GEIS: A Generation-Evaluation-Improvement Loop of Agent Skills for Long-Form Article Generation
Long-form article generation remains difficult for large language models because it combines long context, long instructions, and long outputs. Existing multi-agent pipelines such as STORM improve information coverage by simulating role-specialized agents, but their capabilities are often entangled in prompts and fixed procedures, making them hard to inspect, reuse, or iteratively improve. This paper presents GEIS (Generation-Evaluation-Improvement loop of agent Skills), a loop of named and declarative skills for Wikipedia-style long-form article generation. Implemented and evaluated in Tasi H
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- PossiblePossibly related (embedding) · 50%alfadur7/llm-wiki-newsroom →
- PossiblePossibly related (embedding) · 49%agent-tools →
- PossiblePossibly related (embedding) · 48%J-Wash: A novel way to brainwash and customize large language models based on Anthropic's Jacobian-Lens! →
- PossiblePossibly related (embedding) · 48%AgustiPuigserver/opus-prompt-architect →
- PossiblePossibly related (embedding) · 48%zjunlp/SkillX →
- FuzzySimilar title/name (fuzzy) · 59%AgentCore-8B →
“Fuzzy title match (0.73): “GEIS: A Generation-Evaluation-Improvement Loop of Agent Skil” ≈ “AgentCore-8B””
- LinkedLinked via arxiv author · 85%Jiale Zhang →
“GEIS: A Generation-Evaluation-Improvement Loop of Agent Skills for Long-Form Article Generation”
- LinkedLinked via arxiv author · 85%Juntao Hu →
“GEIS: A Generation-Evaluation-Improvement Loop of Agent Skills for Long-Form Article Generation”