Amirhossein Samandar

Amirhossein Samandar

Cosmology · statistical inference · probabilistic machine learning · computational methods

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portfolio

publications

Cosmic topology. Part IVa. Classification of manifolds using machine learning: a case study with small toroidal universes

Published in Journal of Cosmology and Astroparticle Physics 09 (2024) 057, 2024

Classifiers trained on simulated CMB maps recover near-likelihood-level discrimination between manifolds at a fraction of the cost. A rare setting in which the ground-truth likelihood is computable, so the learned model’s discrepancy from the optimal decision rule can be measured rather than assumed.

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Don’t Pass@k: A Bayesian Framework for Large Language Model Evaluation

Published in International Conference on Learning Representations (ICLR) 2026, 2026

Replaces Pass@k and avg@N with posterior estimates and credible intervals on a model’s underlying success probability. Outcomes are modelled as categorical with a Dirichlet prior, giving closed-form posterior mean and uncertainty for any weighted rubric. Achieves faster convergence and greater rank stability than Pass@k at far smaller sample counts.

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Test-Time Scaling in Reasoning LLMs: Inference Regimes, Evaluation, and Reproducibility

Published in arXiv preprint, 2026

Formalizes test-time scaling as budgeted inference over the implicit prefix tree of an autoregressive transformer, distinguishing sequential, leaf-level and prefix-level scaling. Treats the entire inference system as the evaluated object and specifies reproducibility requirements separating exact replay from distributional reproducibility. Over 2 billion reasoning traces released.

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The Topology of the Universe

Published in Nature Astronomy, 2026

Invited review of the observational status of cosmic topology and the prospects for CMB (LiteBIRD, Taurus) and large-scale-structure searches.

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Beyond the Kullback–Leibler divergence: the likelihood-ratio distribution as a detectability measure for cosmic topology

Published in In preparation, 2026

The KL divergence is an expected information gain and cannot, by itself, give a detection probability on a single realization. This work derives the exact sampling distribution of the log-likelihood ratio between two zero-mean Gaussian models — a generalized non-central chi-squared — and uses it to convert expected information gain into a detection probability with ROC curves and calibrated interval coverage. Supplies the frequentist validation layer that learned ratio estimators currently lack, and connects to Bayesian experimental design. Model-agnostic; cosmic topology is the case study.

talks

LLM Evaluation and Test-Time Scaling

Published:

Invited seminar on calibrated Bayesian evaluation of large language models and the formalization of test-time scaling as budgeted inference.

teaching

Teaching experience 1

Undergraduate course, University 1, Department, 2014

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Teaching experience 2

Workshop, University 1, Department, 2015

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