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