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William Jurayj

4 accepted papers

2026

Compute When Worth It: Risk Control for Reasoning on a Compute Budget

ICML 2026poster

Reasoning Large Language Models (LLMs) enable test-time scaling, with dataset-level accuracy improving as the token budget increases, motivating adaptive reasoning---spending tokens when they improve reliability and stopping early when additional computation is unlikely to help. However, setting the…

Cited by 0SourceScholar
2026

Language Models and Logic Programs for Trustworthy Tax Reasoning

AAAI 2026technical

According to the United States Internal Revenue Service, "the average American spends $270 and 13 hours filing their taxes". Even beyond the U.S., tax filing requires complex reasoning, combining application of overlapping rules with numerical calculations. Because errors can incur costly penalties,

Cited by 0SourcePDFScholar
2025

CLAIMCHECK: How Grounded are LLM Critiques of Scientific Papers?

EMNLP 2025

A core part of scientific peer review involves providing expert critiques that directly assess the scientific claims a paper makes. While it is now possible to automatically generate plausible (if generic) reviews, ensuring that these reviews are sound and grounded in the papers’ claims remains chal

2025

Is That Your Final Answer? Test-Time Scaling Improves Selective Question Answering

ACL 2025short

Scaling the test-time compute of large language models has demonstrated impressive performance on reasoning benchmarks. However, existing evaluations of test-time scaling make the strong assumption that a reasoning system should always give an answer to any question provided. This overlooks concerns…