arXiv (All)
open access
Why Bother Me Again? Benchmarking Personal Agent Memory for Interaction Preferences
Personal intelligence systems are taking on increasingly complex everyday tasks, yet repeated questions, unnecessary confirmations, and unwanted outreach can turn capable assistance into interaction burden. Reducing this burden requires agents to remember how users want to be assisted. However, exis
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
- When Does Memory Help? A Cost-Aware Evaluation of Long-Term Memory in Tool-Using LLM Agents#972506
- Beyond Downstream QA: Rethinking How We Evaluate Agent Memory#1357864
- Beyond Downstream QA: Rethinking How We Evaluate Agent Memory#1348856
- BaRe-Mem: Bayesian Reliability Memory for Robust and Adaptive Agent Consultation#1130131
- BaRe-Mem: Bayesian Reliability Memory for Robust and Adaptive Agent Consultation#1108812
Record · ID 1423277
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