| { |
| "schema_version": 1, |
| "title": "Repro - How much can language models memorize?", |
| "emoji": "🧠", |
| "space_id": "abidlabs/memorize", |
| "paper": { |
| "openreview_id": "bA6BgSbaUi", |
| "arxiv_id": "2505.24832", |
| "icml_id": "27372", |
| "title": "How much can language models memorize?", |
| "claims": [ |
| { |
| "text": "GPT-style transformers trained on uniform random data show an empirical memorization-capacity plateau of about 3.6 bits per parameter (Figure 1)", |
| "status": "in-progress" |
| }, |
| { |
| "text": "Capacity estimates across model widths and depths support a roughly linear bits-per-parameter scaling law, with bfloat16 to float32 increasing capacity only modestly (Table 1)", |
| "status": "in-progress" |
| }, |
| { |
| "text": "On text data, unintended memorization rises with model size but decreases once models begin generalizing relative to an oracle reference model (Figure 2)", |
| "status": "in-progress" |
| }, |
| { |
| "text": "Double descent begins when dataset information content exceeds estimated model capacity in both synthetic bitstrings and text experiments (Figures 3 and 4)", |
| "status": "in-progress" |
| }, |
| { |
| "text": "The paper derives and evaluates scaling-law predictions for membership inference as a function of model capacity and dataset size (Figure 7)", |
| "status": "in-progress" |
| } |
| ] |
| }, |
| "tags": [ |
| "icml2026-repro", |
| "paper-bA6BgSbaUi" |
| ], |
| "updated_at": "2026-07-11T00:04:34+00:00", |
| "root": { |
| "slug": "index", |
| "title": "Repro - How much can language models memorize?", |
| "file": "pages/index.md", |
| "children": [ |
| { |
| "slug": "claim-1-3-6-bits-param-plateau-synthetic", |
| "title": "Claim 1: 3.6 bits/param plateau (synthetic)", |
| "file": "pages/claim-1-3-6-bits-param-plateau-synthetic/page.md", |
| "children": [] |
| }, |
| { |
| "slug": "claim-2-width-depth-scaling-precision", |
| "title": "Claim 2: width/depth scaling + precision", |
| "file": "pages/claim-2-width-depth-scaling-precision/page.md", |
| "children": [] |
| }, |
| { |
| "slug": "claim-3-text-memorization-vs-generalization", |
| "title": "Claim 3: text memorization vs generalization", |
| "file": "pages/claim-3-text-memorization-vs-generalization/page.md", |
| "children": [] |
| }, |
| { |
| "slug": "claim-4-double-descent-at-capacity", |
| "title": "Claim 4: double descent at capacity", |
| "file": "pages/claim-4-double-descent-at-capacity/page.md", |
| "children": [] |
| }, |
| { |
| "slug": "claim-5-membership-inference-scaling", |
| "title": "Claim 5: membership inference scaling", |
| "file": "pages/claim-5-membership-inference-scaling/page.md", |
| "children": [] |
| }, |
| { |
| "slug": "memorize-synthetic", |
| "title": "memorize-synthetic", |
| "file": "pages/memorize-synthetic/page.md", |
| "children": [] |
| }, |
| { |
| "slug": "memorize-claim4", |
| "title": "memorize-claim4", |
| "file": "pages/memorize-claim4/page.md", |
| "children": [] |
| }, |
| { |
| "slug": "memorize-text", |
| "title": "memorize-text", |
| "file": "pages/memorize-text/page.md", |
| "children": [] |
| } |
| ] |
| }, |
| "agent_view_tokens": 6448, |
| "revision": "1783728274322498000" |
| } |
