I Tried to Turn Agent Memory Authority Into a Scoring Formula. The Held-Out Test Changed the Claim.
The article discusses the development of a scoring formula aimed at improving retrieval systems in AI by incorporating authority signals. Initially, the system relied solely on relevance, which led to issues when semantically relevant distractors were selected over authoritative memories. The new formula introduces various weights to better reflect authority, aiming to enhance the accuracy of retrieval outcomes.
- ▪The original retrieval system selected memories based on relevance, which failed in adversarial queries.
- ▪A new scoring model was developed to incorporate authority into the retrieval process.
- ▪The formula includes multiple weights that adjust the relevance score based on authority signals.
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try { if(localStorage) { let currentUser = localStorage.getItem('current_user'); if (currentUser) { currentUser = JSON.parse(currentUser); if (currentUser.id === 3948231) { document.getElementById('article-show-container').classList.add('current-user-is-article-author'); } } } } catch (e) { console.error(e); } Self-Correcting Systems Posted on Jun 3 I Tried to Turn Agent Memory Authority Into a Scoring Formula. The Held-Out Test Changed the Claim. #ai #machinelearning #agentmemory #security A few articles back, a good friend asked a question I could not deflect. He had read the earlier posts in this series — the authority policy, the access gate, the capstone framework map — and his response was direct: Where is the math? Where is the model? You have described the problem.
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