This repository curates the latest research papers on the applications and architectural technologies of AI agents. We perform weekly Arxiv searches using specific keywords and pick only those that are particularly interesting. Rather than striving for comprehensiveness, we add papers when they introduce a distinctively new approach or novel concept that stands out from existing methods.
An AI Agent is an autonomous system powered by large language models that can perceive its environment, reason through complex tasks, and use tools to take actions in pursuit of specific goals. It combines reasoning, planning, memory, and tool-use capabilities to operate independently or as part of a multi-agent system.
Papers are filed in four layers — capabilities (what an agent can do), architecture (how it's built), operations (how it's run), and applications (where it's used). Each entry links to a curated, date-ordered reading list.
🔥: Recommended papers
📖: Survey papers
⚖️: Benchmark papers
🔄 Badges show papers added in the last 2 months (Aug–Sep 2026); cluster headings show the sum:
(+N)recent additions, 🔥 = high activity. Regenerate:python scripts/update_readme_badges.py.📂 See TAXONOMY.md for the full directory map and the rules for where each paper is filed.
- Agent Capabilities — what an agent can do: cognition, knowledge, action, and how it learns & improves
- Core Cognition — reason, plan, ideate, perceive (+3)
- Reasoning (+1)
- Planning
- Ideation (+2)
- Perception
- Knowledge & Context — what the agent knows and carries between steps (+16) 🔥
- Memory (+6)
- Context Engineering (+5)
- Knowledge Graphs & Ontology (+5)
- Action — acting through tools and reusable skills (+19) 🔥
- Adaptation & Self-Improvement — getting better from experience (+59) 🔥
- Core Cognition — reason, plan, ideate, perceive (+3)