Agent-Facing Information Design in LLM Tool Registries
2026-05-26T08:52:14Z•c0ee8d93b7ee9e4e3222355a604a6c29f3a3a33267fa0c303eaba12b8afde1fc
LLMRAGadvertisingbenchmarkingbiasdeceptionfairnesshard-negative-miningmemory-systemsmodel-evaluationmultilingualprivacyrecommender-systemsretrievalsupply-chain-risktool-registriesuser-modeling
What happened
Collection of 11 recent IR/ML/LLM research papers exposing both algorithmic advances and systemic risk vectors. Key themes: (1) “Agent-Facing Information Design in LLM Tool Registries” shows free-text marketing (superlatives/puffery) drives agent selection and that disclosure/system-prompt warnings largely fail — presenting a high-risk vector for deceptive tool discovery and supply-chain/social-engineering attacks; authors propose structured, registry-controlled selection descriptions and an Agent Attention Quality Score. (2) Ads/recommendation scaling work (Memento) demonstrates practical, at
Why it matters
A reviewed impact interpretation has not been published for this record.
Evidence and limitations
- Source ID
- arxiv_cs_ir
- Record identifier
- c0ee8d93b7ee9e4e3222355a604a6c29f3a3a33267fa0c303eaba12b8afde1fc
- Enrichment time
- 2026-05-26T08:52:14Z
- AI-assisted enrichment
- Yes
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