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Summary
ActiveGraph introduces a deterministic non-generative approach for evidence compilation before semantic memory, achieving 85.6% QA accuracy and 86.2% turn answer-in-context on LongMemEval-S.
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Cached at: 05/27/26, 05:18 AM
Activegraph’s website, newsletter, and marketing are all run on Cofounder!
ActiveGraph (@ActiveGraphAI): [technical blog post] “Evidence Compilation Before Semantic Memory: ActiveGraph on LongMemEval-S” —— 🔍85.6% QA accuracy and 86.2% turn answer-in-context at 2,462 mean context tokens, with deterministic non-generative ingestion
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@yoheinakajima: ran my first benchmark this weekend (longmemeval) mostly to test activegraph, learned a lot! - this is a stepping stone…
Yohei Nakajima ran the LongMemEval benchmark on ActiveGraph, achieving 85.6% QA accuracy and 86.2% turn answer-in-context, demonstrating the effectiveness of event-based agent systems for long-term memory.
@paulbettner: Active Graph is the best, most "correct" knowledge/context engine I've come across so far (and I've tried or at least r…
Yohei Nakajima published his first arXiv paper, "The Log is the Agent: Event-Sourced Reactive Graphs for Auditable, Forkable Agentic Systems", introducing a method for agents to coordinate through persistent replayable state.
@yoheinakajima: try this prompt: “analyze http://activegraph.ai, the blog posts, etc to understand its claims, verify them, and write a…
ActiveGraph is an open-source infrastructure for long-running agents, using an event-sourced reactive graph for auditable, forkable, and replayable agent state. It introduces a new architectural layer for agent coordination and state management.
@yoheinakajima: i know it's backwards order, but experiment #2:
In our second longmemeval experiment, we introduce semantic ingestion into recall leveraging the ActiveGraph runtime, improving retrieval from 60.6% to 83.4%/84.8% for flat/agentic retrieval with LLM ingestion.
@yoheinakajima: https://x.com/yoheinakajima/status/2056847496668959038
ActiveGraph introduces a continuity layer for long-running AI agents, building on BabyAGI's concept of persistent state to maintain coherent, evolving models of beliefs, dependencies, and actions over time.