publications

2026

1. Language Models Learn Universal Representations of Numbers and Here’s Why You Should Care

   Michal Štefánik, Timothee Mickus, Marek Kadlčík, and 7 more authors

   In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Jul 2026

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   Prior work has shown that large language models (LLMs) often converge to accurate input embedding for numbers, based on sinusoidal representations.In this work, we demonstrate that these representations are in fact strikingly systematic, to the point of being almost perfectly universal: different LLM families develop equivalent sinusoidal structures, and number representations are broadly interchangeable in a large swathe of experimental setups.We show that properly factoring in this characteristic is crucial when it comes to assessing how accurately LLMs encode numeric and other ordinal information, and that mechanistically enhancing this sinusoidality can also lead to reductions of LLMs’ arithmetic errors.

2. Tracing Uncertainty in Language Model "Reasoning"

   Nils Grünefeld, Bertram Højer, Philipp Mondorf, and 5 more authors

   2026

2025

1. Improving Reasoning Performance in Large Language Models via Representation Engineering

   Bertram Højer, Oliver Jarvis, and Stefan Heinrich

   In The Proceedings of the International Conference on Learning Representations, Apr 2025

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   Recent advancements in large language models (LLMs) have resulted in increasingly anthropomorphic language concerning the ability of LLMs to reason. Whether reasoning in LLMs should be understood to be inherently different is, however, widely debated. We propose utilizing a representation engineering approach wherein model activations are read from the residual stream of an LLM when processing a reasoning task. The activations are used to derive a control vector that is applied to the model as an inference-time intervention, modulating the representational space of the model, to improve performance on the specified task. We publish the code for deriving control vectors and analyzing model representations. The method allows us to improve performance on reasoning benchmarks and assess how control vectors influence the final logit distribution of a model via metrics such as KL divergence and entropy. We apply control vectors to Mistral-7B-Instruct and a range of Pythia models on an inductive, a deductive and mathematical reasoning task. We show that an LLM can, to a certain degree, be controlled to improve its perceived reasoning ability by modulating activations. The intervention is dependent upon the ability to reliably extract the model’s typical state when correctly solving a task. Our results suggest that reasoning performance can be modulated in the same manner as other information-processing tasks performed by LLMs and demonstrate that we are capable of improving performance on specific tasks via a simple intervention on the residual stream with no additional training.

2. Research Community Perspectives on “Intelligence” and Large Language Models

   Bertram Højer, Terne Sasha Thorn Jakobsen, Anna Rogers, and 1 more author

   In Findings of the Association for Computational Linguistics: ACL 2025, Jul 2025

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   Despite the widespread use of ‘artificial intelligence’ (AI) framing in Natural Language Processing (NLP) research, it is not clear what researchers mean by “intelligence”. To that end, we present the results of a survey on the notion of “intelligence” among researchers and its role in the research agenda. The survey elicited complete responses from 303 researchers from a variety of fields including NLP, Machine Learning (ML), Cognitive Science, Linguistics, and Neuroscience.We identify 3 criteria of intelligence that the community agrees on the most: generalization, adaptability, & reasoning.Our results suggests that the perception of the current NLP systems as “intelligent” is a minority position (29%).Furthermore, only 16.2% of the respondents see developing intelligent systems as a research goal, and these respondents are more likely to consider the current systems intelligent.

3. On the Notion That Language Models Reason

   Bertram Højer

   Nov 2025

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   Language models (LMs) are said to be exhibiting reasoning, but what does this entail? We assess definitions of reasoning and how key papers in the field of natural language processing (NLP) use the notion and argue that the definitions provided are not consistent with how LMs are trained, process information, and generate new tokens. To illustrate this incommensurability we assume the view that transformer-based LMs implement an \textit\textbraceleft implicit\textbraceright finite-order Markov kernel mapping contexts to conditional token distributions. In this view, reasoning-like outputs correspond to statistical regularities and approximate statistical invariances in the learned kernel rather than the implementation of explicit logical mechanisms. This view is illustrative of the claim that LMs are "statistical pattern matchers"" and not genuine reasoners and provides a perspective that clarifies why reasoning-like outputs arise in LMs without any guarantees of logical consistency. This distinction is fundamental to how epistemic uncertainty is evaluated in LMs. We invite a discussion on the importance of how the computational processes of the systems we build and analyze in NLP research are described.