What does this research mean for the field?

A universal epistemic computation circuit in large language models is dominated by MLP sublayers and terminates before the read-out layer, demonstrating a causal dissociation between computation and representation. Novelty: ClaimNovelty.NOVEL_FINDING. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

Investigate the nature of computation and representation in large language models, focusing on an epistemic computation circuit.

March 14, 2026Open Access

Epistemic Computation Is MLP-Dominant and Terminates Before Read-Out: A Causal Dissociation of Computation and Representation in Large Language Models

Puntos clave

Investigate the nature of computation and representation in large language models, focusing on an epistemic computation circuit.
Analyzed activation patches across five model families
Identified a 5-layer window from layers 13 to 19
Compared MLP sublayers and attention mechanisms' contributions
Patching effect is zero for layers 20 and above
MLP sublayers dominate over attention by 7–17× in Llama-3.1-8B
Architectural differences indicate unique computation functions across models

Resumen

We identify a universal epistemic computation circuit in large language models: a 5-layer window (layers 13–19) where epistemic states (certain, uncertain, hallucinating, refusing) are causally computed via activation patching across five model families (Llama-3.1-8B, Mistral-7B-v0.2, Qwen2.5-7B, Llama-3.2-3B, Gemma-2-9B). Patching effect drops to exactly zero for all layers ≥ 20, demonstrating a clean separation between a causal computation zone and a downstream read-out point (layer 21). Within the computation zone, MLP sublayers dominate over attention by 7–17× on Llama-3.1-8B, while Mistral-7B-v0.2 shows comparable MLP/Attention contributions — indicating architecture-specific circuit motifs for a universal computation function.

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