{ "schema_version": 1, "title": "Repro: Deep sequence models tend to memorize geometrically; it is unclear why", "emoji": "🎯", "space_id": "latticetower/repro-geometric-memory", "paper": { "arxiv_id": "2510.26745", "openreview_id": "CT2tSmahVQ", "title": "Deep sequence models tend to memorize geometrically; it is unclear why", "github": "https://github.com/Shahriarnz14/Geometric_Memory" }, "tags": [ "icml2026-repro", "paper-CT2tSmahVQ" ], "updated_at": "2026-07-18T16:23:42+00:00", "root": { "slug": "index", "title": "Repro: Deep sequence models tend to memorize geometrically; it is unclear why", "file": "pages/index.md", "children": [ { "slug": "claim-1-near-perfect-in-weights-path-star-accuracy-at-scale", "title": "Claim 1: Near-perfect in-weights path-star accuracy at scale", "file": "pages/claim-1-near-perfect-in-weights-path-star-accuracy-at-scale/page.md", "children": [] }, { "slug": "claim-2-contradicts-associative-memory-exponential-capacity-prediction", "title": "Claim 2: Contradicts associative-memory exponential-capacity prediction", "file": "pages/claim-2-contradicts-associative-memory-exponential-capacity-prediction/page.md", "children": [] }, { "slug": "claim-3-geometric-embedding-structure-heatmap-umap", "title": "Claim 3: Geometric embedding structure (heatmap + UMAP)", "file": "pages/claim-3-geometric-embedding-structure-heatmap-umap/page.md", "children": [] }, { "slug": "claim-4-spectral-bias-fiedler-vector-alignment-node2vec", "title": "Claim 4: Spectral bias / Fiedler-vector alignment (Node2Vec)", "file": "pages/claim-4-spectral-bias-fiedler-vector-alignment-node2vec/page.md", "children": [] }, { "slug": "conclusion", "title": "Conclusion", "file": "pages/conclusion/page.md", "children": [] } ] }, "agent_view_tokens": 7345, "revision": "1784391822522520000" }