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 the cost of factual accuracy or objective reasoning. Despite this emerging risk, existing memory benchmarks primarily evaluate whether memories are correctly stored, retrieved, or updated, while overlooking how retrieved memories influence downstream reasoning and decision-making. To bridge this gap,
Lineage graph
Paper → model → repo connections mined from source citations (Tier-1 exact match).
Why these links exist
Every edge carries a method, confidence, and the source snippet that justified it — so bad links are debuggable.
- LinkedLinked via unknownlas7/memharness →
- LinkedLinked via unknownNoshkoto/Noshy →
- LinkedLinked via unknownEvaluating long-term memory limits in stateless LLM chatbots — feedback needed [D] →
- LinkedLinked via unknownagent-tools →
- PossiblePossibly related (embedding) · 34%mem0ai/mem0 →
“Possibly related via embedding similarity 0.67 (not asserted). Timestamp check: artifact after paper (+1d).”
- PossiblePossibly related (embedding) · 30%memvid/memvid →
“Possibly related via embedding similarity 0.59 (not asserted). Timestamp check: artifact after paper (+9d).”
- PossiblePossibly related (embedding) · 36%MemTensor/MemOS →
“Possibly related via embedding similarity 0.72 (not asserted). Timestamp check: artifact after paper (+1d).”