Glossary · Beta-Bernoulli Utility

Beta-Bernoulli Utility.

Score memories by whether they helped.

Direct answer

Each memory node carries a Beta(α, β) posterior over its usefulness; observed turn feedback updates it, and the mean û=α/(α+β) weights retrieval and packing. Nodes that actually helped get served more; unused ones fade even before decay. This closes the learning loop that flat files and additive memories lack: storage is not value, demonstrated help is.

In Anvaya

How We Implement It.

01Beta-Bernoulli UtilityAnvaya's Feedback service records TurnFeedback per served node; Gravitas multiplies outcome × decay × links × (0.5+0.5·û) at retrieval.

Questions

Asked About Beta-Bernoulli Utility.

Q

What counts as positive feedback?

The served node appearing in successful tool calls, accepted edits, or passing tests downstream, recorded automatically per turn.

Q

Why Beta-Bernoulli specifically?

Conjugate, online, interpretable: α/β count helps vs misses, the mean is a calibrated probability, uncertainty shrinks with evidence.

Q

Does utility override recency?

They multiply: a high-utility old node can outrank a fresh unproven one, exactly the senior-engineer behavior you want.

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