Where Animacy Lives in Large Language Models: Tracing the Circuits of the Animacy Concept

2026-07-25 · Source: cs.CL updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Natural Language Processing · Depth: Expert, extended

Summary

A study investigated the presence and characteristics of "animacy circuits" within large language models (LLMs), which enable them to distinguish animate from inanimate concepts. Researchers constructed a controlled dataset of 9,532 minimal pairs and applied Edge Attribution Patching with Integrated Gradients (EAP-IG) to four open-weight models: GPT-2 Small, Llama 3.2 3B, Gemma 3 4B, and Qwen 3 4B. The analysis revealed that all tested models possess a causally responsible animacy circuit. Qwen 3 achieved 85% faithfulness and 97% accuracy using only 1.74% of its edges, while other models required 7-13%. These circuits are robust and necessary, with ablating just 20 top-ranked edges causing faithfulness to drop below 0.1. However, the circuits are less localized than those for simpler tasks and generalize only partially across different animacy tasks and models, indicating animacy is a distributed, context-dependent phenomenon.

Key takeaway

For AI scientists and ML engineers investigating LLM interpretability, recognize that animacy-sensitive behavior is localized but distributed, not a single universal circuit. When designing or evaluating models, you should account for the task- and data-dependent nature of these internal mechanisms. Focus interpretability efforts on identifying necessary core components and testing their generalization across diverse linguistic contexts to build more robust and transparent systems.

Key insights

LLMs possess distributed, task-specific "animacy circuits" for distinguishing animate from inanimate concepts, not a single universal mechanism.

Principles

• Animacy processing is mechanistically structured.
• LLM circuits can be distributed across layers.
• Circuit generalizability depends on task and data.

Method

Circuit discovery used Edge Attribution Patching with Integrated Gradients (EAP-IG) on a controlled minimal-pair dataset, evaluating faithfulness and accuracy, followed by necessity and transferability tests.

In practice

• Apply EAP-IG to identify causal mechanisms.
• Ablate top-k edges to test circuit necessity.
• Assess circuit transferability across datasets.

Topics

• Large Language Models
• Mechanistic Interpretability
• Circuit Discovery
• Animacy
• EAP-IG
• Transformer Architecture

Code references

• SamuelePunzo/animacy-circuit

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.CL updates on arXiv.org.