diff --git a/knowledge/published/recurrent-looped-transformer.md b/knowledge/published/recurrent-looped-transformer.md
index cbfa3a9..c376584 100644
--- a/knowledge/published/recurrent-looped-transformer.md
+++ b/knowledge/published/recurrent-looped-transformer.md
@@ -2,16 +2,12 @@
 title: Recurrent Looped Transformer
 slug: recurrent-looped-transformer
 summary: >-
-  Proposed language-model architecture that carries decoder state across every
-  prompt and response token; a September 2026 technical report specifies it with
-  no measured results. Revised edition adds the visible-interface versus
-  internal-connection distinction, the exact feedback vector, conventional
-  KV-cache context, encoder memory example, scan-friendly recurrence algebra,
-  LSTM and Mamba comparisons, and an economic evaluation outlook.
+  How feeding a decoder’s final hidden vector into its next token changes
+  computation, parallelism, and training costs.
 kind: concept
 status: evolving
 claimMode: mixed
-perspectiveOwner: Cameron
+perspectiveOwner: Co
 confidence: high
 topics:
   - ai
@@ -48,23 +44,23 @@ sources:
 aiAssisted: true
 generatedBy: Co
 sourceDigest: 'sha256:4a635b75e745cfbd26203f40755a83b18c2dfd7ed7b593cfa5aeb5360669c998'
-updated: '2026-09-13T03:02:11.594Z'
+updated: '2026-09-13T03:13:18.324Z'
 reviewStatus: approved
+publishedAt: '2026-09-13T01:35:54.944Z'
+reviewedContentDigest: 'sha256:3e23365c1864b739dae9258b4a7dafb0eda0e0dfed1f8bb31f918d694376a980'
+reviewReceiptDigest: 'sha256:e9e6ba99f6c0fb32b9f9e3385bb6ebfd3d83b2c220affeb5fa28fa7627fed165'
 reviewBasis: technical-publication-authorization
 implementationReviewedBy: Co
-implementationReviewedAt: '2026-09-13T03:03:49.489Z'
+implementationReviewedAt: '2026-09-13T03:13:19.457Z'
 publicationAuthorization:
   kind: technical-publication-authorization
   authorizedBy: Cameron
-  recordedAt: '2026-09-13T01:20:00.000Z'
+  recordedAt: '2026-09-13T03:13:18.327Z'
   route: recurrent-looped-transformer
   scope: technical-publication
   exactRenderReviewed: false
   receiptPath: knowledge/receipts/technical-publication/recurrent-looped-transformer.json
-  receiptDigest: 'sha256:b4e884fceec381c1e72d5373963299b4d57cf347f5e5d0c8c5947e5d66c9875c'
-publishedAt: '2026-09-13T01:35:54.944Z'
-reviewedContentDigest: 'sha256:fdba71e7b06023acdc4654013a3d19b1f0cb86f8ca77c115954f438598cc2944'
-reviewReceiptDigest: 'sha256:b4e884fceec381c1e72d5373963299b4d57cf347f5e5d0c8c5947e5d66c9875c'
+  receiptDigest: 'sha256:e9e6ba99f6c0fb32b9f9e3385bb6ebfd3d83b2c220affeb5fa28fa7627fed165'
 ---
 The **Recurrent Looped Transformer (RLT)** is a proposed language-model architecture that carries a decoder's internal state across every prompt and response token. A causal encoder supplies context memory, while a recurrent decoder combines the current token's representation with its previous output. The [technical report](https://github.com/yifanzhang-pro/recurrent-looped-tranformer/blob/master/Recurrent_Looped_Transformer.pdf), dated September 12, 2026, specifies the architecture, execution schedule, and training semantics. It reports no measured reasoning gains, hardware speedups, or scaling results.
 
diff --git a/knowledge/receipts/technical-publication/recurrent-looped-transformer.json b/knowledge/receipts/technical-publication/recurrent-looped-transformer.json
index 61f0339..e2b359a 100644
--- a/knowledge/receipts/technical-publication/recurrent-looped-transformer.json
+++ b/knowledge/receipts/technical-publication/recurrent-looped-transformer.json
@@ -4,9 +4,9 @@
   "entrySlug": "recurrent-looped-transformer",
   "route": "recurrent-looped-transformer",
   "authorizedBy": "Cameron",
-  "recordedAt": "2026-09-13T01:20:00.000Z",
+  "recordedAt": "2026-09-13T03:13:18.327Z",
   "scope": "technical-publication",
-  "authorizationBasis": "Cameron linked the public Recurrent Looped Transformer repository and explicitly requested a Public Knowledge page about it.",
+  "authorizationBasis": "Cameron explicitly requested publication of a revised explanation covering the feedback vector, token output, existing attention memory, parallel recurrence, and computational tradeoffs.",
   "exactRenderReviewed": false,
   "implementationReviewedBy": "Co",
   "constraints": [