Research on how much language models can infer about their current user, and interpretability work on such inferences
grantVerifiedChild of Long-Term Future Fund (LTFF)
Metadata
| Source Table | grants |
| Source ID | PgtjNbfMcg |
| Description | to Egg Syntax (legal: Jesse Davis), USD 55000, 2024-01 |
| Source URL | funds.effectivealtruism.org/grants |
| Parent | Long-Term Future Fund (LTFF) |
| Children | — |
| Created | Mar 12, 2026, 5:54 AM |
| Updated | Mar 14, 2026, 6:22 AM |
| Synced | Mar 12, 2026, 4:12 PM |
Record Data
id | PgtjNbfMcg |
organizationId | Long-Term Future Fund (LTFF)(organization) |
granteeId | Egg Syntax (legal: Jesse Davis) |
orgEntityId | Long-Term Future Fund (LTFF)(organization) |
orgDisplayName | — |
granteeEntityId | — |
granteeDisplayName | Egg Syntax (legal: Jesse Davis) |
name | Research on how much language models can infer about their current user, and interpretability work on such inferences |
amount | 55000 |
currency | USD |
period | — |
date | 2024-01 |
status | — |
source | funds.effectivealtruism.org/grants |
notes | [Long-Term Future Fund] Research on how much language models can infer about their current user, and interpretability work on such inferences |
programId | xng_1vsce_ |
dataSourceId | — |
Source Check Verdicts
confirmed95% confidence
Last checked: 4/29/2026
1 → confirmed
Debug info
Thing ID: PgtjNbfMcg
Source Table: grants
Source ID: PgtjNbfMcg
Parent Thing ID: sid_yA12C1KcjQ