@inproceedings{cui-etal-2026-critical,
title = "Critical Positions in {LLM} Reasoning: A Metacognitive Lens on Intervention-Sensitive Locations",
author = "Cui, Wendi and
Huang, Anni and
Wang, Wei and
Lopez, Damien and
Bi, Sirui",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.1474/",
pages = "31357--31390",
ISBN = "979-8-89176-462-0",
abstract = "Large Language Models (LLMs) achieve strong reasoning performance by generating intermediate reasoning trajectories through autoregressive decoding. Existing approaches such as self-consistency, tree-based reasoning, and critical token analysis improve reasoning by exploring diverse trajectories or identifying influential tokens. In this work, we identify a distinct phenomenon in LLM reasoning: **critical positions** {---} locations where re-sampling continuations disproportionately influences the final outcome, independent of the specific token at that position. Motivated by this, we propose **Metacognitive Influence Tracing (MIT)**, the first unsupervised framework for identifying critical positions from a single reasoning trace using only attention structure. MIT models reasoning trajectories as influence graphs and estimates propagated influence through a diffusion process, enabling the discovery of structurally important reasoning positions without supervision or contrastive trajectories. Building on these insights, we further propose **Adaptive Critical Sampling (ACS)**, an intervention framework that selectively re-samples at critical positions instead of regenerating entire reasoning traces. Across six benchmark tasks and five models, ACS improves accuracy by an average of **7.78 points** over CoT while reducing computational cost by **59.83{\%}** over Self-Consistency."
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%0 Conference Proceedings
%T Critical Positions in LLM Reasoning: A Metacognitive Lens on Intervention-Sensitive Locations
%A Cui, Wendi
%A Huang, Anni
%A Wang, Wei
%A Lopez, Damien
%A Bi, Sirui
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F cui-etal-2026-critical
%X Large Language Models (LLMs) achieve strong reasoning performance by generating intermediate reasoning trajectories through autoregressive decoding. Existing approaches such as self-consistency, tree-based reasoning, and critical token analysis improve reasoning by exploring diverse trajectories or identifying influential tokens. In this work, we identify a distinct phenomenon in LLM reasoning: **critical positions** — locations where re-sampling continuations disproportionately influences the final outcome, independent of the specific token at that position. Motivated by this, we propose **Metacognitive Influence Tracing (MIT)**, the first unsupervised framework for identifying critical positions from a single reasoning trace using only attention structure. MIT models reasoning trajectories as influence graphs and estimates propagated influence through a diffusion process, enabling the discovery of structurally important reasoning positions without supervision or contrastive trajectories. Building on these insights, we further propose **Adaptive Critical Sampling (ACS)**, an intervention framework that selectively re-samples at critical positions instead of regenerating entire reasoning traces. Across six benchmark tasks and five models, ACS improves accuracy by an average of **7.78 points** over CoT while reducing computational cost by **59.83%** over Self-Consistency.
%U https://aclanthology.org/2026.findings-emnlp.1474/
%P 31357-31390
Markdown (Informal)
[Critical Positions in LLM Reasoning: A Metacognitive Lens on Intervention-Sensitive Locations](https://aclanthology.org/2026.findings-emnlp.1474/) (Cui et al., Findings 2026)
ACL