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ARXIV:2604.12285 · AGENTS · SUBMITTED 15 APR · 17:02 UTC · FRESHNESS STALE
ARXIV:2604.12285AGENTSSUBMITTED 15 APR · 17:02 UTCFRESHNESS STALEZhaofen Wu · Hanrong Zhang · Fulin Lin · Wujiang Xu · Xinran Xu · Yankai Chen · +6 at arXiv
A hierarchical graph-based memory framework for LLM agents that decouples memory encoding and consolidation to improve long-term coherence and reduce noise interference.
Opportunity summary
Pain A hierarchical graph-based memory framework for LLM agents that decouples memory encoding and consolidation to improve long-term coherence and reduce noise interference.
Evidence 0 refs | 3 sources | 50% coverage
Blocker Evidence unverified
A hierarchical graph-based memory framework for LLM agents that decouples memory encoding and consolidation to improve long-term coherence and reduce noise interference. Current unified stream-based memory systems facilitate context updates but remain vulnerable to…
To sustain coherent long-term interactions, Large Language Model (LLM) agents must navigate the tension between acquiring new information and retaining prior knowledge. Current unified stream-based memory systems facilitate context updates but remain vulnerable to…
ScienceToStartup currently rates this 3.0/10 on the public viability pass. Experiments on LoCoMo and LongDialQA indicate that our method consistently outperforms state-of-the-art baselines in both reasoning accuracy and efficiency.
Agents moved forward this cycle; last verified April 2026. Public score 3.0/10.
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A hierarchical graph-based memory framework for LLM agents that decouples memory encoding and consolidation to improve long-term coherence and reduce noise interference.
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10.48550/arXiv.2604.12285A hierarchical graph-based memory framework for LLM agents that decouples memory encoding and consolidation to improve long-term coherence and reduce noise interference.
Abstract
To sustain coherent long-term interactions, Large Language Model (LLM) agents must navigate the tension between acquiring new information and retaining prior knowledge. Current unified stream-based memory systems facilitate context updates but remain vulnerable to interference from transient noise. Conversely, discrete structured memory architectures provide robust knowledge retention but often struggle to adapt to evolving narratives. To address this, we propose GAM, a hierarchical Graph-based Agentic Memory framework that explicitly decouples memory encoding from consolidation to effectively resolve the conflict between rapid context perception and stable knowledge retention. By isolating ongoing dialogue in an event progression graph and integrating it into a topic associative network only upon semantic shifts, our approach minimizes interference while preserving long-term consistency. Additionally, we introduce a graph-guided, multi-factor retrieval strategy to enhance context precision. Experiments on LoCoMo and LongDialQA indicate that our method consistently outperforms state-of-the-art baselines in both reasoning accuracy and efficiency.
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Proof status
unverified0 refs; 3 sources; 50% coverage.
What was readable
Derived fallback: Estimated from adjacent evidence; not verified from source.
Viability
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Dimensions overall score 3.0
PROBLEM
A hierarchical graph-based memory framework for LLM agents that decouples memory encoding and consolidation to improve long-term coherence and reduce noise interference. Current unified stream-based memory systems facilitate context updates but remain vulnerable to interference...
METHOD
To sustain coherent long-term interactions, Large Language Model (LLM) agents must navigate the tension between acquiring new information and retaining prior knowledge. Current unified stream-based memory systems facilitate context updates but remain vulnerable to interference f...
RESULT
ScienceToStartup currently rates this 3.0/10 on the public viability pass. Experiments on LoCoMo and LongDialQA indicate that our method consistently outperforms state-of-the-art baselines in both reasoning accuracy and efficiency.
WHY NOW
Agents moved forward this cycle; last verified April 2026. Public score 3.0/10.
Abstract-backed public claims while anchored extraction refreshes.
A hierarchical graph-based memory framework for LLM agents that decouples memory encoding and consolidation to improve long-term coherence and reduce noise interference. Current unified stream-based memory systems facilitate context updates but remain vulnerable to interference from transient noise.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
To sustain coherent long-term interactions, Large Language Model (LLM) agents must navigate the tension between acquiring new information and retaining prior knowledge. Current unified stream-based memory systems facilitate context updates but remain vulnerable to interference from transient noise.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 3.0/10 on the public viability pass. Experiments on LoCoMo and LongDialQA indicate that our method consistently outperforms state-of-the-art baselines in both reasoning accuracy and efficiency.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Agents moved forward this cycle; last verified April 2026. Public score 3.0/10.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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A hierarchical graph-based memory framework for LLM agents that decouples memory encoding and consolidation to improve long-term coherence and reduce noise interference.
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Commercial read
3.0/10 public viability
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proof status
unverified
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passport absent
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partial
Current read
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