arXiv (All)
open access
Manifest-Assisted Retrieval in Artificial Intelligence Search: Evidence Gaps and a Security Evaluation
and-scienceartificial-intelligencecomputation-and-languagecomputational-engineeringcryptography-and-securitydatabasesfinanceinformation-retrieval
The external sources retained in our non-systematic audit did not isolate the incremental security or utility effect of consuming /llms.txt while ordinary retrieval remained fixed. The initial audit covered 39 sources through August 16, 2026, with later targeted updates through October 14. A purposi
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Manifest-Assisted Retrieval in Artificial Intelligence Search: Evidence Gaps and a Security Evaluation#1483109
- Relevance Supervision Can Teach Neural Retrievers to Prefer LLM-Generated Text#1354875
- What the Author Wrote, What the Agent Reads: Silent Cue Loss in Enterprise Document Ingestion#1414706
- What the Author Wrote, What the Agent Reads: Silent Cue Loss in Enterprise Document Ingestion#1406496
- AgentDoxx: Agentic Re-identification of Anonymized Text with Web Search#1201599
Record · ID 1489153
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive