# Ecai Writing Style > Use when writing or tightening an ECAI paper body — leading with a general-AI contribution on the first page, matching evidence to claim (a theorem and construction, or a fair empirical comparison), and doing it inside a 7-page body where every paragraph must earn its space. - Skill: `thedixitjain/ecai-writing-style` (Agent Skill) - Install (CLI): `npx skillmds add thedixitjain/ecai-writing-style` - Raw SKILL.md: https://api.skillmd.com/api/skills/thedixitjain/ecai-writing-style/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Coding & Dev Tools - Author: thedixitjain (https://skillmd.com/u/thedixitjain) - Updated: 2026-09-08 - Page: https://skillmd.com/skills/thedixitjain/ecai-writing-style --- # 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`](../../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: | Claim shape | Evidence ECAI expects | |---|---| | "This always holds / is complete / is optimal" | A **proof**, with all assumptions explicit | | "This is more efficient / expands fewer nodes" | A **controlled comparison** vs a fair baseline, with spread | | "This learns better / calibrates better" | A fair empirical comparison, seeds, and a reason *why* (not just a number) | | "This works in deployment" | A credible real-world demonstration (route to **PAIS**) | 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 ```text [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: [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 ``` --- **Source:** [`brycewang-stanford/Awesome-Journal-Skills`](https://github.com/brycewang-stanford/Awesome-Journal-Skills) → `ECAI-Skills/skills/ecai-writing-style/SKILL.md`