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arXiv Paper Summarizer
x402
arXiv Paper Summarizer is a paid API for AI agents from x402-deployer.x402-deployer.workers.dev, paid per call via x402, $0.04/call, status unknown (last checked 2026-09-14).
Fetches an arXiv paper's abstract via the arXiv API and generates a structured LLM summary including TLDR, key findings, methods, limitations, and so-what.
Price / call$0.04
Success—
StatusUnknown
How this service works
arXiv paper summarizer / research-paper TLDR. Fetches abstract via arXiv API + LLM-generated structured summary (TLDR, key findings, methods, limitations, so-what).
Output
A structured JSON summary of the requested arXiv paper including: a TLDR (one-sentence summary), key findings (bullet points), methods description, limitations noted by the authors, and a so-what analysis explaining the paper's broader significance.
Example use cases
Example prompt
“Can you summarize arXiv paper 2310.06825 for me — give me the TLDR, key findings, methods used, limitations, and why it matters?”
Technical details
Endpoint
POSThttps://x402-deployer.x402-deployer.workers.dev/arxiv-summarizeWhen to prefer
Use this endpoint when you need a structured, multi-section summary of a specific arXiv paper rather than just an abstract. Ideal for agents that need to quickly digest academic research and communicate findings in plain language. Prefer over manual abstract retrieval when you want LLM-generated insight layers like limitations and so-what analysis.
Side effects
- Invalid or non-existent arXiv ID returns an error
- arXiv API unavailable causes fetch failure
- LLM generation failure returns partial or no summary
- Malformed request body missing paper ID returns 400
- Rate limiting or payment issues return 402 or 429
Input
{
"input": {
"body": {
"arxiv_id_or_url": "2310.06825",
"include_abstract": true
},
"type": "http",
"method": "POST",
"bodyType": "json"
}
}Schema · Request body
| Field | Type | Description |
|---|---|---|
| inputrequired | object | |
| output | object |
Output
{
"doi": null,
"model": "hermes-3-llama-3.1-405b",
"title": "Mistral 7B",
"source": "morpheus",
"authors": [
"Albert Q. Jiang",
"Alexandre Sablayrolles",
"Arthur Mensch",
"Chris Bamford",
"Devendra Singh Chaplot",
"Diego de las Casas",
"Florian Bressand",
"Gianna Lengyel",
"Guillaume Lample",
"Lucile Saulnier",
"Lélio Renard Lavaud",
"Marie-Anne Lachaux",
"Pierre Stock",
"Teven Le Scao",
"Thibaut Lavril",
"Thomas Wang",
"Timothée Lacroix",
"William El Sayed"
],
"pdf_url": null,
"summary": {
"tldr": "Mistral 7B is a 7-billion-parameter language model that outperforms larger models like Llama 2 13B and Llama 1 34B in various benchmarks while being more efficient.",
"methods": "The model uses grouped-query attention (GQA) for faster inference and sliding window attention (SWA) to handle sequences of arbitrary length with reduced inference cost.",
"so_what": "Mistral 7B demonstrates that smaller, more efficient language models can outperform larger models in various tasks, potentially leading to more accessible and cost-effective AI solutions.",
"keywords": [
"language model",
"grouped-query attention",
"sliding window attention",
"efficiency",
"performance"
],
"limitations": "The abstract does not mention any specific limitations of the model.",
"key_findings": [
"Mistral 7B outperforms Llama 2 13B across all evaluated benchmarks.",
"Mistral 7B surpasses Llama 1 34B in reasoning, mathematics, and code generation tasks.",
"Mistral 7B -- Instruct, a fine-tuned version for following instructions, outperforms Llama 2 13B -- Chat on human and automated benchmarks."
]
},
"updated": "2023-10-10T17:54:58Z",
"abstract": "We introduce Mistral 7B v0.1, a 7-billion-parameter language model engineered for superior performance and efficiency. Mistral 7B outperforms Llama 2 13B across all evaluated benchmarks, and Llama 1 34B in reasoning, mathematics, and code generation. Our model leverages grouped-query attention (GQA) for faster inference, coupled with sliding window attention (SWA) to effectively handle sequences of arbitrary length with a reduced inference cost. We also provide a model fine-tuned to follow instructions, Mistral 7B -- Instruct, that surpasses the Llama 2 13B -- Chat model both on human and automated benchmarks. Our models are released under the Apache 2.0 license.",
"arxiv_id": "2310.06825",
"arxiv_url": "https://arxiv.org/abs/2310.06825",
"published": "2023-10-10T17:54:58Z",
"categories": [
"cs.CL",
"cs.AI",
"cs.LG"
],
"abstract_chars": 671,
"primary_category": "cs.CL"
}Tags
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