arXiv (All)
open access
MemSyco-Bench: Benchmarking Sycophancy in Agent Memory
Memory has emerged as a cornerstone of modern LLM-based agents, supporting their evolution from single-turn assistants to long-term collaborators. However, memory is not always beneficial: retrieved memories often induce a critical issue of sycophancy, causing agents to over-align with the user at t
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
- Remember Before You're Asked: MemDream for Self-Probing Memory Evolution#1128810
- Sentry: Learning to Recover from LLM Agent Failures at Test Time#1187672
- Memory as a Controlled Process: Learned Adaptive Memory Management for LLM Agents#1202155
- Supra Cognitive Modes: A Routed Architecture for Agent Memory#386921
- MemScope: Evolving Agent Memory Architectures through Localized Code Repair#1356506
Record · ID 1357217
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