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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Story provenance
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Record
| Original publisher | DEV.to (Top) |
| Canonical URL | https://dev.to/zep1997/i-tried-to-turn-agent-memory-authority-into-a-scoring-formula-the-held-out-test-changed-the-claim-4aam |
| Publication time | Wed, 03 Jun 2026 02:40:50 +0000 |
| Retrieval time | 2026-06-03T03:11:49.579Z |
| Last seen | 2026-06-03T03:11:49.579Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | UWZUKPa_02jp |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
WeSearch handling by dimension
| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
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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Excerpt limited to ~120 words for fair-use compliance. The full article is at DEV.to (Top).
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