PULSE

Incorporating three innovative features based on recent research into steering controls, Pulse integrates sensor learning for recognizing actuation using conditioning vectors; furthermore, modulation of probabilities is applied such that greater confidence in uncertain model performance leads to stronger control actions; additionally, adjustments vary among different levels which each are independently learned and scaled.

At sensor levels, PULSE determines similarity through latent states compared against learned condition vectors using cosine similarity. When similarity surpasses a previously set threshold level, activation occurs. Subsequently adjustments can be made for steering intensities according to entropy produced from the model; strong activation happens where there is greater uncertainty (higher entropy) among model results.

Successive training using Controlled Activation Alignment (CAA) forms layer upon layer for steering actions alongside inclusion of an explicit optimization phase that focuses on tuning conditioning vectors; such vectors are refined to precisely differentiate high quality positives meant to activate steering mechanisms from negatives which do not activate properly; finally an appropriate threshold value is determined through extensive search through grids with intent to boost performance related to classifying assignments. Emphasis preserved on avoiding task failure by intelligent systems throughout rephrasing.

• Conditional Steering: When you only want to steer on certain types of inputs
• Uncertainty-aware: When steering intensity should depend on model confidence
• Multi-layer Effects: When behavior requires steering at multiple layers
• Dynamic Intensity: When fixed steering strength isn't appropriate for all inputs
• Minimize False Positives: When it's important not to steer on harmless inputs