ECAI Writing Style

ECAI reviews a 7-page body across the full breadth of AI (symbolic reasoning, KR, planning, search, multi-agent systems, ML, and applications). Two things follow, and they drive most of the advice here: the contribution must be legible to a general AI audience, and the writing must be dense — at 7 pages there is no room for a paragraph that does not carry the argument.

The ECAI first-page arc

Land the whole contribution before the fold:

1. A well-posed AI problem — stated as a problem, not as "X has become popular."
2. Why current methods are inadequate — the specific gap, in one or two sentences.
3. The contribution — the mechanism, and where the claim is provable, the guarantee.
4. Evidence proportional to the claim — a theorem + construction, and/or a fair empirical comparison; named on the first page, delivered in the body.
5. What it means for AI — why a general AI audience should care.

The worked example (../../resources/worked-examples/01-introduction.md) shows this arc rebuilt from a benchmark-first draft.

Lead with the AI contribution, not the application of a model

The most common re-route signal is a paper that leads with "we apply to ." ECAI rewards a contribution that generalizes — a mechanism, a guarantee, a characterization, an understanding — over a single benchmark delta. Apply the model-swap test: if you replaced the underlying model/solver with another, would a lasting AI lesson remain? If not, the paper may belong at a pure-ML venue (ecai-topic-selection).

Match evidence to the claim shape

ECAI's breadth means the right evidence differs by contribution:

A provable claim asserted only empirically is a weakness a reviewer will name; a purely empirical claim dressed as a theorem is worse.

Density discipline (the 7-page reality)

• Paragraph one carries the contribution. Do not spend the opening on the importance of AI.
• Cut the roadmap. At 7 pages the paper cannot afford a "Section 2 does X, Section 3 does Y" preview; a single orienting sentence is enough.
• Define once, precisely. Symbolic-AI reviewers check every later lemma against your definitions; sloppy notation costs you the proof's credibility.
• One figure doing three jobs beats three figures. Merge panels; caption them to be self-contained.
• Push detail, keep the idea. Full proofs and extra tables go to the supplement; the idea and the decision-critical result stay in the body (ecai-supplementary).

Threats / limitations as argument, not boilerplate

State the honest boundary of the claim where it lives — the assumption the theorem needs, the regime where the method stops helping, the confound the experiment cannot rule out. In a single-round, no-revision process (ecai-review-process), a limitation you name yourself is far cheaper than one a reviewer discovers.

Language and audience

• Write for a broad AI reader: define subfield jargon, motivate why a planning/KR/ML reader should care even if it is not their area.
• ECAI is an international European venue; keep the English clear and the claims measured — EurAI's reviewer pool spans many first languages and subfields.
• Avoid overclaiming ("revolutionizes," "solves"); ECAI rewards a precise, bounded contribution.

Anti-patterns

• Benchmark-first abstract that never states an AI problem.
• Model-as-contribution with no lesson surviving a model swap.
• Proof by assertion — a completeness/optimality claim with no proof.
• Roadmap padding eating the 7-page budget.
• Decision-critical content in the supplement because the body ran long.

Output format

[First-page arc] problem / inadequacy / contribution+guarantee / proportional evidence / meaning — all present?
[Model-swap test] does an AI lesson survive swapping the model/solver? yes/no
[Evidence match] claim shape -> proof and/or fair comparison present? gaps: <list>
[Density] roadmap trimmed? paragraph one carries the contribution? figures merged?
[Limitations] stated as argument where the claim lives? yes/no
[Budget] decision-critical content inside 7 pages? yes/no