests on invariance: perturb the input and a faithful reader's answer should not change. This has a known blind spot, a systematic…
When Does Consensus Mean Correctness? Measuring the Agreement-Accuracy Coupling with Semantics-Preserving Re-Rendering
Rasul Khanbayov, Hasan Kurban
A model's agreement across perturbed inputs is used both as a label-free reliability signal and as a self-training target, on the premise that agreement tracks correctness. That co…
STEMGym: Benchmarking Sequential Decision-Making under Dose Budgets in Autonomous Electron Microscopy
Can Polat, Erchin Serpedin, Mustafa Kurban +1
A central premise of autonomous scientific imaging is that smarter navigation, whether Bayesian, RL-based, or otherwise adaptive, is the principal lever for sample-efficient acquis…
SCALAR: Quantifying Structural Hallucination, Consistency, and Reasoning Gaps in Materials Foundation Models
Can Polat, Erchin Serpedin, Mustafa Kurban +1
Large language models are increasingly applied to materials science reasoning, yet their behavior under physically structured distribution shifts remains poorly understood. We intr…
Exploring Various Sequential Learning Methods for Deformation History Modeling
Muhammed Adil Yatkin, Mihkel Korgesaar, Jani Romanoff +2
Current neural network (NN) models can learn patterns from data points with historical dependence. Specifically, in natural language processing (NLP), sequential learning has trans…