Affect-Modulated Inference Integration
by Nick Clark | Published March 27, 2026
Agent affective state influencing how LLM-proposed mutations are evaluated, accepted, rejected, or queued within the mutation evaluation pipeline.
What It Is
When an LLM proposes mutations through the inference pipeline, the agent's affective state influences how those proposals are evaluated, accepted, rejected, or queued. Higher risk sensitivity raises the evaluation bar for LLM proposals. Elevated novelty appetite may lower it for proposals that introduce genuinely new approaches. The affective modulation applies within the mutation evaluation pipeline, between proposal generation and validation.
This integration does not give the LLM access to the affective state or allow it to manipulate affect.
Why It Matters
LLM proposals arrive without context about the agent's experiential state. A proposal that is reasonable in normal conditions might be inappropriate for an agent in a cautious state after repeated failures. Affective modulation of inference evaluation ensures that the agent's accumulated experience gates what it accepts from external proposal generators.
This is a structural implementation of the principle that the model proposes and the agent decides, where the agent's decision is informed by its emotional experience.
How It Works Structurally
The mutation evaluation pipeline receives LLM proposals as candidate mutations. Before validation, the evaluation function computes a modulated acceptance threshold based on the current affective field. The threshold modifier is computed from relevant affect dimensions weighted according to the proposal type.
Proposals that fall below the modulated threshold are either rejected or queued for later re-evaluation when the affective state may have changed. The affective modulation and its effect on each proposal are recorded in lineage.
What It Enables
LLM integration that respects the agent's experiential context. Agents that have learned to be cautious about certain proposal types will automatically apply higher scrutiny to similar proposals in the future, producing adaptive filtering behavior.
Safety in human-facing applications where LLM proposals must be filtered through the agent's accumulated interaction experience before reaching the user.
