GAM: Hierarchical Graph-based Agentic Memory for LLM Agents explores A hierarchical graph-based memory framework for LLM agents that decouples memory encoding and consolidation to improve long-term coherence and reduce noise interference.. Commercial viability score: 3/10 in Agents.
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This route is the stable paper-level surface for citations, viability, references, and downstream handoffs. Use it as the proof layer behind Signal Canvas, workspace creation, and launch-pack generation.
Freshness
Canonical route: /paper/gam-hierarchical-graph-based-agentic-memory-for-llm-agents
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Agent Handoff
Canonical ID gam-hierarchical-graph-based-agentic-memory-for-llm-agents | Route /paper/gam-hierarchical-graph-based-agentic-memory-for-llm-agents
REST example
curl https://sciencetostartup.com/api/v1/agent-handoff/paper/gam-hierarchical-graph-based-agentic-memory-for-llm-agentsMCP example
{
"tool": "get_paper",
"arguments": {
"arxiv_id": "2604.12285"
}
}source_context
{
"surface": "paper",
"mode": "paper",
"query": "GAM: Hierarchical Graph-based Agentic Memory for LLM Agents",
"normalized_query": "2604.12285",
"route": "/paper/gam-hierarchical-graph-based-agentic-memory-for-llm-agents",
"paper_ref": "gam-hierarchical-graph-based-agentic-memory-for-llm-agents",
"topic_slug": null,
"benchmark_ref": null,
"dataset_ref": null
}Constellation, claims, and market context stay visible on the paper proof page even when commercialization rails are held back for incomplete proof receipts.
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Dimensions overall score 3.0
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