autonomous-agents-papers-guide

Autonomous Agents Papers Guide

Overview

A daily-updated collection of research papers on autonomous AI agents — systems that use LLMs for planning, reasoning, tool use, and multi-step task execution. Covers the full agent stack from foundational prompting techniques (ReAct, Chain-of-Thought) to multi-agent systems, memory architectures, and real-world deployments. Organized chronologically with category tags for easy navigation.

Agent Taxonomy

Autonomous Agents
├── Planning & Reasoning
│   ├── Chain-of-Thought (CoT, ToT, GoT)
│   ├── ReAct (Reasoning + Acting)
│   ├── Reflexion (Self-reflection)
│   └── LATS (Language Agent Tree Search)
├── Tool Use & Actions
│   ├── Function calling
│   ├── Code execution
│   ├── Web browsing
│   └── API interaction
├── Memory Systems
│   ├── Short-term (context window)
│   ├── Long-term (vector stores)
│   ├── Episodic (experience replay)
│   └── Procedural (learned strategies)
├── Multi-Agent Systems
│   ├── Debate/discussion (ChatDev, MetaGPT)
│   ├── Hierarchical (manager/worker)
│   ├── Collaborative (shared goals)
│   └── Competitive (adversarial)
└── Applications
    ├── Software engineering (SWE-agent, Devin)
    ├── Scientific research (AI Scientist)
    ├── Web automation (WebArena)
    └── Game playing (Voyager)

Landmark Papers

Paper	Year	Key Contribution
ReAct	2023	Interleaving reasoning and acting
Toolformer	2023	Self-taught tool use
Voyager	2023	Lifelong learning agent in Minecraft
AutoGPT	2023	Autonomous goal-directed agent
MetaGPT	2023	Multi-agent software company
Reflexion	2023	Verbal self-reflection for learning
SWE-agent	2024	Autonomous software engineering
AI Scientist	2024	Autonomous research paper generation
Claude Computer Use	2024	GUI agent via screenshots
OpenHands	2024	Open platform for AI agents

Paper Tracking

import arxiv
from datetime import datetime, timedelta

def find_agent_papers(days=7, max_results=30):
    """Find recent autonomous agent papers."""
    queries = [
        "abs:autonomous agent AND abs:large language model",
        "abs:LLM agent AND (abs:planning OR abs:tool use)",
        "abs:multi-agent AND abs:LLM",
    ]

    seen = set()
    papers = []

    for query in queries:
        search = arxiv.Search(
            query=query,
            max_results=max_results,
            sort_by=arxiv.SortCriterion.SubmittedDate,
        )
        cutoff = datetime.now() - timedelta(days=days)
        for r in search.results():
            if (r.entry_id not in seen and
                r.published.replace(tzinfo=None) > cutoff):
                seen.add(r.entry_id)
                papers.append({
                    "title": r.title,
                    "url": r.entry_id,
                    "date": r.published.strftime("%Y-%m-%d"),
                    "categories": r.categories,
                })

    papers.sort(key=lambda x: x["date"], reverse=True)
    return papers

for p in find_agent_papers(days=14):
    print(f"[{p['date']}] {p['title']}")