Ai Agent Papers Guide

AI Agent Papers Guide (2024-2026)

Overview

A focused collection of AI agent research papers from 2024-2026, tracking the latest developments in LLM-based agent systems. Unlike broader collections, this focuses on recent breakthroughs — new architectures, benchmarks, multi-agent coordination, and real-world applications. Updated frequently as the field evolves rapidly.

Paper Categories

Copy code
Recent AI Agent Research
├── Agent Architectures
│   ├── Planning (o1-style reasoning, search-augmented)
│   ├── Memory (long-term, episodic, working)
│   └── Tool use (function calling, code execution)
├── Multi-Agent Systems
│   ├── Collaboration (task decomposition, debate)
│   ├── Competition (red team, adversarial)
│   └── Emergence (self-organization, culture)
├── Evaluation
│   ├── Benchmarks (SWE-bench, WebArena, GAIA)
│   ├── Safety (jailbreak, misuse, alignment)
│   └── Reliability (error recovery, hallucination)
├── Applications
│   ├── Software engineering (coding agents)
│   ├── Scientific research (lab automation)
│   ├── Web automation (browsing, form-filling)
│   └── Enterprise (workflow, data analysis)
└── Infrastructure
    ├── Frameworks (LangGraph, CrewAI, AutoGen)
    ├── Protocols (MCP, A2A, tool standards)
    └── Deployment (scaling, monitoring, cost)

Highlighted Papers (2024-2025)

Tracking New Papers

pythonCopy code
import arxiv
from datetime import datetime, timedelta

def find_recent_agent_papers(days=14):
    """Find cutting-edge agent papers."""
    queries = [
        "ti:agent AND (ti:LLM OR ti:language model)",
        "abs:autonomous agent AND abs:tool use AND abs:2024",
        "ti:multi-agent AND abs:large language",
        "abs:coding agent OR abs:software agent",
    ]

    seen = set()
    papers = []

    for q in queries:
        search = arxiv.Search(
            query=q, max_results=15,
            sort_by=arxiv.SortCriterion.SubmittedDate,
        )
        for r in search.results():
            if r.entry_id not in seen:
                seen.add(r.entry_id)
                papers.append({
                    "title": r.title,
                    "date": r.published.strftime("%Y-%m-%d"),
                    "url": r.entry_id,
                })

    papers.sort(key=lambda x: x["date"], reverse=True)
    for p in papers[:20]:
        print(f"[{p['date']}] {p['title']}")
        print(f"  {p['url']}")

find_recent_agent_papers()

Framework Comparison

pythonCopy code
frameworks = {
    "LangGraph": {
        "paradigm": "Graph-based workflows",
        "persistence": "Built-in checkpointing",
        "multi_agent": "Yes",
        "language": "Python/JS",
    },
    "CrewAI": {
        "paradigm": "Role-based agents",
        "persistence": "Memory module",
        "multi_agent": "Yes (crew)",
        "language": "Python",
    },
    "AutoGen": {
        "paradigm": "Conversational agents",
        "persistence": "Chat history",
        "multi_agent": "Yes (group chat)",
        "language": "Python/.NET",
    },
    "OpenHands": {
        "paradigm": "Computer use agent",
        "persistence": "Workspace state",
        "multi_agent": "No",
        "language": "Python",
    },
}

for name, info in frameworks.items():
    print(f"\n{name}:")
    for k, v in info.items():
        print(f"  {k}: {v}")

Use Cases

1. Literature tracking: Stay current on agent research
2. Framework selection: Compare agent development tools
3. Research planning: Identify open problems and trends
4. Course material: Teach cutting-edge agent systems
5. Benchmark tracking: Compare agent capabilities

References

• awesome-ai-agent-papers
• VoltAgent Framework