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Agents for Humans: Turning a paper claim into evidence with Paper2Proof

Paper2Proof turns one bounded computational-paper claim into isolated execution, deterministic verification, and content-addressed evidence—pausing whenever scientific judgment or authorization belongs to a human.

Computational researchers rarely stop at reading a paper. To decide whether a result is useful, they repeatedly locate a numerical claim, recover its conditions, find or write the smallest corresponding experiment, build an environment, run it, compare the result, and record what happened.
Most research assistants stop at search, summary, or question answering. That still leaves the researcher with a paper, not evidence.
I built Paper2Proof for the Agents for Humans Hackathon to close that gap. It is an autonomous reproducibility steward for independent researchers and small teams. A user gives it a public arXiv URL or selects a transparent controlled demo. Six specialized Strands Agents roles move one bounded claim through:
  1. source intake and risk assessment;
  1. numerical claim extraction;
  1. reproduction planning;
  1. isolated execution;
  1. deterministic verification and critique; and
  1. evidence reporting and the next human decision.
The output is not a confident paragraph. It is a content-addressed evidence object with the exact source locator and hash, reported value and tolerance, code, runtime provenance, observed output, state transitions, agent/tool trace, limitations, verdict, and next action.

The human is an authority boundary

“Human in the loop” can become a vague safety phrase. Paper2Proof makes it a state transition.
If a claim has multiple valid interpretations, a license is unclear, a plan needs network access, a dependency is undeclared, or an operation could write externally, the job does not silently guess. It reaches AWAITING_APPROVAL or BLOCKED with structured reasons and concrete options. A prohibited operation cannot be approved into safety.
This division is the product's central rule:
Models propose. Deterministic policy and explicit human approval authorize.
The model never owns URL policy, code safety, budgets, state transitions, or the PASS/MISMATCH comparison. That authority stays in reviewable code.

A successful run is evidence, not truth

One of the easiest mistakes in reproducibility tooling is to equate a process that exited successfully with a paper that is scientifically reproduced. Paper2Proof keeps these separate.
A PASS means one observed number matched one reported number under the recorded conditions and tolerance. The evidence still states its limitations. A real public-paper run may correctly end at AWAITING_APPROVAL because source licensing or scientific interpretation requires a person.
That is not an incomplete demo. It is the intended behavior of a responsible research agent.

Try the public service

Start with Reproducible mean to see an end-to-end AgentCore Code Interpreter run. Then try Ambiguous spread to see a human decision stop execution.
Paper2Proof does not promise to reproduce every paper. It automates the bounded work that can be made inspectable, and returns the rest to the human whose judgment matters.
Any opinions in this article are those of the individual author and may not reflect the opinions of AWS.
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