Theorist Toolbox: Tools for Agent Based LLM-assisted economic theory Research

Empirical economists often start their projects with a toolbox.

Shared packages, replication archives, and circulated guides shorten the time between and idea and a rough initial draft.

Theorists, on the other-hand, largely start from a blank page.

By 2026, large language models can a produce and check nontrivial mathematics.

The can also hallucinate and write wrong claims very convincingly.

The current bottleneck on machine-assisted theory is no longer production but trust: a model will claim to prove a false theorem as readily as a true one.

Building on recent attempts in mathematics, I present 3 methods for doing economic theory with a language model.

These methods differ on how the work is verified: a single disciplined pass, an adversarial prover-verifier pair (Claude Opus~4.8 proposing, OpenAI Codex refuting), and a structured multi-agent project with a reviewer gate (inspired by the Google co-mathematician architecture).

I demonstrate these protocols on one open worked example: designing a Groves/Pigouvian incentive mechanism for the Gans--Kominers eigengrade model of grade inflation.

None of the three runs produced a strict direct-revelation VCG/Clarke mechanism (as requested, perhaps due to the non-existence of such mechanism).

Three phenomena recur.

First, convergent discovery: two runs derive the same effective-resistance externality kernel on opposite margins.

Second, adversarial verification is load-bearing: the pair caught three of its own false claims and the gate rejected a sub-goal.

Third, polish is not rigor: the most finished-looking output was the least verified.

The methodological takeaway is that external verification, not model capability, is the design variable.