ICALP Experiments (proof strategy, and computation in service of proofs)
SkillDev toolsUse when matching the argument of an ICALP (EATCS) theory paper to its claim, choosing the proof strategy for an upper or lower bound, deciding when supporting computation (SAT/SMT-verified base cases, computer-assisted case analysis, exhaustive small-case checks) legitimately backs a theorem, and keeping any such computation reproducible without turning a proof paper into an experimental one.
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What this skill tells your AI
The instructions your AI receives, as published by brycewang-stanford/awesome-journal-skills in ICALP-Skills/skills/icalp-experiments/SKILL.md and read by ahel’s review.
At ICALP there is usually no experiment section — the evidence for the claim is the proof. This
skill is therefore about matching the argument to the claim shape, and about the narrow, real
cases where computation supports a theorem (a computer-assisted proof, an SMT-checked base case, an
exhaustive small-case verification). It is deliberately not an empirical-evaluation guide: a paper
whose contribution is a benchmark result is mis-routed (icalp-topic-selection).
Match the argument to the claim
| Claim shape | The argument that fits | Common failure caught by referees |
|---|---|---|
| Upper bound / faster algorithm | Algorithm + correctness proof + complexity analysis | Correctness hand-waved; complexity ignores a hidden cost |
| Approximation ratio | An analysis bounding cost vs optimum, with a tight example | Ratio proved only on the easy case; no tight instance |
| Lower bound (unconditional) | A reduction, adversary, or information-theoretic argument | Model too weak to be interesting, or gap left open |
| Conditional lower bound | A fine-grained reduction from SETH/3SUM/APSP | Wrong assumption invoked; reduction loses a factor |
| Decidability / complexity (Track B) | A decision procedure + matching hardness | Procedure sketched; hardness for a different fragment |
| Dichotomy / characterization | Exhaustive case analysis with each case proved | A case silently dropped; "similarly" hiding a hard case |
When computation legitimately supports a theorem
Some ICALP results genuinely rely on computation. It must be rigorous and checkable, not suggestive:
- Exhaustive small-case verification — checking a property for all objects up to size k as a base case of an induction. State the exact range, the encoding, and make the search reproducible.
- SAT/SMT-certified steps — using a solver to verify a finite gadget or unsatisfiability. Ship the encoding and, where possible, an independently checkable certificate (UNSAT proof, Farkas witness), not just "the solver said so."
- Computer-assisted case analysis — a program enumerating cases in a proof. The program is part of the proof; its logic must be described and its output verifiable.
The bar: a referee (or a reader of the full version) must be able to re-run or independently check the computation. A number a solver produced with no reproducible input is not a proof step.
Keep it a proof paper, not an experiment paper
- Supporting computation certifies a step; it does not replace the theorem. If the only evidence for the main claim is "it worked on our instances," the paper is experimental and belongs elsewhere.
- Do not add a benchmark table to a theory paper to look more complete — ICALP referees read it as either irrelevant or as a signal the theorem is weak.
- Running time measured is not running time proved. The contribution is the provable bound.
Reproducibility of the computational part
If computation backs a proof, treat it like the full version (see icalp-reproducibility):
- Provide the code and inputs (or precise pseudocode) in the appendix / full version or a public repository referenced at camera-ready.
- Provide certificates an independent checker can verify where the technique allows.
- Pin versions (solver, seed if randomized search) so the check is deterministic.
Worked vignette: a dichotomy with a computer-checked base
A Track B paper proves a dichotomy over a family of constraint languages: tractable vs NP-hard. The inductive step is by hand; the base cases (finitely many small languages) are verified by an exhaustive program. To meet the bar: state the finite base set precisely, describe the enumeration, ship the code and its output in the full version, and — for the hardness base cases — include reductions a referee can check by hand rather than leaving them to the program alone. State clearly which cases are machine-verified and which are proved analytically.
Output format
[Claim shape] upper / approximation / lower (uncond) / lower (conditional) / decidability / dichotomy
[Argument fit] the proof strategy matches the claim? gaps: <where>
[Computation role] none / base-case check / solver-certified step / computer-assisted cases
[Checkability] certificate or reproducible input provided? independent check possible? yes/no
[Not-an-experiment guard] is the theorem the evidence (not benchmark performance)? yes/no
[Fix queue] <ordered: proof gaps, missing certificates, mis-routed empirical framing>
Signals
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- Last commit
- Sep 2026
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icalp-experiments- Source
- github.com/brycewang-stanford/awesome-journal-skills
github.com/brycewang-stanford/awesome-journal-skills
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