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The Emergence of Abstract Thought in Large Language Models Beyond Any Language

T0 review · 3 major / 5 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read Large language models progressively develop a compact set of language-agnostic neurons whose deactivation degrades performance across all languages.

desk verdict A real empirical trend that is worth refereeing, but the load-bearing sigma threshold is never specified and the appendix computes a layer-wise proxy rather than the Eq. 1 final-embedding norm, so the central measurement is underdetermined. read the letter →

arxiv 2506.09890 v1 pith:6SOSWDQS submitted 2025-06-11 cs.CL cs.AI

Yuxin Chen , Yiran Zhao , Yang Zhang , An Zhang , Kenji Kawaguchi , Shafiq Joty , Junnan Li , Tat-Seng Chua
show 2 more authors
Michael Qizhe Shieh Wenxuan Zhang
This is my paper
classification cs.CLcs.AI
keywords largelanguagemodelsmultilingualismlanguage-agnosticneuronslanguage-sharedneuronablationabstractthoughtneuron-targetedtrainingmultilingualreasoning
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper argues that large language models do not merely "think in English" when handling other languages; they progressively develop a compact, language-agnostic core of shared neurons whose removal degrades performance in every language tested. The authors identify language-related neurons by deleting single rows and columns of the parameter matrices, split them into neurons shared across six languages and neurons exclusive to one language, and track how the shared set grows in size and functional importance across successive model generations. Over time shared neurons become disproportionately important: deactivating them raises perplexity far more than deactivating language-exclusive neurons, which the paper reads as evidence that these neurons carry abstract reasoning not tied to any particular language. On this basis the authors propose neuron-specific training: models that lack the language-agnostic core improve from training any language-related neurons, while models that have it improve most from training language-exclusive neurons. If correct, this shifts multilingual AI from translation-based pipelines toward directly strengthening the model's shared reasoning machinery.

What carries the argument

The central object is the language-related neuron: a single row or column of a parameter matrix counts as activated for a language if zeroing it changes the model's output embedding by at least a threshold $\sigma$ on every sampled input in that language. Shared neurons are those activated for all languages considered; exclusive neurons are those active for only one. Two metrics carry the argument: the shared-neuron ratio (Equation 4), the size of the shared set relative to the average exclusive set, and the language-agnostic score (Equation 6), the log of the average, over languages, of the per-neuron perplexity increase from ablating shared versus exclusive neurons. A parallel neuron-detection algorithm makes the single-neuron ablations computationally feasible for feed-forward and attention parameters. This machinery converts the question "does the model think in English?" into a measurable comparison of ablation effects.

What would settle it

Recompute the shared-neuron ratio and language-agnostic score for the Qwen1.5-Qwen2-Qwen2.5 family while sweeping the activation threshold $\sigma$ over values from 0.001 to 0.1 in output-embedding L2 norm; if the across-generation increase is not monotonic across the whole sweep, the central trend is an artifact of the unspecified threshold.

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Extended reading notes

Core claim

The paper's central claim is that LLMs progressively develop a core language-agnostic parameter space: a remarkably small set of neurons whose deactivation degrades performance across all languages, and that these shared neurons evolve into language-agnostic neurons supporting abstract functions like semantic reasoning and generalization. The evidence has three parts. Across Qwen, Llama, and Gemma families, the proportion of language-shared relative to language-exclusive neurons rises with release date and correlates strongly with multilingual benchmark scores (Pearson $R=0.92$, Spearman $\rho=0.88$). The language-agnostic score—the log of the average per-language ratio of shared-versus-exclusive neuron importance measured by perplexity change upon ablation—also rises across generations; in recent models, ablating shared neurons degrades perplexity by orders of magnitude more than ablating exclusive neurons, while random deactivation has almost no effect. Third, targeted continuous pretraining guided by this score improves multilingual reasoning on MGSM, with the largest average gains (about 5.7 points) when the neuron group matched to the model's language-agnostic level is trained. The authors interpret the growing dominance of shared neurons and the shrinking influence of exclusive ones as evidence that later-generation LLMs operate in a language-agnostic conceptual space rather than through an English intermediate.

Load-bearing premise

The paper never states the numerical cutoff that decides when a neuron counts as language-related, and all of the shared-versus-exclusive trends and training advice would shift if that cutoff moved.

Editorial extensions

If this is right

  • Later-generation LLMs increasingly compute multilingual answers through a language-agnostic core rather than a translation-through-English pipeline, so non-English performance is not necessarily capped by English performance.
  • A model's language-agnostic score can be used to pick the right neuron group to train: for low-score models almost any language-related neuron helps, for medium-score models shared neurons help most, and for high-score models exclusive neurons give the largest gains (e.g., +4.0 average MGSM points for Llama-3.1-8B).
  • Because language-related neurons are sparse, multilingual ability is concentrated in a manipulable subset of parameters rather than distributed uniformly across the network.
  • Neuron-removal tests can serve as a training-free diagnostic for when a model has developed language-agnostic, reasoning-like behavior, guiding when to switch training emphasis.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Going beyond the paper: a sensitivity sweep over the activation threshold $\sigma$ is needed to confirm that the rising shared-neuron trend and the language-agnostic score orderings are not artifacts of a single cutoff.
  • Going beyond the paper: the same shared-versus-exclusive ablation ratio could be measured in vision-language models to test whether the "language-agnostic" neurons support a modality-agnostic semantic space.
  • Going beyond the paper: if shared neurons are the seat of cross-lingual reasoning, then content embedded in one language can spread to all languages through them, giving a concrete mechanism for the cross-lingual misinformation risk the authors flag.
  • Going beyond the paper: a task-dissociation experiment—ablating shared versus exclusive neurons on reasoning-heavy versus knowledge-retrieval benchmarks separately—would directly test whether the shared set truly carries abstract reasoning rather than general task competence.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 5 minor

Summary. The paper argues that LLMs progressively develop a small, shared set of parameters ("language-agnostic neurons") that are critical for processing all languages, and that this set grows in proportion and functional importance across model generations. It defines language-related neurons through an ablation threshold σ (Eq. 1), classifies them as shared or exclusive (Eq. 3), and introduces a Language Agnostic Score (Eq. 6) based on perplexity changes. It reports increasing trends across Qwen, Llama, and Gemma models, and proposes neuron-targeted continuous-pretraining strategies. The training experiments on three Llama models show small gains on MGSM and MMMLU.

Significance. If the measurement were fully specified and robust, the paper would make a substantial contribution: it offers a parameter-level account of multilingual processing that challenges the English-centric view, and it connects this account to a practical training recipe. Strengths include coverage of 20 models across families and release dates, the use of external multilingual benchmarks (MMMLU, MGSM), a random-neuron ablation control, and an open-source code repository. However, the central quantitative evidence is not reproducible as reported because the activation threshold is never specified and the detection algorithm computes a different quantity from the one in the defining equation. The interpretive leap from “shared neurons have larger deletion-induced perplexity changes” to “abstract thought” also needs additional controls.

major comments (3)
  1. [Eq. (1), Eq. (2), Appendix A] The definition of a language-related neuron depends on a threshold σ that is never specified, and the implementation does not compute the quantity in Eq. (1). Specifically, Eq. (1) defines activation via the change in the final output embedding, but Appendix A computes layer-wise impact norms ‖f(X;Θ) − f(X;Θ⊖N)‖₂ and explicitly calls this “a proxy or component” for the overall impact. No value, selection rule, sensitivity analysis, or calibration between layer-wise and final-embedding norms is provided. Because N_lang, N_shared, N_exclusive, the ratio in Eq. (4), the importance in Eq. (5), and the Language Agnostic Score in Eq. (6) all derive from these neuron sets, the reported proportions, the trends in Figures 1, 3, and 5, and the classification into low/medium/high language-agnostic models used in Section 4 are not reproducible from the manuscript as written.
  2. [Section 3.3, Eq. (5)] The inference from a high importance ratio Impℓ to “abstract thought” is underdetermined. Shared neurons are defined as the intersection of language-related neurons across six languages; such neurons could simply be generally critical parameters whose deletion hurts any input, rather than carriers of language-agnostic semantics. The random-neuron control in Figure 4 shows only that the identified neurons are not random; it does not compare shared neurons with a control matched on overall ablation impact, activation frequency, or layer/position. Without such a control, the language-agnostic score cannot distinguish “shared because important for all languages” from “shared because important for everything.” A concrete control, such as matching exclusive neurons on their monolingual importance before comparing shared and exclusive groups, would be needed to support the conceptual claim.
  3. [Section 4, Table 1] The training results are suggestive but not yet load-bearing evidence for the proposed strategy. The experiments use only three models and 100,000 general documents per language, with no variance or significance measures reported. The pattern in Table 1 is also not fully consistent with the stated predictions: for Llama-3.2-1B, classified as low language-agnostic, tuning exclusive neurons produces the largest MGSM gain (4.2) rather than the expected comparable benefit from both shared and exclusive, and for Llama-3.2-3B random neuron tuning gives +0.7 on MGSM, whereas the text emphasizes that random tuning is ineffective. This does not invalidate the approach, but it requires additional ablations and reporting of variability before the prescriptive claims can be accepted.
minor comments (5)
  1. [Section 4.2] There is a typo: “Lamma-3.1-8B” should be “Llama-3.1-8B.”
  2. [Figure 2 caption] The caption lists “Dr” among the languages; this should be “De” for German.
  3. [Section 3.1] The text says the fraction of shared and exclusive neurons is “coarsely estimated at around 0.3%”, but Figure 2 is titled “Fraction of All Neurons (%)”; please clarify whether the axis is a percentage or a fraction to avoid confusion.
  4. [References] Several references appear with both a 2024 and a 2025 version of the same title (e.g., Liu et al. 2024/2025, Schut et al. 2025a/2025b); please consolidate or distinguish them clearly.
  5. [Section 2.2, Eq. (4)] The shared neuron ratio is averaged over languages in the denominator, but the rationale for this normalization is not discussed; a brief explanation would help readers understand why this particular ratio is preferred over the simpler |Nshared| / (|Nshared| + |N_exclusive|).

Circularity Check

1 steps flagged · score 5.0 of 10

Neuron importance is measured on the same data used to select the neurons, so the 'language-specialized' and 'language-agnostic' findings are partly forced by construction; the cross-generation trend and training experiments add independent evidence.

  1. fitted input called prediction [Section 3.1, 'Neuron Detection Corpus'; see also Appendix B.2]
    "For each language, we identify language-related neurons by analyzing activation patterns on 1000 sentences sampled from the OSCAR corpus (Abadji et al., 2022). To quantify the functional contribution of these neurons, we further compute perplexity changes caused by deactivating them, using the same language-specific OSCAR data."

    The language-related neuron sets (Eq. 2) are selected because removing the neuron changes the output embedding on those exact 1000 sentences (Eq. 1). Their 'functional contribution' is then quantified as perplexity change on the same sentences. A neuron chosen for large embedding-change on a corpus will, on average, also produce large perplexity change on that same corpus, so the Figure 4 finding that shared/exclusive deactivation degrades performance while random deactivation does not is partly guaranteed by the selection rule rather than independently discovered; random neurons were not selected for impact. This inflates the importance ratio (Eq. 5) and the Language Agnostic Score (Eq. 6).

full rationale

The paper's main derivation chain is not globally circular: shared/exclusive sets are defined by activation thresholds (Eqs. 1-3), the importance ratio (Eq. 5) is an empirical ablation comparison, and the language-agnostic score (Eq. 6) is a well-defined aggregate of that comparison. The reported cross-generation increase in shared-neuron importance and the correlation with MMMLU/MGSM are external to the neuron-selection procedure, and the Section 4 training experiments are validated on held-out benchmarks. The self-citations to Zhao et al. (2024a) and Wang et al. (2025a) provide the parallel detection method but are not load-bearing for the paper's central claim, and no uniqueness theorem is imported. The unspecified threshold sigma in Eq. (1) and the Appendix A layer-wise proxy are serious reproducibility/correctness concerns but are not circularity per se. The one genuine circular element is that neurons are selected for output impact on the OSCAR corpus and then their importance is measured as perplexity impact on the same corpus; this makes the claim that the identified neurons are 'indeed specialized for language processing' partly true by construction. Because the shared-vs-exclusive trend and the training results are not fully reducible to this selection, the overall circularity is partial rather than total.

Assumptions & free parameters 1 free parameters · 3 assumptions · 1 invented entities

The paper's central claims rest on one unspecified threshold, an interpretative leap from neuron importance to abstract thought, and a temporal proxy that is confounded with model scale and data changes. The invented entity of language-agnostic neurons has no independent falsifiable handle outside the paper's own definitions.

free parameters (1)
  • Activation threshold sigma = not reported
    Eq. (1) defines a neuron as activated if removal changes the output embedding by at least sigma; no value, selection rule, or sensitivity analysis is provided, and sigma controls the membership of Nshared and Nexclusive, hence every downstream metric.
assumptions (3)
  • domain assumption Neuron removal impact on embeddings identifies causally meaningful language-processing units.
    Inherited from Frankle and Carbin (2018); Tang et al. (2024); Zhao et al. (2024a); the paper uses it without independently validating that the identified rows/columns are the actual loci of language processing.
  • domain assumption The release-date ordering of models represents 'continued development' independent of scale, data mix, and architecture changes.
    Section 3.2 interprets the trend across Qwen1.5/2/2.5 and Llama versions as temporal emergence, but model scales and training data also change.
  • ad hoc to paper Higher per-neuron perplexity impact of shared versus exclusive neurons implies abstract, language-agnostic thought.
    Section 3.3, Eq. (5)-(6) name this ratio the Language Agnostic Score; the interpretation that it indexes abstract cognition is introduced by this paper and is not independently established.
invented entities (1)
  • Language-Agnostic Neurons / core language-agnostic parameter space
    purpose: Posited as the neural substrate of abstract thought that generalizes across languages; used to justify targeted training of exclusive versus shared neurons.
    No external prediction or intervention beyond the paper's own metrics identifies this subspace; the entity is defined by the same importance metric used to measure it.

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Pith. "Pith review of The Emergence of Abstract Thought in Large Language Models Beyond Any Language." pith.science (2026). https://pith.science/paper/6SOSWDQS

@misc{pith2026250609890,
  author       = {Pith},
  title        = {Pith review of: The Emergence of Abstract Thought in Large Language Models Beyond Any Language},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/6SOSWDQS}},
  note         = {Machine review of arXiv:2506.09890}
}
read the original abstract

As large language models (LLMs) continue to advance, their capacity to function effectively across a diverse range of languages has shown marked improvement. Preliminary studies observe that the hidden activations of LLMs often resemble English, even when responding to non-English prompts. This has led to the widespread assumption that LLMs may "think" in English. However, more recent results showing strong multilingual performance, even surpassing English performance on specific tasks in other languages, challenge this view. In this work, we find that LLMs progressively develop a core language-agnostic parameter space-a remarkably small subset of parameters whose deactivation results in significant performance degradation across all languages. This compact yet critical set of parameters underlies the model's ability to generalize beyond individual languages, supporting the emergence of abstract thought that is not tied to any specific linguistic system. Specifically, we identify language-related neurons-those are consistently activated during the processing of particular languages, and categorize them as either shared (active across multiple languages) or exclusive (specific to one). As LLMs undergo continued development over time, we observe a marked increase in both the proportion and functional importance of shared neurons, while exclusive neurons progressively diminish in influence. These shared neurons constitute the backbone of the core language-agnostic parameter space, supporting the emergence of abstract thought. Motivated by these insights, we propose neuron-specific training strategies tailored to LLMs' language-agnostic levels at different development stages. Experiments across diverse LLM families support our approach.

Figures

Figures reproduced from arXiv: 2506.09890 by the authors.

Figure 1
Figure 1. (Top) The trendline of shared neu￾ron proportion rises with model release date (see Section 2.2). (Bottom) The trendline of shared neuron importance also grows, in￾dicating their increasing language-agnostic property (see Section 2.3). In this work, we explore the existence and develop￾ment of abstract thought in LLMs by analyzing how individual neurons, responsible for models’ thinking, respond to multilingual quer… view at source ↗
Figure 2
Figure 2. Neuron distribution across language-shared and language-exclusive neurons for six lan [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. The relationship between multilingual ability and shared neuron ratio (as defined in Equation [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figures from the paper (2 more)
Figure 4
Figure 4. Figure 4: Perplexity changes caused by deactivating random neuron [PITH_FULL_IMAGE:figures/full_fig_p006_4.png]
Figure 5
Figure 5. Figure 5: The relationship between multilingual ability and language-agnostic score (as defined in [PITH_FULL_IMAGE:figures/full_fig_p007_5.png]

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Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.