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cs.LG2026

Infinite Worlds from One Example: What Local Generators Must Share

Amin Najafgholizadeh, Mohsen Afsharchi

Seeded noise lets games generate any region of an unbounded world on demand. We ask what such a generator needs when every small window must come from one example, the constraint b…

cs.LG2026

Spectral Limits of Polynomial Graph Filters on the Long-Range Ising Model Benchmark

Amin Najafgholizadeh, Mohsen Afsharchi

The Long-Range Ising Model benchmark (LRIM) guarantees long-range dependence by construction: each label is the node's spin times the spin field convolved with a power-law coupling…

cs.LG2026

PATTERN Can Be Solved from Memory: Generator-Aware Baselines for Synthetic Node Classification

Amin Najafgholizadeh, Mohsen Afsharchi

PATTERN, a synthetic node-classification benchmark still used to rank graph networks, plants a random subgraph in a random host graph and asks which nodes belong to it. Its generat…

cs.LG2026

Fitting Every Hint Is Not Enough: Premature Floods in Recurrent Algorithmic Reasoners

Amin Najafgholizadeh, Mohsen Afsharchi

Recurrent algorithmic reasoners are trained with per-step hints on small graphs and run for many more steps on large ones. On breadth-first search in SALSA-CLRS, we find that a rea…

cs.LG2026

DADIR: Density-Aware Data-level Imbalanced Regression Framework

Shermin Shahbazi, Hossein Mohammadi, Mohsen Afsharchi

Imbalanced learning addresses predictive modeling problems with underrepresented regions of the data distribution. Although widely studied in classification, imbalanced regression…

cs.LG2026

Hybrid Imbalanced Regression Through Unified Data-Level and Algorithm-Level Balancing

Shermin Shahbazi, Hossein Mohammadi, Mohsen Afsharchi

Imbalanced learning is a critical challenge in machine learning, where underrepresented target values can bias models and degrade prediction performance on rare but important cases…