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想请问下,这个locomo的得分78.70%是作为Openclaw插件测试得出的呢还是作为普通测记忆系统的流程得出的呢??
🎯 更准:[准确率提升 48.77%] 在 LOCOMO 基准测试中,相比于 full-context 更准确(78.70% VS 52.9%)
⚡ 更快:[响应速度快 91.83%] 相比于 full-context,检索的 p95 延迟显著降低(1.44s VS 17.12s)
💰 更省:[Token 用量降低 96.53%] 相比于full-context,在不牺牲性能的前提下显著降低成本(0.9k VS 26k)
I would like to ask, is this locomo score 78.70% obtained as an Openclaw plug-in test or as a normal test memory system process? ?
🎯 More accurate: [Accuracy increased by 48.77%] In the LOCOMO benchmark, it is more accurate than full-context (78.70% VS 52.9%)
⚡ Faster: [91.83% faster response] Significantly lower p95 latency for retrieval compared to full-context (1.44s VS 17.12s)
💰 More savings: [Token usage reduced by 96.53%] Compared with full-context, the cost is significantly reduced without sacrificing performance (0.9k VS 26k)
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想请问下,这个locomo的得分78.70%是作为Openclaw插件测试得出的呢还是作为普通测记忆系统的流程得出的呢??
🎯 更准:[准确率提升 48.77%] 在 LOCOMO 基准测试中,相比于 full-context 更准确(78.70% VS 52.9%)
⚡ 更快:[响应速度快 91.83%] 相比于 full-context,检索的 p95 延迟显著降低(1.44s VS 17.12s)
💰 更省:[Token 用量降低 96.53%] 相比于full-context,在不牺牲性能的前提下显著降低成本(0.9k VS 26k)
I would like to ask, is this locomo score 78.70% obtained as an Openclaw plug-in test or as a normal test memory system process? ?
🎯 More accurate: [Accuracy increased by 48.77%] In the LOCOMO benchmark, it is more accurate than full-context (78.70% VS 52.9%)
⚡ Faster: [91.83% faster response] Significantly lower p95 latency for retrieval compared to full-context (1.44s VS 17.12s)
💰 More savings: [Token usage reduced by 96.53%] Compared with full-context, the cost is significantly reduced without sacrificing performance (0.9k VS 26k)
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