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-skillsIs 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
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- 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)
| Paper | Venue | Key Contribution |
|---|---|---|
| SWE-agent | ICLR 2025 | Agent interface design for SE |
| OpenHands | 2024 | Open platform for coding agents |
| AgentBench | ICLR 2024 | Multi-environment agent benchmark |
| GAIA | ICLR 2024 | General AI assistant benchmark |
| Voyager | NeurIPS 2024 | Lifelong learning in Minecraft |
| OS-Copilot | 2024 | Self-improving computer agent |
| AutoGen | 2024 | Multi-agent conversation framework |
| Agent-FLAN | ACL 2024 | Agent 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
- Literature tracking: Stay current on agent research
- Framework selection: Compare agent development tools
- Research planning: Identify open problems and trends
- Course material: Teach cutting-edge agent systems
- Benchmark tracking: Compare agent capabilities
References
How can the creator link this skill?
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>