Position: Stop Anthropomorphizing Intermediate Tokens as Reasoning/Thinking Trac
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.
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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 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 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.
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