--- name: european-conference-on-artificial-intelligence description: Use when targeting European Conference on Artificial Intelligence (ECAI) or deciding whether a computer-science manuscript fits this venue. Encodes conference fit, framing, evidence bar, submission-cycle checks, rebuttal posture, and desk-reject risks for AI/ML regional flagship. --- # European Conference on Artificial Intelligence (ECAI) ## Conference positioning European Conference on Artificial Intelligence (ECAI) is a top computer-science conference venue for broad European AI research across reasoning, learning, agents, planning, and applications. It rewards a general AI paper positioned for cross-area European AI readership. Treat this skill as a **fit / venue-selection / re-framing** tool for conference submission strategy, not as a substitute for the current year's CFP, author kit, ethics policy, or submission portal. Because CS conferences change deadlines, templates, page limits, review workflow, artifact rules, AI-use policy, and rebuttal formats every cycle, always verify the live official instructions before making a submission-ready recommendation. Start from the official source anchor recorded for this venue in `../../resources/conference-roster.md` and `../../resources/official-source-map.md`. ## When to trigger - The author names ECAI / European Conference on Artificial Intelligence as the target venue. - A manuscript in broad European AI research across reasoning needs a conference-fit read before being formatted or submitted. - The paper must be re-framed from journal style or arXiv style into a selective CS conference narrative. - The author needs an evidence-gap, anonymity, artifact, rebuttal, or re-routing diagnosis for this venue. ## Scope & topic fit - Core fit: broad European AI research across reasoning, learning, agents, planning, and applications. - Best submissions make a precise contribution type visible: algorithm, theorem, system, dataset, benchmark, empirical finding, design artifact, tool, or socio-technical analysis. - The paper should explain why the result matters to ECAI's reviewers, not just why it is interesting to the authors' lab or product context. - Position related work against the most recent conference-cycle papers in this venue and its closest siblings; stale comparisons are a common early-review weakness. - If the contribution is interdisciplinary, state which part is CS research and which part is domain evidence. ## Venue-specific calibration - Reviewer lens: Treat ECAI as the European broad-AI venue with EurAI community orientation. It is appropriate for general AI work across reasoning, agents, planning, knowledge, and learning, especially when the European AI audience and cross-area conversation are natural. - Contribution hook to foreground: the venue-specific contribution bar. - Scope vocabulary to use naturally in the abstract and introduction: broad European AI research across reasoning, learning, agents, planning, and applications. - Distinctive fingerprint for reviewer calibration: broad, european, across, reasoning, learning, agents, planning, applications, venue-specific, contribution, regional, flagship, ecai2026. - Official anchor domain: ecai2026.org. Quote annual rules only after opening that source and the current-year CFP/author kit. ## Method & evidence bar - Compare against current strong baselines and explain exactly what changes in the algorithm, objective, data, or inference procedure. - Report ablations that isolate the claimed mechanism; do not rely on aggregate benchmark wins alone. - Document data, compute, hyperparameters, model selection, and failure cases so the result can be reviewed as science rather than demo output. - For ECAI, the evidence must support the venue-specific signature: a general AI paper positioned for cross-area European AI readership. - Include limitations, negative results, compute/resource reporting, data provenance, and ethics details when they affect the claim. ## Structure & house style - Frame the contribution as a reusable idea: method, theory, benchmark, dataset, system, or socio-technical finding. - Separate main claims from exploratory results; reviewers at top AI venues punish overclaiming and hidden cherry-picking. - Use the current official template exactly; do not guess page limits, font sizes, supplement rules, anonymity exceptions, or camera-ready requirements from old cycles. - The introduction should answer: problem, why now, what is new, why this venue, and what evidence proves the claim. - Put the strongest result in the main paper, not only in the appendix or supplement; reviewers should not have to reconstruct the contribution. ## Official-cycle checklist - Open the live official venue page: https://ecai2026.org/ - Re-check the current cycle's CFP, author kit, submission system, abstract/paper deadlines, page limits, supplementary-material rules, anonymity policy, dual-submission policy, ethics policy, AI-use policy, artifact/code/data expectations, rebuttal/author-response format, and camera-ready requirements. - Confirm the review workflow and portal: OpenReview / CMT / HotCRP / PCS / START or society portal, as specified for the current cycle. - Check whether accepted papers require in-person presentation, separate registration, artifact badges, proceedings copyright, or post-acceptance release forms. - If the live official instructions conflict with this skill, the official instructions win. ## Pre-submission self-check - [ ] One sentence states why this manuscript belongs at ECAI, using the venue's scope rather than generic "top conference" language. - [ ] The claim is calibrated to the evidence: no broader than the datasets, proofs, systems, user studies, deployments, or threat model support. - [ ] Related work includes the nearest current-cycle AI/ML regional flagship papers and explains the technical delta. - [ ] The paper satisfies the current official template, anonymity, ethics, artifact, and rebuttal requirements. - [ ] The main paper is self-contained enough for reviewers to evaluate novelty and correctness without hunting through external links. ## Common desk-reject triggers - Leaderboard-only novelty with weak explanation of why the method works. - Unclear data contamination, missing baselines, or evaluation that cannot be reproduced. - Claims about safety, fairness, health, or society without matching evidence and limitations. - Formatting, anonymity, dual-submission, external-link, or supplement violations under the current-year policy. - A contribution framed for a neighboring field while giving ECAI reviewers too little technical or empirical substance. ## Re-routing decision If the paper misses ECAI's bar, compare against `neural-information-processing-systems` / `international-conference-on-machine-learning` / `international-conference-on-learning-representations` / `aaai-conference-on-artificial-intelligence`. Re-route based on contribution type, not prestige: theory to a theory venue, systems to a systems venue, application-heavy work to a domain venue, and early ideas to workshops or shorter tracks when the official CFP supports them. ## Output format ```text [Fit] High / Medium / Low (one-line reason) [Target] European Conference on Artificial Intelligence (ECAI) [Contribution type] algorithm / theory / system / dataset / benchmark / empirical / design / security / other [Main evidence gap] [Official items to re-check] CFP / author kit / deadline / format / anonymity / ethics / AI-use / artifact / rebuttal / camera-ready [Top rejection risk] [Re-route suggestion] ```