@inproceedings{kervadec-etal-2026-tracing,
title = "Tracing Computation Density in {LLM}s",
author = "Kervadec, Corentin and
Lysova, Iuliia and
Macocco, Iuri and
Baroni, Marco and
Boleda, Gemma",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.1633/",
pages = "37124--37144",
ISBN = "979-8-89176-459-0",
abstract = "Transformer-based large language models (LLMs) are comprised of billions of parameters arranged in deep and wide computational graphs, but it is not clear that they exploit their full capacity for all inputs. We introduce the s-Trace method to efficiently estimate a subgraph of size s that approximates a full model output. With this method, we find the computation in a variety of LLMs to be organized in two distinct phases. A small subgraph mostly composed of early-layer nodes can reconstruct the head of the full model output distribution. Adding further nodes, mostly located in later layers and increasingly consisting of attention heads, leads to incremental refinements in approximating the full output distribution. We find moreover that the amount of necessary computation per input correlates with model uncertainty, and that sparser subgraphs encode shallow statistics, such as unigram frequency. Overall, our results suggest a consistent modular organization in effective LLM computation, with a sparse early-layer core providing a rough prediction that is further refined through denser computations in later layers."
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%0 Conference Proceedings
%T Tracing Computation Density in LLMs
%A Kervadec, Corentin
%A Lysova, Iuliia
%A Macocco, Iuri
%A Baroni, Marco
%A Boleda, Gemma
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F kervadec-etal-2026-tracing
%X Transformer-based large language models (LLMs) are comprised of billions of parameters arranged in deep and wide computational graphs, but it is not clear that they exploit their full capacity for all inputs. We introduce the s-Trace method to efficiently estimate a subgraph of size s that approximates a full model output. With this method, we find the computation in a variety of LLMs to be organized in two distinct phases. A small subgraph mostly composed of early-layer nodes can reconstruct the head of the full model output distribution. Adding further nodes, mostly located in later layers and increasingly consisting of attention heads, leads to incremental refinements in approximating the full output distribution. We find moreover that the amount of necessary computation per input correlates with model uncertainty, and that sparser subgraphs encode shallow statistics, such as unigram frequency. Overall, our results suggest a consistent modular organization in effective LLM computation, with a sparse early-layer core providing a rough prediction that is further refined through denser computations in later layers.
%U https://aclanthology.org/2026.emnlp-main.1633/
%P 37124-37144
Markdown (Informal)
[Tracing Computation Density in LLMs](https://aclanthology.org/2026.emnlp-main.1633/) (Kervadec et al., EMNLP 2026)
ACL
- Corentin Kervadec, Iuliia Lysova, Iuri Macocco, Marco Baroni, and Gemma Boleda. 2026. Tracing Computation Density in LLMs. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 37124–37144, Budapest, Hungary. Association for Computational Linguistics.