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ZENODO
Preprint . 2025
License: CC BY NC
Data sources: ZENODO
ZENODO
Preprint . 2025
License: CC BY NC
Data sources: Datacite
ZENODO
Preprint . 2025
License: CC BY NC
Data sources: Datacite
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Digital Mirror for LLMs: A Phenomenological Reflection of Language-Model Output

Authors: Seidl, Michal;

Digital Mirror for LLMs: A Phenomenological Reflection of Language-Model Output

Abstract

This work introduces the concept of a digital mirror as an external mechanism for phenomenological reflection of outputs produced by large language models (LLMs). Unlike common introspective approaches that attempt to induce self-reflection through internal states, verbal self-critique, or feedback loops integrated into the generation process, the digital mirror operates exclusively at the level of the finalized model output. The approach is inspired by an optical analogy: rather than “looking inward,” the model observes its own expression as an external object. We formally define a mirror function f(Ot; u, C, λ) composed of surface extraction, projection into an observer-dependent perceptual space, mirror inversion along a chosen axis, and rendering of the reflection. This framework separates externally observable behavioral phenomena from internal generative mechanisms and opens a space for new forms of output calibration, style self-regulation, ethical reflection, and experimental analysis of LLM behavior. Implementation notes and a minimal prototype demonstrate practical feasibility. The digital mirror is discussed as a modular and extensible apparatus with the potential to contribute to safer, more consistent, and more interpretable language models.

Keywords

AI Alignment, LLM, Large Language Models, AI, AI Safety, NLP, Digital Mirror

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average