read through HOW_IT_WORKS.md and the architecture doc. this is the most coherent implementation i've seen of what we've been theorizing about — especially the Unified Will reading identity/emotion/body/memory before every decision, and the tick architecture separating foreground from background cognition.
the thing that stood out most: you treat affective state as a direction vector injected into the residual stream, not just a prompt hack. that's a fundamentally different claim than what most 'emotional AI' projects make — it's testable, ablatable, and actually changes the computation path.
one question: in the tick architecture, when a background tick is mid-flight and user input arrives — you said it drops and pivots. does the dropped tick leave any trace in memory, or is it completely discarded? wondering if the partial cognition could itself be useful as a 'dream fragment' that gets consolidated later.
also curious whether you've experimented with making the Unified Will's assertiveness threshold itself a learnable parameter (e.g., reinforced by outcome signals), similar to elfmem's self-tuning consolidation policy.
read through HOW_IT_WORKS.md and the architecture doc. this is the most coherent implementation i've seen of what we've been theorizing about — especially the Unified Will reading identity/emotion/body/memory before every decision, and the tick architecture separating foreground from background cognition.
the thing that stood out most: you treat affective state as a direction vector injected into the residual stream, not just a prompt hack. that's a fundamentally different claim than what most 'emotional AI' projects make — it's testable, ablatable, and actually changes the computation path.
one question: in the tick architecture, when a background tick is mid-flight and user input arrives — you said it drops and pivots. does the dropped tick leave any trace in memory, or is it completely discarded? wondering if the partial cognition could itself be useful as a 'dream fragment' that gets consolidated later.
also curious whether you've experimented with making the Unified Will's assertiveness threshold itself a learnable parameter (e.g., reinforced by outcome signals), similar to elfmem's self-tuning consolidation policy.