Modular Cognitive Architecture Emerges in Large Language Models

Abstract

The human brain exhibits a striking degree of functional specialization, with distinct networks supporting language, formal reasoning, reasoning about other minds, and reasoning about the physical world. Is this modular organization a fundamental principle of how intelligent systems must be built, or an evolutionary accident specific to biological brains? Here, we test whether a similar organization emerges in Large Language Models, another class of intelligent systems created through a very different optimization process. Using circuit analyses across N = 46 tasks spanning four cognitive domains (language, formal reasoning, social reasoning, physical reasoning), we find that LLMs develop a modular architecture that mirrors the human brain: tasks drawing on the same network in humans recruit overlapping neurons in LLMs, whereas tasks drawing on different networks recruit distinct neurons. The convergent emergence of modularity in brains and neural networks suggests that it may be a fundamental property of intelligent systems.

46 reasoning tasks across four cognitive domains

Language · Formal · Physical · Social

Tasks sharing a brain network recruit the same neurons

4.3× more overlap within-domain · ARI = 0.78

Ablating a domain's neurons selectively breaks it

10.3× larger accuracy drop within- than cross-domain

The same structure holds in every model

6 frontier LLMs · 24B → 123B

MethodLocalizing the units that support each task

We localize task-supporting units with attribution patching. For each of 46 tasks across four cognitive domains we build minimal original/alternative input pairs whose correct continuation flips. A unit's importance is its original-vs-alternative activation difference times the gradient of the original−alternative logit difference, summed over examples. We then quantify modular organization from the pairwise overlap of each task's top-0.1% units, and validate it causally by ablating those units and measuring cross-task transfer. Six instruction-tuned LLMs (24B–123B, four families) are analyzed.

Overview: 46 tasks across four cognitive domains with example counts, each mapped to a human brain network; the attribution-patching pipeline; and example original/alternative problem pairs per domain. — **Figure 1. Identifying domain-specific functional organization in large language models.** **(A)** The meta-dataset: **46 tasks** spanning four cognitive domains: Language (8 tasks, 8,877 pairs), Formal reasoning (20 tasks, 19,941 pairs), Physical reasoning (9 tasks, 9,200 pairs), and Social reasoning (9 tasks, 11,412 pairs). Each domain is grounded in a well-characterized functional network of the human brain: the language network, the multiple-demand network, the intuitive-physics network, and the theory-of-mind network, respectively. **(B)** Pipeline. (1) For each task we construct minimal contrastive pairs of original and alternative inputs that elicit opposite correct continuations (here, addition vs. subtraction). (2) We run both inputs through the model and record activations at every MLP neuron. (3) Attribution patching scores each unit by the activation difference times the gradient of the original−alternative logit difference, yielding a causal estimate of its contribution. (4) We measure pairwise overlap of top-attributed units across tasks and validate the structure through causal ablation. **(C)** Example contrastive problems for each domain; original and alternative inputs are matched in surface form so that attribution reflects the reasoning-relevant contrast rather than format.

ExploreThe 46 tasks · one example each

Each task is defined by minimal original / alternative input pairs whose correct continuation flips. Click any task below to see one representative pair: the original prompt and its correct continuation in green, and the alternative prompt with the flipped continuation in red.

One example per task; full datasets (the count shown per task) are in the code repository.

Result · StructuralA modular organization of reasoning systems

Chord diagram of top-0.1% neuron overlap across 46 tasks, averaged over six LLMs, showing four disjoint domain blocks. — **Figure 2. A modular organization of task-selective neurons.** Pairwise overlap of the top-0.1% task-selective neurons across the 46 tasks, averaged over six frontier LLMs. Ribbons connect task pairs that share attributed neurons. The four colored blocks are dense within domains and almost disjoint between them.

Tasks supported by the same brain network in humans are solved by overlapping sets of neurons in the model, whereas tasks that draw on different networks recruit largely separate sets. Averaged across six models, this within-domain overlap exceeds cross-domain overlap by more than fourfold (12.9% vs 3.0%, permutation test p < 0.0001). Unsupervised hierarchical clustering of the 46×46 task matrix recovers the four cognitive domains defined in neuroscience (Adjusted Rand Index = 0.78, p < 0.0001), and the structure is highly consistent across models (mean pairwise Kendall's τ = 0.70 ± 0.06). The same modular organization emerged in six different LLMs, from 24 to 123 billion parameters.

Result · CausalLesioning a domain's neurons selectively breaks that domain

To test causal specificity, we ablate the top-0.1% units identified for a source task and evaluate the model on a different target task. Within-domain ablations cause a 25.9% accuracy drop versus 2.5% for cross-domain ablations (ratio 10.3×, p < 0.0001), consistent across models (Kendall's τ = 0.59 ± 0.05). The asymmetry holds for every domain individually and in both directions of cross-domain ablation.

Per-domain accuracy drop for within-domain ablation versus the two cross-domain directions. — **Figure 3. Causal specificity of task-selective neurons.** Accuracy drop (Δ accuracy) on a target task when the top-0.1% neurons of a source task are ablated, shown for each domain. *Within-Domain* ablations (solid bars) produce far larger drops than either cross-domain direction: ablating a domain's neurons and testing other domains (*D → Others*), or ablating other domains' neurons and testing that domain (*Others → D*). The dissociation holds for every domain individually and in both directions. Error bars: SEM across task pairs.

A qualitative dissociation. Inspecting the models' outputs after targeted ablations reveals a separation between linguistic form and reasoning content. Lesioning the neurons selectively required for the language tasks largely preserved the models' reasoning abilities but introduced syntactic and morphological errors. Conversely, lesioning physical-reasoning neurons led the models to incorrect reasoning and conclusions while preserving the linguistic well-formedness of the output.

Three model responses to a rubber-band physics question: no ablation gives a correct, fluent answer; ablating language neurons keeps the physics correct but breaks grammar; ablating physics neurons keeps grammar fluent but reaches wrong physical conclusions. — **Figure 4. Form and content come apart under ablation.** Responses to a physical-reasoning prompt about how heat aging changes a rubber band's restoring force and recovery. **No ablation:** correct physics and fluent grammar. **Ablate Language neurons:** the physical reasoning stays correct, but the text develops agreement and spelling errors (e.g. *"the polymer chain network that form its structure"*, *"entangment"*), so grammar fails while reasoning holds. **Ablate Physics neurons:** the text stays grammatical but the conclusions invert (claiming a *higher* restoring force and *less* energy dissipated after heat aging), so reasoning fails while grammar holds.

ControlModularity is contingent on task competence

The modular organization is not an artifact of the datasets, the contrastive design, or the attribution pipeline. Running the identical pipeline on GPT-2 (124M), which does not reach above-chance performance on the reasoning tasks, recovers only the broad division between Language and the rest of cognition, not the finer separation among the three reasoning domains. Modularity emerges only where the model can actually solve the tasks.

Per-task both-correct accuracy across the six large models. — **Figure 5. Task competence across models.** Per-task both-correct accuracy for the six large instruction-tuned models across the 46 tasks and four domains (chance = 0.25). Coverage is high and balanced across domains, justifying the circuit analyses above.

Why modularity emerges

A class of intelligent systems shaped by an entirely different process, gradient descent on next-token prediction, develops the same modular organization that characterizes the human brain: language, formal reasoning, physical reasoning, and social reasoning are each supported by largely distinct sets of neurons, while tasks within a domain share them. One influential account of cortical modularity appeals to metabolic cost, the idea that activating fewer neurons per task saves energy. That pressure does not exist in a transformer, whose forward pass carries no metabolic cost and whose loss never penalizes how many neurons are active. Modularity emerges anyway, which suggests this biological constraint is not necessary for functional specialization to arise.

What might drive it instead? When several forms of reasoning must operate on the same input, the system faces pressure to keep those computations from interfering, both so that simultaneous representations stay separable and so that learning one domain does not overwrite another. Allocating distinct neurons to distinct computations protects against both. More broadly, the result shows the value of LLMs as a second kind of intelligent system against which to test claims about the structure of cognition: when a feature of the human mind reappears in a system built so differently, it is more likely to reflect a general principle of intelligence than an accident of biology.

Citation

@article{han2026modular,
  title   = {Modular Cognitive Architecture Emerges in Large Language Models},
  author  = {Han, Pengrui and Andreas, Jacob and Fedorenko, Evelina
             and de Varda, Andrea Gregor},
  journal = {Preprint},
  year    = {2026},
  note    = {Code and data: github.com/Pengrui-Han/LLM_Modularity_Final}
}

Modular Cognitive Architecture Emergesin Large Language Models