Conference on Machine Learning and Systems (MLSys)
Conference positioning
Conference on Machine Learning and Systems (MLSys) is a top computer-science conference venue for machine learning systems, training/inference infrastructure, compilers, data systems, and hardware-aware ML. It rewards a paper where the systems contribution changes ML capability, cost, reliability, or deployment scale. 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 MLSys / Conference on Machine Learning and Systems as the target venue.
A manuscript in machine learning systems 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: machine learning systems, training/inference infrastructure, compilers, data systems, and hardware-aware ML.
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 MLSys'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: Read reviewers as systems builders for ML. Training, inference, compilers, accelerators, data pipelines, and deployment constraints must be measured, not only described.
Contribution hook to foreground: the venue-specific contribution bar.
Scope vocabulary to use naturally in the abstract and introduction: machine learning systems, training/inference infrastructure, compilers, data systems, and hardware-aware ML.
Official anchor domain: mlsys.org. Quote annual rules only after opening that source and the current-year CFP/author kit.
Close-neighbor routing guardrail
Use this profile only when the manuscript's central contribution is genuinely in ML systems
and the author can say why MLSys reviewers are the primary audience, not merely a convenient
deadline.
Closest roster neighbors to compare before final routing: uncertainty-in-artificial- intelligence (UAI), conference-on-learning-theory (COLT), conference-on-lifelong- learning-agents (CoLLAs), international-conference-on-automated-machine-learning (AutoML
Conference). Break ties by contribution type, evidence shape, reviewer community, and the
current official CFP from mlsys.org.
MLSys-specific routing detail
Prefer MLSys when the contribution changes ML training, inference, serving, compiler/runtime behavior, data pipeline, hardware utilization, reliability, cost, or deployment scale.
Route autonomous-agent interaction, negotiation, incentives, or decentralized decision-making to AAMAS; route algorithmic ML without systems constraints to NeurIPS/ICML/ICLR.
MLSys evidence should include system bottlenecks, throughput/latency/cost/reliability tradeoffs, and realistic ML workloads rather than only benchmark accuracy.
Method & evidence bar
Build the artifact or prototype far enough that the core design can be measured under realistic workloads.
Use appropriate baselines, sensitivity analyses, and workload characterization; systems reviewers look for hidden bottlenecks.
Separate engineering effort from research contribution: name the abstraction, mechanism, or tradeoff.
For MLSys, the evidence must support the venue-specific signature: a paper where the systems contribution changes ML capability, cost, reliability, or deployment scale.
Include limitations, negative results, compute/resource reporting, data provenance, and ethics details when they affect the claim.
Structure & house style
Start from a systems pain point and show why existing abstractions fail.
Use evaluation sections that answer research questions, not a tour of every benchmark run.
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.
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: the current USENIX/ACM/IEEE author kit, artifact policy, and submission system.
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 MLSys, 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 ML systems 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
Toy implementation or microbenchmark-only evidence for a systems claim.
No comparison to mature systems or no explanation of deployment constraints.
Performance gains with unclear workload representativeness.
Formatting, anonymity, dual-submission, external-link, or supplement violations under the current-year policy.
A contribution framed for a neighboring field while giving MLSys reviewers too little technical or empirical substance.
Re-routing decision
If the paper misses MLSys's bar, compare against acm-symposium-on-operating-systems-principles / usenix-symposium-on-operating-systems-design-and-implementation / usenix-symposium-on-networked-systems-design-and-implementation / acm-sigcomm. 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
[Fit] High / Medium / Low (one-line reason)
[Target] Conference on Machine Learning and Systems (MLSys)
[Contribution type] algorithm / theory / system / dataset / benchmark / empirical / design / security / other
[Main evidence gap] <single most important missing proof, experiment, study, artifact, or policy check>
[Official items to re-check] CFP / author kit / deadline / format / anonymity / ethics / AI-use / artifact / rebuttal / camera-ready
[Top rejection risk] <venue-specific risk>
[Re-route suggestion] <better-matched conference or journal if not a fit>
