Why Longer Context Makes LLMs Forget Faster: 7 Failure Modes and Memory System Solutions

The article analyzes how extending the context window of large language models leads to rapid forgetting, outlines seven concrete failure modes, examines cognitive‑science‑based memory architectures, and walks through practical layers—from Python lists to markdown files to vector retrieval—highlighting why simple context expansion alone cannot solve the problem.

Agent DesignContext WindowLLM Memory

0 likes · 10 min read

Why Longer Context Makes LLMs Forget Faster: 7 Failure Modes and Memory System Solutions

Instant Consumer Technology Team

Dec 18, 2025 · Artificial Intelligence

How a Multi‑Agent Framework Boosts Graph Chain‑of‑Thought Reasoning Efficiency

The paper introduces GLM, a multi‑agent Graph‑CoT framework with an optimized LLM serving architecture that dramatically improves accuracy, reduces token consumption, lowers latency, and increases throughput across diverse domains, as demonstrated by extensive GRBench evaluations.

LLM optimizationMulti-AgentToken efficiency

0 likes · 10 min read

How a Multi‑Agent Framework Boosts Graph Chain‑of‑Thought Reasoning Efficiency

DataFunTalk

Nov 4, 2022 · Artificial Intelligence

Explainable Knowledge Graph Reasoning: Background, Advances, Motivation, Recent Research, and Outlook

This article reviews explainable knowledge graph reasoning, covering its background, core concepts, downstream applications, major reasoning methods, motivations for interpretability, recent advances such as hierarchical and Bayesian reinforcement learning, meta‑path mining, and future research directions.

Reinforcement Learningexplainable AIgraph reasoning

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Explainable Knowledge Graph Reasoning: Background, Advances, Motivation, Recent Research, and Outlook

DataFunTalk

Jun 11, 2022 · Artificial Intelligence

Explainable Recommendation: Background, Development, Graph‑Based Structured Explanations, and Natural Language Generation Advances

This article reviews the emerging field of explainable recommendation, covering its motivation, historical evolution from template‑based to knowledge‑graph and generative‑language approaches, recent advances in graph‑structured and natural‑language explanations, key research works, industrial applications, and open challenges such as fact‑checking, low‑resource settings, and evaluation methods.

AIexplainable recommendationgraph reasoning

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Explainable Recommendation: Background, Development, Graph‑Based Structured Explanations, and Natural Language Generation Advances

Meituan Technology Team

Nov 22, 2018 · Artificial Intelligence

Meituan Brain: Large‑Scale Knowledge Graph Construction and Applications

Meituan Brain builds a massive multi‑modal knowledge graph of billions of entities and triples across food, entertainment, and travel, using advanced extraction, validation, fusion, and reasoning techniques to empower search, recommendation, merchant tools, and fraud detection while addressing scalability and schema‑evolution challenges.

AIMeituanNLP

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Meituan Brain: Large‑Scale Knowledge Graph Construction and Applications