Position: Stop Anthropomorphizing Intermediate Tokens as Reasoning/Thinking Trac

xiaoyu20061 pts0 comments

[2504.09762] Position: Stop Anthropomorphizing Intermediate Tokens as Reasoning/Thinking Traces!

-->

Computer Science > Artificial Intelligence

arXiv:2504.09762 (cs)

[Submitted on 14 Apr 2025 (v1), last revised 9 Jun 2026 (this version, v4)]

Title:Position: Stop Anthropomorphizing Intermediate Tokens as Reasoning/Thinking Traces!

Authors:Subbarao Kambhampati, Karthik Valmeekam, Siddhant Bhambri, Vardhan Palod, Lucas Saldyt, Kaya Stechly, Soumya Rani Samineni, Durgesh Kalwar, Upasana Biswas<br>View a PDF of the paper titled Position: Stop Anthropomorphizing Intermediate Tokens as Reasoning/Thinking Traces!, by Subbarao Kambhampati and 8 other authors

View PDF<br>HTML (experimental)

Abstract:Intermediate token generation (ITG), where a model produces output before the solution, has become a standard method to improve the performance of language models on reasoning tasks. These intermediate tokens have been called \say{reasoning traces} or even \say{thinking traces} -- implicitly anthropomorphizing the traces, and implying that these traces resemble steps a human might take when solving a challenging problem, and as such can provide an interpretable window into the operation of the model's thinking process to the end user. In this position paper, we present evidence that this anthropomorphization isn't a harmless metaphor, and instead is quite dangerous -- it confuses the nature of these models and how to use them effectively, and leads to questionable research. We call on the community to avoid such anthropomorphization of intermediate tokens.

Comments:<br>Appears in ICML 2026. [This is a fork of v1. This fork, while overlapping with v1 in background section, differs both in the overall focus as well as the specific argument against anthropomorphization of reasoning traces]

Subjects:

Artificial Intelligence (cs.AI)

Cite as:<br>arXiv:2504.09762 [cs.AI]

(or<br>arXiv:2504.09762v4 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2504.09762

Focus to learn more

arXiv-issued DOI via DataCite

Journal reference:<br>ICML 2026

Submission history<br>From: Subbarao Kambhampati [view email]<br>[v1]<br>Mon, 14 Apr 2025 00:03:34 UTC (381 KB)

[v2]<br>Tue, 27 May 2025 16:35:47 UTC (536 KB)

[v3]<br>Fri, 6 Mar 2026 14:36:07 UTC (1,321 KB)

[v4]<br>Tue, 9 Jun 2026 20:08:39 UTC (4,278 KB)

Full-text links:<br>Access Paper:

View a PDF of the paper titled Position: Stop Anthropomorphizing Intermediate Tokens as Reasoning/Thinking Traces!, by Subbarao Kambhampati and 8 other authors<br>View PDF<br>HTML (experimental)<br>TeX Source

view license

Current browse context:

cs.AI

next >

new<br>recent<br>| 2025-04

Change to browse by:

cs

References & Citations

NASA ADS<br>Google Scholar

Semantic Scholar

export BibTeX citation<br>Loading...

BibTeX formatted citation

&times;

loading...

Data provided by:

Bookmark

Bibliographic Tools

Bibliographic and Citation Tools

Bibliographic Explorer Toggle

Bibliographic Explorer (What is the Explorer?)

Connected Papers Toggle

Connected Papers (What is Connected Papers?)

Litmaps Toggle

Litmaps (What is Litmaps?)

scite.ai Toggle

scite Smart Citations (What are Smart Citations?)

Code, Data, Media

Code, Data and Media Associated with this Article

alphaXiv Toggle

alphaXiv (What is alphaXiv?)

Links to Code Toggle

CatalyzeX Code Finder for Papers (What is CatalyzeX?)

DagsHub Toggle

DagsHub (What is DagsHub?)

GotitPub Toggle

Gotit.pub (What is GotitPub?)

Huggingface Toggle

Hugging Face (What is Huggingface?)

ScienceCast Toggle

ScienceCast (What is ScienceCast?)

Demos

Demos

Replicate Toggle

Replicate (What is Replicate?)

Spaces Toggle

Hugging Face Spaces (What is Spaces?)

Spaces Toggle

TXYZ.AI (What is TXYZ.AI?)

Related Papers

Recommenders and Search Tools

Link to Influence Flower

Influence Flower (What are Influence Flowers?)

Core recommender toggle

CORE Recommender (What is CORE?)

Author

Venue

Institution

Topic

About arXivLabs

arXivLabs: experimental projects with community collaborators

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs .

Which authors of this paper are endorsers? |<br>Disable MathJax (What is MathJax?)

toggle traces intermediate reasoning arxiv tokens

Related Articles