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  3. Learning Chain Of Thoughts Prompts for Predicting Entities,
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Learning Chain Of Thoughts Prompts for Predicting Entities, Relations, and even Literals on Knowledge Graphs

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Evidence Receipt

Freshness: 2026-04-15T16:44:08.417259+00:00

Claims: 0

References: 0

Proof: unverified

Freshness: fresh

Source paper: Learning Chain Of Thoughts Prompts for Predicting Entities, Relations, and even Literals on Knowledge Graphs

PDF: https://arxiv.org/pdf/2604.12651v1

Repository: https://github.com/dice-group/RALP

Source count: 4

Coverage: 67%

Last proof check: 2026-04-15T16:58:26.609Z

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Paper Mode

Learning Chain Of Thoughts Prompts for Predicting Entities, Relations, and even Literals on Knowledge Graphs

Overall score: 8/10
Lineage: 5645fb5fd1ad…
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Canonical Paper Receipt

Last verification: 2026-04-15T16:58:26.609Z

Freshness: fresh

Proof: unverified

Repo: active

References: 0

Sources: 4

Coverage: 67%

Missingness
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  • Workspace mode blends saved sources, prior evidence queries, and linked papers.

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Dimensions overall score 8.0

GitHub Code Pulse

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Last commit
7/31/2025
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Keep exploring

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Reasoning While Asking: Transforming Reasoning Large Language Models from Passive Solvers to Proactive Inquirers
Score 7.0down
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Topology-Aware Reasoning over Incomplete Knowledge Graph with Graph-Based Soft Prompting
Score 6.0down
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Beyond the Answer: Decoding the Behavior of LLMs as Scientific Reasoners
Score 3.0down
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KG-Reasoner: A Reinforced Model for End-to-End Multi-Hop Knowledge Graph Reasoning
Score 7.0down
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Small Generalizable Prompt Predictive Models Can Steer Efficient RL Post-Training of Large Reasoning Models
Score 6.0down
Builds On This
SmartThinker: Progressive Chain-of-Thought Length Calibration for Efficient Large Language Model Reasoning
Score 7.0down
Prior Work
Hierarchical Chain-of-Thought Prompting: Enhancing LLM Reasoning Performance and Efficiency
Score 8.0stable
Prior Work
KG-Hopper: Empowering Compact Open LLMs with Knowledge Graph Reasoning via Reinforcement Learning
Score 8.0stable

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Related Resources

  • How can knowledge graphs improve code evolution and API adaptation with LLMs?(question)
  • What are the advantages of using knowledge graphs for code generation?(question)
  • What are the best practices for integrating knowledge graphs into code generation pipelines?(question)

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