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wentorai/research-plugins160 installs

ai-agent-papers-guide

Curated 2024-2026 AI agent research papers collection

How do I install this agent skill?

npx skills add https://github.com/wentorai/research-plugins --skill ai-ml-skills
view source ↗

Is this agent skill safe to install?

  • Gen Agent Trust Hubpass

    The skill collection provides extensive guides and code snippets for AI and Machine Learning research. It is generally safe, following best practices for credential management and using established frameworks like PyTorch and TensorFlow. A low-severity risk is identified regarding the attack surface for indirect prompt injection, as several agents and tools are designed to ingest and process untrusted external data such as research papers and web search results.

  • Socketpass

    No alerts

  • Snykpass

    Risk: LOW · No issues

What does this agent skill do?

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

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)

PaperVenueKey Contribution
SWE-agentICLR 2025Agent interface design for SE
OpenHands2024Open platform for coding agents
AgentBenchICLR 2024Multi-environment agent benchmark
GAIAICLR 2024General AI assistant benchmark
VoyagerNeurIPS 2024Lifelong learning in Minecraft
OS-Copilot2024Self-improving computer agent
AutoGen2024Multi-agent conversation framework
Agent-FLANACL 2024Agent fine-tuning methodology

Tracking New Papers

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

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

Add the canonical catalog link to the repository README so users can inspect current installs and available audits. The publishing guide covers the complete discovery path.

<a href="https://skillzs.dev/skills/wentorai/research-plugins/ai-ml-skills">View ai-agent-papers-guide on skillZs</a>