Author

S. Feizi

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Preprint Aug 2026

Thinking Hard, Not Smart: Reasoning Models Fail to Ration Test-Time Compute Across Questions

An exam-style evaluation framework is introduced for studying the global budget allocation of reasoning language models when multiple problems share an end-to-end cost or latency constraint, in which a model must distribute one shared token budget across questions with different difficulty and point values.

Chenrui Fan, Yize Cheng, Ming Li et al. · 0 citations

Preprint Jul 2026

Do Agent Optimizers Compound? A Continual-Learning Evaluation on Terminal-Bench 2.0

A two-phase continual-learning evaluation built from hard tasks in Terminal-Bench 2.0 found that optimization gains compounded only when regression control was built into the optimization loop, providing an inductive bias against shortcut solutions that fail to generalize.

Wenxiao Wang, Priyatham Kattakinda, S. Feizi · 2 citations

Preprint Jul 2026

Retrieval is Enough: Training-Free Interpretability with a Tool-Using Agent

The results suggest that current training-based methods do not yet extract insights beyond their training data, and motivate benchmarks that explicitly require interpretability methods to demonstrate such insights.

S. Balasubramanian, S. Feizi · 0 citations