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Itcs experiments

Skill brycewang-stanford/Awesome-Journal-Skills/ITCS-Skills/skills/itcs-experiments

Use when deciding what counts as evidence for an ITCS theory claim — proofs as the primary evidence, worked examples and separations that make a model concrete, and the rare, well-scoped illustrative computation or simulation — and how to keep any computational content checkable and subordinate to the mathematics.From its SKILL.md

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npx -y skills add brycewang-stanford/Awesome-Journal-Skills --skill itcs-experiments

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SKILL.md

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ITCS Experiments

ITCS is a pure-theory venue: the primary — usually the only — evidence for a claim is a proof. There is no experiments requirement, no benchmark, no leaderboard, and no reviewer expectation of an empirical section. Bringing an ML-conference or SE reflex here (a table of numbers "showing" the method works) misreads the venue: at ITCS a theorem is proved, not measured. This skill is about matching evidence to a theory claim and about the rare case where a small computation genuinely helps.

Proofs are the evidence

  • Every central claim is settled by a complete proof, not by examples. A pattern that "holds in all cases we tried" is a conjecture, not a theorem — label it as such and prove or drop it.
  • Match the claim shape to the argument shape: an upper bound needs a construction + analysis; a lower bound needs an adversary/reduction; a separation needs a witness object; an impossibility needs a contradiction from the assumption. Reviewers check that the kind of argument fits the kind of claim.
  • A new model needs an anchoring result (see itcs-writing-style) — a separation or a surprising possibility that proves the model is neither empty nor everything. That anchoring result is the "experiment" an ITCS PC wants: evidence the model is alive.

Worked examples and separations as evidence

Non-proof evidence that is welcome, because it makes the mathematics concrete:

  • Worked examples that instantiate a definition on a small case and show the intended behavior — invaluable for a new model, and cheap insurance against the "is this trivial?" objection.
  • Explicit separating objects — a small graph, code, distribution, or gadget that witnesses a gap between two settings. If finite, include the object so a reviewer verifies the separation directly.
  • Tight-example constructions showing an analysis cannot be improved — the theory analogue of an ablation, demonstrating the bound is not loose by accident.

The rare, well-scoped computation

Some ITCS papers include a small computational component: a computer search that found a gadget, a SAT/SMT solve certifying a finite separation, a numerically evaluated construction. When one genuinely helps, scope it tightly:

  • It supports a proved claim; it is never the claim. "A search over all graphs on <= 12 vertices found the gadget of Lemma 4, whose properties we then prove" is legitimate. "Our method achieves 92% on a benchmark" is a category error at ITCS.
  • Make it checkable without rerunning. State the exact search space, the tool and version, and — crucially — include the finite object the search produced (the graph, certificate, code) so verification is a static check, not a re-computation. A reviewer should be able to confirm the object has the claimed property by hand or with a one-line check.
  • Report it honestly. If a construction is only verified numerically (not proved), say so and mark exactly which claims rest on computation versus proof.
  • Keep it off the anonymity leak surface. A linked repository under a personal GitHub is a lightweight-double-blind slip; fold the object into an appendix or host it neutrally (see itcs-submission).

What NOT to import from empirical venues

Empirical-venue habitWhy it misfires at ITCS
A benchmark table as the main resultITCS proves; it does not measure. A table cannot establish a theorem
"Outperforms baselines by X%" framingThere are no baselines to beat; the contribution is an idea/proof
Runtime plots to argue efficiencyState and prove the asymptotic bound instead
An artifact/reproducibility package of codeNo artifact track exists; the "artifact" is the proof (see itcs-artifact-evaluation)
Statistical significance / error barsIrrelevant to a deterministic mathematical claim

Decision procedure

[Claim] is it a theorem (prove it) or a pattern (label as conjecture, or prove/drop)?
[Argument fit] upper=construction+analysis / lower=adversary / separation=witness / impossibility=contradiction
[Alive] new model? -> anchoring separation or surprising-possibility result present?
[Compute?] does a small search/solve genuinely help a proved claim? if not, omit it
[Checkable] if compute used: search space + tool/version stated, finite object included?
[Honesty] each claim tagged proved vs. numerically-verified; anonymity leak surface clean?

Output format

[ITCS evidence status] proof-complete / gaps / mis-imported-empirics
[Central claims] each has a complete proof of matching shape? yes/no + list gaps
[Model alive] anchoring result present for any new model? yes/no
[Computation] present? if so: supports-a-proof only? checkable object included?
[Anonymity] no personal-repo leak from any computational content? yes/no
[Fix queue] <ordered edits>

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