Papers by Mo Vazifeh

1 papers
seqBench: A Tunable Benchmark to Quantify Sequential Reasoning Limits of LLMs (2025.emnlp-main)

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Challenge: **seqBench** allows systematic variation of several key complexity dimensions.
Approach: They introduce a parametrized benchmark for probing sequential reasoning limits in Large Language Models through precise, multi-dimensional control over several key complexity dimensions.
Outcome: The framework allows systematic variation of logical depth, backtracking requirements and noise ratio on state-of-the-art LLMs.

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