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Can a Bayesian Oracle Prevent Harm from an Agent? paper by Independent / Community
⭐ 65.6
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Paper

Can a Bayesian Oracle Prevent Harm from an Agent?

by Independent / Community 001453eafa97460890151bc036c58b1371dafdc4
Free2AITools Nexus Index
65.6
S: Semantic 50

Query-time baseline · scored live at search

A: Authority 77
P: Popularity 52
R: Recency 100
Q: Quality 65
Tech Context
Vital Performance —

Is there a way to design powerful AI systems based on machine learning methods that would satisfy probabilistic safety guarantees? With the long-term goal of obtaining a probabilistic guarantee that would apply in every context, we consider estimating a context-dependent bound on the probability of violating a given safety specification. Such a risk evaluation would need to be performed at run-time to provide a guardrail against dangerous actions of an AI. Noting that different plausible hypo...

Source β†’
Semantic Scholar 11 Citations
Paper Information Summary
Entity Passport
Registry ID 001453eafa97460890151bc036c58b1371dafdc4
License ArXiv
Provider semantic_scholar
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Cite this paper

Academic & Research Attribution

BibTeX 
@misc{001453eafa97460890151bc036c58b1371dafdc4,
  author = {Unknown},
  title = {Can a Bayesian Oracle Prevent Harm from an Agent? Paper},
  year = {2026},
  howpublished = {\url{https://api.semanticscholar.org/001453eafa97460890151bc036c58b1371dafdc4}},
  note = {Accessed via Free2AITools.}
}
APA Style
Unknown. (2026). Can a Bayesian Oracle Prevent Harm from an Agent? [Paper]. Free2AITools. https://api.semanticscholar.org/001453eafa97460890151bc036c58b1371dafdc4

πŸ”¬Technical Deep Dive

Full Specifications [+]

βš–οΈ Free2AITools Nexus Index V2.0

Methodology How FNI works
Semantic (S) 50

Query-time baseline · scored live at search

Authority (A) 77
Popularity (P) 52
Recency (R) 100
Quality (Q) 65

πŸ’¬ Index Insight

FNI V2.0 for Can a Bayesian Oracle Prevent Harm from an Agent?: Authority (A:77), Popularity (P:52), Recency (R:100), Quality (Q:65). Semantic (S) is a query-time baseline scored live at search.

Free2AITools Nexus Index

Data Sources / Provenance

HuggingFace API GitHub Metadata Arxiv Citation DB Methodology
Open data Updated: Live data

πŸ“ Executive Summary

"Is there a way to design powerful AI systems based on machine learning methods that would satisfy probabilistic safety guarantees? With the long-term goal of obtaining a probabilistic guarantee that would apply in every context, we consider estimating a context-dependent bound on the probability of violating a given safety specification. Such a risk evaluation would need to be performed at run-time to provide a guardrail against dangerous actions of an AI. Noting that different plausible hypo..."

❝ Cite Node 

@article{Unknown2026Can,
  title={Can a Bayesian Oracle Prevent Harm from an Agent?},
  author={},
  note={Indexed by Free2AITools},
  year={2026}
}

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πŸ“Š Research Signals

πŸ“ˆ11CitationsSemantic Scholar
πŸ›οΈ77AuthorityFNI pillar
⏱️100RecencyFNI pillar
βœ…65QualityFNI pillar
πŸ—‚οΈautomation workflowField
πŸ“¦Data Source: semantic_scholar
πŸ”„ Updated daily

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πŸ†” Identity & Source

source
semantic_scholar
author
Unknown
license
ArXiv
tags
paper, research, academic

βš™οΈ Technical Specs

architecture
null
params billions
null
context length
null
pipeline tag

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citations
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