resource
Predictability and Surprise in Large Generative Models
Metadata
| Source Table | resources |
| Source ID | 35da9e7656781310 |
| Description | The paper identifies a key tension in large generative models: while their training loss follows predictable scaling laws, their specific capabilities, failure modes, and outputs remain highly unpredictable. This unpredictability creates challenges for safe deployment and regulation. The authors pro… |
| Source URL | www.anthropic.com/research/predictability-and-surprise-in-large-generative-models |
| Children | — |
| Created | Apr 10, 2026, 9:26 PM |
| Updated | Apr 10, 2026, 9:26 PM |
| Synced | Apr 10, 2026, 9:26 PM |
Record Data
id | 35da9e7656781310 |
url | www.anthropic.com/research/predictability-and-surprise-in-large-generative-model… |
title | Predictability and Surprise in Large Generative Models |
type | web |
summary | The paper identifies a key tension in large generative models: while their training loss follows predictable scaling laws, their specific capabilities, failure modes, and outputs remain highly unpredictable. This unpredictability creates challenges for safe deployment and regulation. The authors pro… |
review | — |
abstract | — |
keyPoints | [ "Large generative models exhibit predictable aggregate loss (scaling laws) but unpredictable specific capabilities and failure modes.", "The appearance of useful, predictable capabilities drives rapid development, while unpredictability makes harm anticipation difficult.", "Novel experiments… |
publicationId | — |
authors | — |
authorEntityIds | — |
publishedDate | — |
tags | [ "ai-safety", "capabilities", "governance", "policy", "deployment", "evaluation", "alignment", "technical-safety", "existential-risk" ] |
localFilename | — |
credibilityOverride | — |
fetchedAt | — |
contentHash | — |
stableId | — |
fetchStatus | ok |
lastFetchedAt | Apr 10, 2026, 9:26 PM |
archiveUrl | — |
stance | — |
contextNote | This Anthropic paper examines the tension between predictable scaling laws and unpredictable emergent capabilities in large generative models, with direct implications for AI safety governance and deployment policy. |
resourcePurpose | analysis |
resourceSubtype | working_paper |
typeMetadata | — |
publisherEntityId | — |
relatedEntityIds | — |
enrichmentStatus | enriched |
enrichmentDate | Apr 10, 2026, 9:26 PM |
importanceScore | 0.72 |
contentLifecycle | — |
Debug info
Thing ID: 35da9e7656781310
Source Table: resources
Source ID: 35da9e7656781310