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Kiran Tomlinson

8 accepted papers

2026

Reasoning about Reasoning: BAPO Bounds on Chain-of-Thought Token Complexity in LLMs

ICML 2026poster

Inference-time scaling via chain-of-thought (CoT) reasoning is a major driver of state-of-the-art LLM performance, but it comes with substantial latency and compute costs. We address a fundamental theoretical question: *how many* reasoning tokens are required to solve a problem as input size grows? …

Cited by 0SourceScholar
2025

Lost in Transmission: When and Why LLMs Fail to Reason Globally

NeurIPS 2025spotlight

Despite their many successes, transformer-based large language models (LLMs) continue to struggle with tasks that require complex reasoning over large parts of their input. We argue that these failures arise due to capacity limits on the accurate flow of information within LLMs. To formalize this is…

Cited by 0SourceScholar
2025

When the Universe is Too Big: Bounding Consideration Probabilities for Plackett-Luce Rankings

AISTATS 2025poster

The widely used Plackett-Luce ranking model assumes that individuals rank items by making repeated choices from a universe of items. But in many cases the universe is too big for people to plausibly consider all options. In the choice literature, this issue has been addressed by supposing that indiv…

Cited by 0SourcecodeScholar