Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add wentorai/research-plugins --skill ai-agent-papers-guidegit clone --depth 1 https://github.com/wentorai/research-pluginsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/wentorai/research-plugins/ai-agent-papers-guide)<a href="https://agentmods.dev/skills/wentorai/research-plugins/ai-agent-papers-guide"><img src="https://agentmods.dev/badge/skills/wentorai/research-plugins/ai-agent-papers-guide.svg" alt="Measured on agentmods" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00019 | $0.01198 |
| Opus 5 | $0.00010 | $0.00599 |
| Sonnet 5 | $0.00004 | $0.00240 |
| Haiku 4.5 | $0.00002 | $0.00120 |
Grade A, and why
ai-agent-papers-guide scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 8d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 147 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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()
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 8d ago First seen · 147 lines · 19 tokens per session scan A cf7ff9d873ad
ai-agent-papers-guide is a skill published in the GitHub repository wentorai/research-plugins (288 stars, last pushed 2mo ago), licensed MIT. It adds 19 tokens to every session and 1,198 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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