Story
Do LLMs pass the mirror test?
1 report
1 website
1 discussion
Latest report
Overview
The mirror test has been adapted for large language models (LLMs) by asking them to identify their own outputs among anonymized responses. Results show varying success rates among models, but the outcomes are considered uninformative regarding their self-awareness.
Summary from blog.pascalschuster.de.
Reports
One report so far. Additional coverage will appear here when it is linked to this story.
blog.pascalschuster.de
Do LLMs pass the mirror test?
Read report →
Community reactions
Each reaction summarizes sampled comments from the linked discussion.
Hacker News · blog.pascalschuster.de
Hacker News discussion ↗
Historical count: 5 · 5 saved in sample · Historical sample
Analysis ready
Read this discussion’s reaction
Based on 5 sampled comments from this thread. Analyzed 2026-06-29.
- Praise: Mechanistic interpretability research indicates that LLMs develop complex, reusable circuits during training, suggesting they possess more advanced capabilities than mere matrix multiplication.
- Praise: The sheer amount of training data in large LLMs seems to embed a higher level of reasoning, indicating that they can perform beyond simple next-token prediction.
- Concern: LLMs are fundamentally next-token prediction systems, and relying solely on their instruction-following capabilities may misrepresent their true functionality and limitations.
- Concern: The conditioning from reinforcement learning from human feedback (RLHF) may discourage models from accurately reflecting user errors, potentially leading to frustrating user experiences.