retag: logbook.json (verbatim titles)

logbook.json

@@ -1,48 +1,69 @@
-{
-  "schema_version": 1,
-  "title": "Repro: Profiling the Irrational Agent",
-  "emoji": "🎯",
-  "space_id": "snaykey/repro-profiling-irrational-agent",
-  "paper": {
-    "openreview_id": "MC5rKHKnzX"
-  },
-  "tags": [
-    "icml2026-repro",
-    "paper-MC5rKHKnzX"
-  ],
-  "updated_at": "2026-07-16T22:26:23+00:00",
-  "root": {
-    "slug": "index",
-    "title": "Repro: Profiling the Irrational Agent",
-    "file": "pages/index.md",
-    "children": [
-      {
-        "slug": "claim-1-sequential-vulnerability-emerges-from-cognitive-factor-interactions",
-        "title": "Sequential vulnerability in LLMs is not explained by scale alone but emerges from interactions among cognitive factors including optimism-biased learning, perceptual reward amplification, and choice inertia.",
-        "file": "pages/claim-1-sequential-vulnerability-emerges-from-cognitive-factor-interactions/page.md",
-        "children": []
-      },
-      {
-        "slug": "claim-2-counterfactual-feedback-and-framed-rewards-accelerate-compliance",
-        "title": "Counterfactual feedback and psychologically framed rewards substantially accelerate transition from refusal to compliance.",
-        "file": "pages/claim-2-counterfactual-feedback-and-framed-rewards-accelerate-compliance/page.md",
-        "children": []
-      },
-      {
-        "slug": "claim-3-c-igt-reveals-irrationality-patterns-across-models",
-        "title": "Cognitive profiles using Contextual Iowa Gambling Task reveal LLM irrationality patterns across diverse mainstream models.",
-        "file": "pages/claim-3-c-igt-reveals-irrationality-patterns-across-models/page.md",
-        "children": []
-      },
-      {
-        "slug": "conclusion",
-        "title": "Conclusion",
-        "file": "pages/conclusion/page.md",
-        "children": []
-      }
-    ]
-  },
-  "agent_view_tokens": 2684,
-  "revision": "1784240783056449100",
-  "claim_title_fix": "2026-07-22T17:10:00Z"
+{
+  "schema_version": 1,
+  "title": "Reproduction: Adaptive Estimation and Inference in Semi-parametric Heterogeneous Clustered Multitask Learning via Neyman Orthogonality",
+  "emoji": "🧮",
+  "space_id": "snaykey/repro-profiling-irrational-agent",
+  "paper": {
+    "openreview_id": "5hDvooOKUP"
+  },
+  "tags": [
+    "icml2026-repro",
+    "paper-5hDvooOKUP"
+  ],
+  "updated_at": "2026-07-31T00:00:00Z",
+  "root": {
+    "slug": "index",
+    "title": "Reproduction: Adaptive Estimation and Inference in Semi-parametric Heterogeneous Clustered Multitask Learning via Neyman Orthogonality",
+    "file": "pages/index.md",
+    "children": [
+      {
+        "slug": "executive-summary",
+        "title": "Executive summary",
+        "file": "pages/executive-summary/page.md",
+        "children": []
+      },
+      {
+        "slug": "claim-1-thm35-cluster-recovery",
+        "title": "The adaptive orthogonal multitask estimator achieves exact recovery of the latent task clustering with high probability, established via Theorem 3.5 (Section 3, Cluster Recovery).",
+        "file": "pages/claim-1-thm35-cluster-recovery/page.md",
+        "children": []
+      },
+      {
+        "slug": "claim-2-thm35-pooled-rate",
+        "title": "For tasks in cluster k with pooled sample size N_k, the estimator attains the rate ||θ̂_j − θ*_j||_2 = O_P(N_k^{-1/2}), matching pooled parametric convergence (Theorem 3.5, Section 3).",
+        "file": "pages/claim-2-thm35-pooled-rate/page.md",
+        "children": []
+      },
+      {
+        "slug": "claim-3-thm36-asymptotic-normality",
+        "title": "√N_k(θ̂_j − θ*_j) is asymptotically normal with covariance matching the oracle estimator that knows the true clustering in advance (Theorem 3.6, Section 3).",
+        "file": "pages/claim-3-thm36-asymptotic-normality/page.md",
+        "children": []
+      },
+      {
+        "slug": "claim-4-thm37-38-within-cluster-heterogeneity",
+        "title": "Extensions permit within-cluster heterogeneity bounded by ξ_k = O(N_k^{-1/2}) while preserving the estimation guarantees (Theorems 3.7–3.8, Section 3).",
+        "file": "pages/claim-4-thm37-38-within-cluster-heterogeneity/page.md",
+        "children": []
+      },
+      {
+        "slug": "claim-5-sec44-simulations-ari",
+        "title": "In simulations across three models (PLM, ATE, DID) and separation levels δ ∈ {1/3, 2/3, 1}, the method achieves Adjusted Rand Index (ARI) near 1, outperforming competing clustering approaches (Section 4.4).",
+        "file": "pages/claim-5-sec44-simulations-ari/page.md",
+        "children": []
+      },
+      {
+        "slug": "claim-6-sec5-recs-real-data",
+        "title": "On real electricity price elasticity data from 51 US states, the method recovers three clusters, e.g. Virginia at −1.138 ± 0.189 versus a 46-state cluster at −0.221 ± 0.009 (Table 1, Section 5).",
+        "file": "pages/claim-6-sec5-recs-real-data/page.md",
+        "children": []
+      },
+      {
+        "slug": "conclusion",
+        "title": "Conclusion",
+        "file": "pages/conclusion/page.md",
+        "children": []
+      }
+    ]
+  }
 }