mlsanityData to 7 Oct 2026

DAVIS: Planning Agent with Knowledge Graph-Powered Inner Monologue

arXiv 2410.09252 ↗First posted 11 Oct 2024cs.CL cs.AI cs.HCcited by 3

On the map: Retrieval-augmented generation → Agentic RAG, agents and memory

Abstract (arXiv)

Designing a generalist scientific agent capable of performing tasks in laboratory settings to assist researchers has become a key goal in recent Artificial Intelligence (AI) research. Unlike everyday tasks, scientific tasks are inherently more delicate and complex, requiring agents to possess a higher level of reasoning ability, structured and temporal understanding of their environment, and a strong emphasis on safety. Existing approaches often fail to address these multifaceted requirements. To tackle these challenges, we present DAVIS. Unlike traditional retrieval-augmented generation (RAG) approaches, DAVIS incorporates structured and temporal memory, which enables model-based planning. Additionally, DAVIS implements an agentic, multi-turn retrieval system, similar to a human's inner monologue, allowing for a greater degree of reasoning over past experiences. DAVIS demonstrates substantially improved performance on the ScienceWorld benchmark comparing to previous approaches on 8 out of 9 elementary science subjects. In addition, DAVIS's World Model demonstrates competitive performance on the famous HotpotQA and MusiqueQA dataset for multi-hop question answering. To the best of our knowledge, DAVIS is the first RAG agent to employ an interactive retrieval method in a RAG pipeline.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Placed in Agentic RAG, agents and memory, one of 15 directions in Retrieval-augmented generation. Builds on 26 of its 38 references that are in the map. First posted on arXiv 11 Oct 2024.

Builds on

26 of 38 references.

Closest ancestors · 1

Same direction, newest first

  1. 2024RAP: Retrieval-Augmented Planning with Contextual Memory for Multimodal LLM Agents6 Feb

Other references · 25

  1. 2023LLaMA: Open and Efficient Foundation Language Modelscited by 13,958
  2. 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,355
  3. 2023Reflexion: Language Agents with Verbal Reinforcement Learningcited by 4,064
  4. 2018HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answeringcited by 3,686
  5. 2013Playing Atari with Deep Reinforcement Learningcited by 3,211
  6. 2019Dream to Control: Learning Behaviors by Latent Imaginationcited by 1,598
  7. 2019Mastering Atari, Go, Chess and Shogi by Planning with a Learned Modelcited by 1,327
  8. 2023Describe, Explain, Plan and Select: Interactive Planning with Large Language Models Enables Open-World Multi-Task Agentscited by 396
  9. 2018TextWorld: A Learning Environment for Text-based Gamescited by 304
  10. 2022ScienceWorld: Is your Agent Smarter than a 5th Grader?cited by 298
  11. 2019Challenges of Real-World Reinforcement Learningcited by 282
  12. 2024Agent Q: Advanced Reasoning and Learning for Autonomous AI Agentscited by 201
13 more
  1. 2023JARVIS-1: Open-World Multi-task Agents with Memory-Augmented Multimodal Language Modelscited by 170
  2. 2023SwiftSage: A Generative Agent with Fast and Slow Thinking for Complex Interactive Taskscited by 166
  3. 2019Interactive Fiction Games: A Colossal Adventurecited by 165
  4. 2023Hallucinations in Large Multilingual Translation Modelscited by 126
  5. 2015Deep Reinforcement Learning with a Natural Language Action Spacecited by 88
  6. 2020Graph Constrained Reinforcement Learning for Natural Language Action Spacescited by 80
  7. 2015Language Understanding for Text-based Games Using Deep Reinforcement Learningcited by 70
  8. 2023Temporal Knowledge Graph Forecasting Without Knowledge Using In-Context Learningcited by 36
  9. 2021Learning Knowledge Graph-based World Models of Textual Environmentscited by 31
  10. 2024HOLMES: Hyper-Relational Knowledge Graphs for Multi-hop Question Answering using LLMscited by 24
  11. 2024Empowering Large Language Model Agents through Action Learningcited by 20
  12. 2022TextWorldExpress: Simulating Text Games at One Million Steps Per Secondcited by 12
  13. 2020Belief-Grounded Networks for Accelerated Robot Learning under Partial Observabilitycited by 9

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 2; 2026: 1.

3 papers.

  1. 2025Large Language Models for Planning: A Comprehensive and Systematic Surveycited by 38
  2. 2026Graph World Models: Concepts, Taxonomy, and Future Directionscited by 5
  3. 2025KnowMap: Efficient Knowledge-Driven Task Adaptation for LLMscited by 1

Similar, not cited

Close in wording, no citation either way

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  3. 2025MA-RAG: Multi-Agent Retrieval-Augmented Generation via Collaborative Chain-of-Thought Reasoning
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  5. 2026RAGShaper: Eliciting Sophisticated Agentic RAG Skills via Automated Data Synthesis
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  7. 2025Graph-R1: Towards Agentic GraphRAG Framework via End-to-end Reinforcement Learning
  8. 2026SoK: Agentic Retrieval-Augmented Generation (RAG): Taxonomy, Architectures, Evaluation, and Research Directions