Salience Attention Mechanism

Problem

Without an explicit notion of salience, the agent has only two bad strategies. Dumping all of memory into context blows up the token budget and gives the model no focus on what matters now. Taking only the most recent items provides no continuity and misses anything older that has become relevant again because of a surprise in the current context. Recency alone misses the items that matter; bulk loading buries them in noise. The agent needs a way to score every candidate memory by how salient it is to the current moment and to surface only the top-scoring ones into context.

Solution

Score each candidate memory item `m` with a weighted sum: `alpha * novelty(m) + beta * goal_relevance(m) + gamma * recency(m) + delta * prediction_error(m) - epsilon * fatigue(m)`. Pick the top-k into the working set for the next tick. Persist the weights in a tunable config so a reflection pass can adjust them. The fatigue term penalises items that have already been attended to many times in the recent window, breaking rumination loops.

When to use

• The persistent memory store is too large to read in full at every tick.
• Memory items have features (recency, importance, frequency, similarity) that can be combined into a salience score.
• The agent needs predictable per-tick read cost.