Autonomous Reproducibility

Trust every machine learning paper.

VerityLab AI builds agents that replicate and verify machine learning research, starting with LLM quantization. We read each paper, execute the code, benchmark the results, and publish what truly holds up—turning peer review into measurable verification.

Why it matters

Transparency, accountability, trust.

Autonomous verification closes the gap between ambitious research claims and repeatable results. By validating every step—from data to metrics—we make AI progress auditable.

  • Agentic reproduction pipelines
  • Automated code execution & reporting
  • Evidence-backed release notes

Our mission

Make research verification as automatic as training.

We believe the next generation of AI breakthroughs must be built on verifiable foundations. Our system ingests research artifacts, reconstructs experiments, and tracks reproducibility metrics so the community can rely on trustworthy benchmarks.

Paper ingestion

Structured parsing of PDFs, appendices, and repos to capture experimental intent alongside executable code.

Autonomous execution

Containerized runs mirror author environments to reproduce results without manual babysitting.

Verification insights

Detailed diffs, alerts, and confidence scores help teams trust what they ship and cite.

Demo walkthrough

How the agent verifies OmniQuant.

We run our autonomous verifier against OmniQuant: Omnidirectionally Calibrated Quantization for Large Language Models to ensure the reported W4A4 LLaMA-7B metrics hold up. The agent ingests the paper, clones the repo, then executes the quantization scripts end-to-end before publishing a reproducibility verdict.

  • Repository OpenGVLab/OmniQuant
  • Model target LLaMA-7B W4A4 quantization
  • Claimed metric 2.58 ppl on C4 subset

  1. 01 · Ingest & plan

    Structured extraction

    Parse PDF + README, capture training recipe, dependency matrix, expected checkpoints, and evaluation harness.

    Artifacts normalized in less than 2 minutes.

  2. 02 · Environment build

    Deterministic sandbox

    Provision CUDA 12.1 container, install OmniQuant requirements, pull LLaMA weights, and seed datasets for the C4 validation split.

    Hash-locked images guarantee replayability.

  3. 03 · Execution trace

    Agentic runbook

    Execute scripts/run_llama.sh --bits 4 --act-bits 4, capture logits, and log intermediate perplexity curves.

    Autonomous retries triggered on divergence.

  4. 04 · Verification

    Result comparison

    Reported ppl 2.58 vs reproduced 2.63 (Δ +1.9%). Confidence band within tolerance, so the claim is verified.

    Delta + explanation logged to evidence vault.

OmniQuant verification verdict

Model: LLaMA-7B Bits: W4A4 Dataset: C4 subset

Reported ppl

2.58

Measured ppl

2.63

Δ %

+1.9%

Evidence bundle includes container hash, logs, and reproducibility script for downstream audits.

How it works

01

Read & extract

The agent ingests each paper, repository, and dataset to create a reproducibility blueprint.

02

Replica lab run

We execute the code, enforce dependencies, and compare metrics against the author’s claims.

03

Verified report

Results are packaged into a shareable report with pass/fail status, deltas, and insights.

Early partners

Put your research on a verifiable foundation.

Whether you ship models or evaluate them, VerityLab AI is your co-pilot for reproducibility.

Stay in the loop

Join the VerityLab mailing list.

Receive launch updates, verification reports, and early access to the autonomous agent. We send meaningful updates—nothing else.