RippleMem: From Isolated Retrieval to Associative Recollection for Long-Term Agent Memory
LLM-based agents increasingly rely on external memory to support long-horizon reasoning and interaction. However, the main bottleneck is not simply storing past experience, but recovering the right set of evidence when relevant information is distributed across many interactions. Existing approaches struggle with this access problem. Full-context methods require noisy long-context search, flat retrieval often returns isolated and incomplete records, and graph-based memory systems can be expensive to construct while compressing rich event context. We introduce RippleMem, a long-term memory syst
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- FuzzySimilar title/name (fuzzy) · 59%AgentCore-8B →
“Fuzzy title match (0.73): “RippleMem: From Isolated Retrieval to Associative Recollecti” ≈ “AgentCore-8B””
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- PossiblePossibly related (embedding) · 61%MemoryOps AI: governed memory runtime for LLM agents with deletion-leakage evals and context admission [P] →
- PossiblePossibly related (embedding) · 60%TRACE: open-source hierarchical memory for LLM agents, 82.5% on MemoryAgentBench’s EventQA using gpt-oss-20B [P] →
- FuzzySimilar title/name (fuzzy) · 87%SWE-agent/SWE-agent →
“Fuzzy title match (0.94): “RippleMem: From Isolated Retrieval to Associative Recollecti” ≈ “SWE-agent/SWE-agent””
- FuzzySimilar title/name (fuzzy) · 87%zhayujie/CowAgent →
“Fuzzy title match (0.94): “RippleMem: From Isolated Retrieval to Associative Recollecti” ≈ “zhayujie/CowAgent””
- FuzzySimilar title/name (fuzzy) · 66%TencentCloud/TencentDB-Agent-Memory →
“Fuzzy title match (0.78): “RippleMem: From Isolated Retrieval to Associative Recollecti” ≈ “TencentCloud/TencentDB-Agent-Memory””
- FuzzyOverlapping authors or contributors · 62%DietrichGebert/ponytail →
“Shared author/contributor keys: cheng”
