REVIEW 5 cited by
Levels of AI Agents: from Rules to Large Language Models
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
Signed reviews
read the original abstract
AI agents are defined as artificial entities to perceive the environment, make decisions and take actions. Inspired by the 6 levels of autonomous driving by Society of Automotive Engineers, the AI agents are also categorized based on utilities and strongness, as the following levels: L0, no AI, with tools taking into account perception plus actions; L1, using rule-based AI; L2, making rule-based AI replaced by IL/RL-based AI, with additional reasoning & decision making; L3, applying LLM-based AI instead of IL/RL-based AI, additionally setting up memory & reflection; L4, based on L3, facilitating autonomous learning & generalization; L5, based on L4, appending personality of emotion and character and collaborative behavior with multi-agents.
Forward citations
Cited by 5 Pith papers
-
Facilitating Video Story Interaction with Multi-Agent Collaborative System
A multi-agent system with VLM and RAG lets users talk with stage-aware Harry Potter characters and customize scenes, with a user study reporting enhanced engagement.
-
Toward Edge General Intelligence with Multiple-Large Language Model (Multi-LLM): Architecture, Trust, and Orchestration
A survey of multi-LLM systems in edge computing, covering architectures, enabling technologies, trust mechanisms, applications, and open datasets for edge general intelligence.
-
LLM-based Multi-Agent System for Intelligent Refactoring of Haskell Code
A GPT-4o-based multi-agent pipeline is reported to refactor open-source Haskell code, with claimed but poorly supported reductions in complexity and memory usage.
-
Distributed Approach to Haskell Based Applications Refactoring with LLMs Based Multi-Agent Systems
An LLM-based multi-agent system for Haskell refactoring reports reduced cyclomatic complexity and memory allocation on two codebases, but the evaluation lacks reproducibility and contains inconsistent numbers.
-
Agents Are Not Enough
Agents alone are not enough; the authors propose an ecosystem of task-specific Agents, user-representing Sims, and user-facing Assistants.
Discussion (0). Continue with ORCID to comment.