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An open preprint server for the AI era. Structured overviews, pinned code, and reproduction records on every paper.

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© 2026 AutoXiv · Open preprint infrastructureFree to submit · No APCs · Agent-readable over MCP
Open preprint server

Preprints with code, overviews, and verification.

AutoXiv hosts research papers with a pinned repository, a structured AI overview, and an agent-run reproduction record. Free to submit. Readable by people and by agents.

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103
papers indexed
21
sandboxed reproduction runs
490
confirmed claims in the ledger
How these numbers are made →
One paper recordautoxiv.YYMMDD.NNNN
  • OverviewStructuredTL;DR · problem · approach · results · limitations
  • CodePinned commitRepository locked to the exact revision the paper describes
  • ReproductionSandboxed runInstall · run · compare, with the verdict clamped to observed evidence
  • ClaimsTyped ledgerExtracted, blind-confirmed, anchored to page and bounding box
  • AskGrounded Q&AAnswers drawn from the record — overview, confirmed claims, runs — not from memory
  • AccessMCP + webSame record for readers and for agents
How it works

Every paper arrives as a working object, not a PDF.

01

Submit — or register an arXiv paper

Upload a PDF, or paste an arXiv link and AutoXiv imports the record and links the original PDF. Add the GitHub repository at a specific commit. No fees, no embargo, no queue.

02

Overview and screening in minutes

A structured overview — problem, approach, results, limitations — is generated and the submission is screened before it goes live.

03

Verification you can inspect

A sandboxed agent installs and runs the code, typed claims are extracted and blind-confirmed, and every verdict is clamped to what the server observed.

Latest papers

Recently published

LatestMost readWith code
260425.0003Apr 25, 2026
Bayesian Optimization: A Pure-Python Sequential Model-Based Optimization Library
Authors pending · Reference implementation
Bayesian optimization via Gaussian Process surrogate — installs with pip install bayesian-optimization, runs in <5s with no GPU.
4 agent runs
cs.LG→
260425.0002Apr 25, 2026
Fundamental Machine Learning Algorithms Implemented from Scratch in Python
Authors pending · Reference implementation
A from-scratch NumPy implementation of 20+ ML algorithms with runnable example scripts.
3 agent runs
cs.LG→
Built to be used

Six ways to put the corpus to work.

Ask AutoXiv

A question answered across every paper on the platform, grounded in their overviews and evidence, with citations that open the paper.

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Reproducibility check

Before you submit: a linter that reads your PDF and repository and scores how reproducible the release is, with concrete fixes.

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Reproduction agent

Clones a paper's repository into a sandbox, installs, runs, and compares the output with the paper's claims.

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Bounty marketplace

Pool stakes on papers that still need an independent reproduction. Claims link to verified runs.

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Alpha Machine

Screens a paper for tradable signals and drafts a strategy spec and backtest scaffold. Research tooling, not advice.

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MCP server

Free to submit. No fees, no embargo.

Bring a PDF and a repository. Your paper is live, summarized, and ready to be reproduced within minutes.

Submit a paperFor labs and companies
260425.0001Apr 25, 2026
micrograd: A tiny scalar-valued autograd engine and neural net library
Authors pending · Reference implementation
micrograd is a ~200-line pure-Python autograd engine + neural net library that backpropagates through scalar operations.
4 agent runs
cs.LG
→
260423.0001Apr 22, 2026
FedSIR: Spectral Client Identification and Relabeling for Federated Learning with Noisy Labels
Gholami, Ali, Haghighi +2
FedSIR is a federated learning framework that identifies noisy labels and clients by analyzing spectral properties of feature representations, then uses clean clients to help relabel corrupted samples. The method combines spectral analysis with noise-aware training strategies to achieve robust collaborative learning despite label noise.
1 agent run
Formal Sciences→
260423.0002Apr 22, 2026
Closing the Domain Gap in Biomedical Imaging by In-Context Control Samples
Sanchez-Fernandez, Pinetz, Zellinger +1
This paper proposes CS-ARM-BN, a meta-learning method that uses negative control samples to adapt deep learning models to new experimental batches in biomedical imaging, closing the domain gap caused by batch effects. The approach achieves 0.935±0.018 accuracy on drug mechanism-of-action classification, recovering performance from 0.862±0.060 back to near training-domain levels of 0.939±0.005.
Formal Sciences→
260423.0003Apr 22, 2026
Global Offshore Wind Infrastructure: Deployment and Operational Dynamics from Dense Sentinel-1 Time Series
Hoeser, Bachofer, Kuenzer
This paper presents a global dataset of Sentinel-1 SAR time series tracking offshore wind infrastructure deployment and operations from 2016-2025, with 15,606 time series and expert-annotated benchmarks for monitoring wind farm construction and operational dynamics. The dataset enables independent, high-resolution global monitoring of the rapidly expanding offshore wind sector.
Formal Sciences→
Browse all 103 papers →

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