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retag: logbook.json (verbatim titles)

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  1. logbook.json +31 -27
logbook.json CHANGED
@@ -1,55 +1,61 @@
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  {
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  "schema_version": 1,
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- "title": "FlashOptim: Optimizers for Memory-Efficient Training",
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- "emoji": "🎯",
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  "space_id": "snaykey/repro-flashoptim",
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  "paper": {
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- "openreview_id": "Wfe1iJocjF"
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  },
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  "tags": [
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  "icml2026-repro",
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- "paper-Wfe1iJocjF"
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  ],
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- "updated_at": "2026-07-20T18:01:07+00:00",
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  "root": {
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  "slug": "index",
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- "title": "FlashOptim: Optimizers for Memory-Efficient Training",
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  "file": "pages/index.md",
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  "children": [
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  {
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- "slug": "claim-1",
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- "title": "Claim 1: Master-weight splitting + companded 8-bit quantization mechanism",
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- "file": "pages/claim-1/page.md",
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  "children": []
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  },
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  {
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- "slug": "claim-2",
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- "title": "Claim 2: Table 1 byte accounting (16 to 7 to 5 bytes/param)",
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- "file": "pages/claim-2/page.md",
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  "children": []
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  },
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  {
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- "slug": "claim-3",
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- "title": "Claim 3: Llama-3.1-8B peak memory 175 to 113 GiB",
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- "file": "pages/claim-3/page.md",
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  "children": []
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  },
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  {
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- "slug": "claim-4",
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- "title": "Claim 4: Training-loss trajectory matching (GPT-2 / ResNet-50)",
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- "file": "pages/claim-4/page.md",
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  "children": []
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  },
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  {
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- "slug": "claim-5",
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- "title": "Claim 5: Benchmark scores (ResNet-50 val acc / GSM8K / GPT-2 ICL)",
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- "file": "pages/claim-5/page.md",
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  "children": []
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  },
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  {
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- "slug": "claim-6",
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- "title": "Claim 6: ULP weight splitting error + companding anti-divergence",
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- "file": "pages/claim-6/page.md",
 
 
 
 
 
 
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  "children": []
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  },
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  {
@@ -59,7 +65,5 @@
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  "children": []
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  }
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  ]
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- },
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- "agent_view_tokens": 7777,
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- "revision": "1784570467807071400"
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  }
 
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  {
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  "schema_version": 1,
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+ "title": "Reproduction: All ERMs Can Fail in Stochastic Convex Optimization (Lower Bounds in Linear Dimension)",
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+ "emoji": "📉",
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  "space_id": "snaykey/repro-flashoptim",
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  "paper": {
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+ "openreview_id": "Lzwp2KXedc"
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  },
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  "tags": [
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  "icml2026-repro",
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+ "paper-Lzwp2KXedc"
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  ],
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+ "updated_at": "2026-07-30T00:00:00Z",
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  "root": {
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  "slug": "index",
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+ "title": "Reproduction: All ERMs Can Fail in Stochastic Convex Optimization (Lower Bounds in Linear Dimension)",
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  "file": "pages/index.md",
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  "children": [
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  {
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+ "slug": "executive-summary",
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+ "title": "Executive summary",
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+ "file": "pages/executive-summary/page.md",
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  "children": []
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  },
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  {
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+ "slug": "claim1-thm1-all-erms-fail-linear-dim",
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+ "title": "In dimension d = 6m, there exists a stochastic convex optimization instance where every ε-ERM solution with ε = Θ(m^{-3/2}) incurs constant excess risk and fails to generalize (Theorem 1).",
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+ "file": "pages/claim1-thm1-all-erms-fail-linear-dim/page.md",
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  "children": []
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  },
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  {
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+ "slug": "claim2-thm2-strongly-convex-rate",
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+ "title": "For λ-strongly convex losses with m^{-3/2} ≤ λ ≤ m^{-1/2}, any ε-ERM solution w_S satisfies F(w_S) − min F(w) ≥ Ω(1/(λ m^{3/2})) (Theorem 2).",
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+ "file": "pages/claim2-thm2-strongly-convex-rate/page.md",
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  "children": []
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  },
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  {
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+ "slug": "claim3-cor3-gd-constant-error",
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+ "title": "When ηT = Ω(m^{3/2}), gradient descent incurs constant generalization error with high probability, as a corollary of the ERM lower bound (Corollary 3).",
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+ "file": "pages/claim3-cor3-gd-constant-error/page.md",
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  "children": []
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  },
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  {
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+ "slug": "claim4-thm4-gd-excess-risk-rate",
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+ "title": "Gradient descent's excess risk is lower-bounded by F(w_S^{GD}) − min F(w) = Ω(min{√(ηT/m^{3/2}), 1}) (Theorem 4).",
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+ "file": "pages/claim4-thm4-gd-excess-risk-rate/page.md",
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  "children": []
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  },
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  {
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+ "slug": "claim5-thm4-narrowing-gap",
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+ "title": "This new generalization lower bound of Ω(ηT + √(ηT/m^{3/2})) for constrained gradient descent narrows the previously exponential gap to the best known upper bound of O(ηT + ηT/m) (Theorem 4).",
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+ "file": "pages/claim5-thm4-narrowing-gap/page.md",
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+ "children": []
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+ },
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+ {
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+ "slug": "claim6-construction-code-link-function",
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+ "title": "The construction uses Feldman's asymptotically good binary code combined with a novel 'link function' mapping sample information to bad ERM solutions while preserving convexity, giving the first proof that all approximate ERMs fail in linear dimension (Section on technical construction / proof of Theorem 1).",
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+ "file": "pages/claim6-construction-code-link-function/page.md",
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  "children": []
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  },
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  {
 
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  "children": []
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  }
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  ]
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+ }
 
 
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  }