# Towards general embodied intelligence: integrating large language models, knowledge bases, and reasoning capabilities to build the next generation of AI agents

arXiv: https://arxiv.org/abs/2608.19794 · HTML: https://mlsanity.com/paper/2608.19794/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Spatial and embodied reasoning, one of 17 directions in LLM reasoning.
- Builds on 28 of its 174 references that are in the map.
- First posted on arXiv 20 Aug 2026.

## Placed in

- LLM reasoning / [Spatial and embodied reasoning](https://mlsanity.com/lr/spatial-and-embodied-reasoning/)
- LLM agents and tool use / [Multimodal and embodied agents](https://mlsanity.com/agents/multimodal-and-embodied-agents/)

## Builds on (most cited first, 28 of 28)

- [Hierarchical Text-Conditional Image Generation with CLIP Latents](https://mlsanity.com/paper/2204.06125/) (2022)
- [Aligning Cyber Space with Physical World: A Comprehensive Survey on Embodied AI](https://mlsanity.com/paper/2407.06886/) (2024)
- [GNN-RAG: Graph Neural Retrieval for Large Language Model Reasoning](https://mlsanity.com/paper/2405.20139/) (2024)
- [Bias and Unfairness in Information Retrieval Systems: New Challenges in the LLM Era](https://mlsanity.com/paper/2404.11457/) (2024)
- [Embodied Task Planning with Large Language Models](https://mlsanity.com/paper/2307.01848/) (2023)
- [Embodied LLM Agents Learn to Cooperate in Organized Teams](https://mlsanity.com/paper/2403.12482/) (2024)
- [Bias in Large Language Models: Origin, Evaluation, and Mitigation](https://mlsanity.com/paper/2411.10915/) (2024)
- [SQL-R1: Training Natural Language to SQL Reasoning Model By Reinforcement Learning](https://mlsanity.com/paper/2504.08600/) (2025)
- [Unlocking Efficient Long-to-Short LLM Reasoning with Model Merging](https://mlsanity.com/paper/2503.20641/) (2025)
- [EnvGen: Generating and Adapting Environments via LLMs for Training Embodied Agents](https://mlsanity.com/paper/2403.12014/) (2024)
- [Steve-Eye: Equipping LLM-based Embodied Agents with Visual Perception in Open Worlds](https://mlsanity.com/paper/2310.13255/) (2023)
- [Towards Efficient LLM Grounding for Embodied Multi-Agent Collaboration](https://mlsanity.com/paper/2405.14314/) (2024)
- [BadRobot: Jailbreaking Embodied LLM Agents in the Physical World](https://mlsanity.com/paper/2407.20242/) (2024)
- [FiDeLiS: Faithful Reasoning in Large Language Model for Knowledge Graph Question Answering](https://mlsanity.com/paper/2405.13873/) (2024)
- [ROS-LLM: A ROS framework for embodied AI with task feedback and structured reasoning](https://mlsanity.com/paper/2406.19741/) (2024)
- [Towards Uncovering How Large Language Model Works: An Explainability Perspective](https://mlsanity.com/paper/2402.10688/) (2024)
- [Large language models for artificial general intelligence (AGI): A survey of foundational principles and approaches](https://mlsanity.com/paper/2501.03151/) (2025)
- [Dissecting Bias in LLMs: A Mechanistic Interpretability Perspective](https://mlsanity.com/paper/2506.05166/) (2025)
- [Any Large Language Model Can Be a Reliable Judge: Debiasing with a Reasoning-based Bias Detector](https://mlsanity.com/paper/2505.17100/) (2025)
- [Guide-LLM: An Embodied LLM Agent and Text-Based Topological Map for Robotic Guidance of People with Visual Impairments](https://mlsanity.com/paper/2410.20666/) (2024)
- [Adapting Language-Specific LLMs to a Reasoning Model in One Day via Model Merging -- An Open Recipe](https://mlsanity.com/paper/2502.09056/) (2025)
- [The influence of persona and conversational task on social interactions with a LLM-controlled embodied conversational agent](https://mlsanity.com/paper/2411.05653/) (2024)
- [RALLRec+: Retrieval Augmented Large Language Model Recommendation with Reasoning](https://mlsanity.com/paper/2503.20430/) (2025)
- [HydraRAG: Structured Cross-Source Enhanced Large Language Model Reasoning](https://mlsanity.com/paper/2505.17464/) (2025)
- [Read and Think: An Efficient Step-wise Multimodal Language Model for Document Understanding and Reasoning](https://mlsanity.com/paper/2403.00816/) (2024)
- [LINKED: Eliciting, Filtering and Integrating Knowledge in Large Language Model for Commonsense Reasoning](https://mlsanity.com/paper/2410.09541/) (2024)
- [GreenMind: A Next-Generation Vietnamese Large Language Model for Structured and Logical Reasoning](https://mlsanity.com/paper/2504.16832/) (2025)
- [FaGeL: Fabric LLMs Agent empowered Embodied Intelligence Evolution with Autonomous Human-Machine Collaboration](https://mlsanity.com/paper/2412.20297/) (2024)

## Built on by (most cited first, 1 of 1)

- [Defining AI Agents: A Compendium of Criteria, Metrics, and Benchmarks](https://mlsanity.com/paper/2609.11018/) (2026)

## Abstract (arXiv)

The convergence of large language models (LLMs), structured knowledge bases (KBs), and reasoning ability (RA) presents a promising trajectory toward general embodied intelligence (GEI). This paper reviews the evolution of LLM-centered intelligent systems, emphasising their integration with knowledge representation, logical reasoning, and physical embodiment. We analyse LLM architectures, pre-training methods, and inference mechanisms, along with their interaction with external knowledge sources and structured reasoning frameworks. Furthermore, we examine embodied intelligence (EI) paradigms wherein agents learn and act in physical environments. To synthesise these dimensions, we present a conceptual framework that illustrates the synergy among LLMs, KBs, RA, and embodiment, serving as a guiding model for perception, reasoning, and action rather than an implemented engineering architecture. To advance toward GEI, we identify five key challenges: efficient LLM deployment, closed-loop knowledge integration, hybrid symbolic-neural reasoning, perception-action grounding, and continual learning. This survey provides a comprehensive roadmap for developing adaptive, multimodal agents capable of operating in complex, dynamic settings.