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

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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 Tracking

pythonCopy code
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']}")

Agent Benchmarks

pythonCopy code
benchmarks = {
    "SWE-bench": {
        "task": "Resolve real GitHub issues",
        "metric": "% resolved",
        "top_score": "49% (Claude 3.5 + SWE-agent)",
    },
    "WebArena": {
        "task": "Complete web tasks in realistic sites",
        "metric": "Task success rate",
        "top_score": "35.8%",
    },
    "GAIA": {
        "task": "General AI assistant tasks",
        "metric": "Accuracy across levels",
        "top_score": "Level 1: 75%, Level 3: 30%",
    },
    "AgentBench": {
        "task": "8 diverse agent environments",
        "metric": "Overall score",
    },
    "ToolBench": {
        "task": "API tool selection and chaining",
        "metric": "Pass rate",
    },
}

for name, info in benchmarks.items():
    print(f"\n{name}: {info['task']}")
    print(f"  Metric: {info['metric']}")
    if "top_score" in info:
        print(f"  SOTA: {info['top_score']}")

Reading Roadmap

markdownCopy code
### Foundations
1. "Chain-of-Thought Prompting" (Wei et al., 2022)
2. "ReAct: Synergizing Reasoning and Acting" (Yao et al., 2023)
3. "Toolformer" (Schick et al., 2023)

### Planning & Memory
4. "Tree of Thoughts" (Yao et al., 2023)
5. "Reflexion" (Shinn et al., 2023)
6. "Generative Agents" (Park et al., 2023)

### Multi-Agent
7. "MetaGPT" (Hong et al., 2023)
8. "AutoGen" (Wu et al., 2023)
9. "ChatDev" (Qian et al., 2023)

### Applications
10. "SWE-agent" (Yang et al., 2024)
11. "The AI Scientist" (Lu et al., 2024)

Use Cases

1. Literature survey: Track the fast-moving agent research field
2. System design: Learn from agent architecture patterns
3. Benchmark comparison: Compare agent frameworks
4. Research direction: Identify open problems in agent AI
5. Course material: Teach LLM-based agent systems

References

• Autonomous-Agents GitHub
• LLM-Agent-Paper-List
• Agent Survey