Autonomous Agents Research Papers
Daily-updated curated list of research papers on autonomous agents (LLMs).
This repository is a list of research papers with no executable code or agent functionality. Therefore, there is no least privilege, user confirmation, data flow transparency, sensitive data handling, dependency security, external effects, or rollback. Source attribution is partially present: paper entries include arXiv links and author information, but selection criteria or verification process are not stated.
Self-consistency: README structure is clear, paper entries are consistently formatted, and links point to arXiv. Dependency availability: No code or dependencies. Failure messages: No code or runtime behavior.
Audience and scenarios: Aimed at researchers and practitioners interested in autonomous agents, providing a list of papers organized by year. Capability boundaries: Not explicitly stated, but implied as paper aggregation. Trigger precision: Not applicable as there is no automated trigger. Environment fit: Static documentation only, no environment requirements.
Information architecture: README provides clear navigation and categorization. Install notes: Not applicable as there is no code. Naming stability: Repository name and structure are stable. Examples and FAQ: Paper abstracts serve as examples, but no FAQ. Known limitations: Not mentioned. License: MIT license is explicit. Versioning and changelog: Updates by year, but no formal changelog. Maintenance responsibility: Author Teemu Maatta is explicit, but not verified.
Output usability: Paper list and abstracts are easy to browse. Marginal value: Provides daily updated paper aggregation, useful for researchers. Cost benefit: No code, no runtime cost, but maintenance cost unknown.
Claim traceability: Each paper has an arXiv link, traceable. Cross-source corroboration: No other sources provided. Fact-inference separation: Abstracts may contain inferences, but not explicitly separated.
- This repository contains no executable code or agent functionality; it is only a list of papers.
- Paper selection criteria and verification process are not stated, so information may be unverified.
- Publisher identity is unverified, and maintenance responsibility is unclear.
What does this agent do, and when should you use it?
Autonomous-Agents is a curated collection of research papers on autonomous agents (LLMs), updated daily. The repository organizes papers by year in separate markdown files, allowing chronological browsing. Each entry provides the paper title, arXiv link, and a bullet-point summary of key contributions. It serves as a literature tracking tool for researchers and practitioners to stay current. The repository is MIT licensed and includes a resources section.
The repository maintains a chronologically ordered list of research papers, updated daily. Each paper entry includes title, arXiv link, and summary bullets. Papers are grouped by year and month, linked to separate markdown files under resources/. A resources section provides additional content.
- Researchers quickly scan the latest papers in autonomous agents.
- Practitioners track specific topics like multi-agent systems or embodied agents.
- Students find introductory papers and classic literature.
- Tech enthusiasts get daily summaries of arXiv papers.
What are this agent's strengths and limitations?
- Daily updates ensure freshness
- Papers are organized chronologically for easy browsing
- Each entry includes concise bullet summaries
- Covers diverse areas: multi-agent, embodied, etc.
- Provides only paper lists, not full text or code
- No search or filtering functionality
- Requires manual browsing of multiple files
- No citation format provided (except BibTeX style reference)
How do you install or deploy this agent?
Installation requires Git to clone the repository. Command: git clone https://github.com/tmgthb/Autonomous-Agents.gitHow do you use this agent?
After cloning, open README.md for the latest papers, or browse year-based files in resources/. For example, view resources/Autonomous_Agents_Research_Papers_2026_5.md for May 2026 papers.