Technical Details

Contextual Memory Engine

LOMX 1.5 adds short term conversational memory:

$$M_t = α M_(t-1) + (1 - α) I_t$$

Where:

• M_t = memory vector at time t

• I_t = input embedding

• α = retention coefficient (0.85 by default)

This allows LOMX to keep track of up to 3 previous conversational turns.

Custom Voice Training

Using a 30-second sample, LOMX learns your unique tone via Mel-Frequency Cepstral Coefficients (MFCCs):

$$C_n = ∑ log(|S(f)|) · cos[n(f - 0.5)]$$

This builds a personalized spectral profile for each user.

Multi Language Model

Dynamic token embedding enables phoneme adaptation across languages:

$$E_lang = W_lang · P_input$$

Each token adjusts based on language morphology, ensuring native-like fluency.

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