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mlsys-topic-selection
GitHub辅助判断项目是否适合MLSys会议，通过共设计测试和路由表区分系统与ML领域，指导选题定位及轨道选择。
Trigger Scenarios
决定论文投稿目标会议
评估ML与系统的相互约束关系
选择研究或工业赛道
Install
npx skills add brycewang-stanford/Awesome-Journal-Skills --skill mlsys-topic-selection -g -y
SKILL.md
Frontmatter
{
"name": "mlsys-topic-selection",
"description": "Use when deciding whether a project belongs at MLSys rather than OSDI, SOSP, NSDI, ASPLOS, ATC, EuroSys, or the ML conferences, testing for genuine ML-systems co-design, choosing between the research and industrial tracks, and sharpening the systems-for-ML or ML-for-systems framing before writing starts."
}
MLSys Topic Selection
Use this before writing begins. MLSys exists for work where machine learning and computer systems constrain each other — the CFP spans systems for ML (training, inference, serving, compilers, runtimes, specialized hardware, hardware-efficient methods, benchmarks and tooling) and ML for systems (learned scheduling, LLM-driven hardware design and system optimization), plus adjacent lanes like federated learning, compound AI and agent systems, privacy/security for ML, and data preparation (2026 topics list, verified 2026-07-08). Fit failures here are usually routing failures: strong work aimed at the wrong reviewer pool.
The co-design test
Ask two questions; MLSys wants both answers to be yes.
- Does an ML property (model structure, training dynamics, tolerance to approximation, request patterns of ML workloads) shape the system design?
- Does a systems property (memory hierarchy, interconnect, scheduling, cost) shape the ML method or its evaluation?
One yes is a re-routing signal: pure systems novelty exercised on an ML workload belongs at a general systems venue; pure ML novelty measured for speed belongs at an ML venue.
Routing table
| Project's center of gravity | Better venue | Why |
|---|---|---|
| New OS/storage/scheduling abstraction, ML is one workload among several | OSDI / SOSP / ATC / EuroSys | Their reviewers reward general-purpose systems depth |
| Network protocol or datacenter fabric for training traffic | NSDI / SIGCOMM | Networking-first reviewer pool |
| Microarchitecture or accelerator design | ASPLOS / ISCA / MICRO | Hardware evaluation norms (simulators, RTL) differ |
| New learning algorithm; efficiency is one experiment | NeurIPS / ICML / ICLR | Algorithmic novelty is the contribution |
| HPC-scale training on supercomputers | SC / HPDC | HPC evaluation culture |
| Mechanism where ML and system constraints genuinely interlock | MLSys | This is the house genre |
| Production ML system's design + deep benchmarks, limited novelty | MLSys industrial track (2026+) | Novelty explicitly not required there |
| Early idea, workshop-scale evidence | An MLSys-adjacent workshop first | Expanded workshop papers can return with chair approval |
Signals your project is MLSys-shaped
- The abstract's key number is a systems metric (goodput, p99, memory, $/token) achieved because of an ML-aware design decision — not incidentally.
- The natural reviewer nightmare is "is this workload representative?" rather than "is this theorem tight?" or "is this abstraction general?"
- The related work splits across systems venues and ML venues — no single community owns
the problem (see
mlsys-related-work). - You can promise an artifact whose evaluation is meaningful on hardware a committee can access — badge culture is part of this venue's identity.
- The contribution survives this compression: "We observed [ML property]; therefore we built [mechanism]; which yields [measured system payoff] at [stated cost]."
Track choice within MLSys (2026 structure)
- Research track: previously unpublished ideas, full double-blind, novelty judged against both literatures.
- Industrial track: built-at-scale systems, design methodology and detailed benchmarks required, novelty not required, company identity may remain visible. Choosing it for a paper whose real asset is a clever new mechanism undersells the mechanism; choosing research track for a deployment report gets it rejected for novelty. Route by the paper's strongest honest asset.
Sharpening moves before committing
Fill these four lines; blanks are routing information.
ML property exploited: ______________________
System constraint engaged: ______________________
Named mechanism: ______________________
Headline systems metric: ______ on workload ______ vs baseline ______
If "named mechanism" is blank -> industrial track or a workshop.
If "ML property" is blank -> general systems venue.
If "system constraint" is blank -> ML venue.
If the metric line is blank -> not ready for any venue; measure first.
- Check the current cycle's topics list before finalizing — the venue's emphasis has drifted fast (the 2026 list added compound AI/agent systems and LLM-driven hardware design; assume continued drift).
- Consider the calendar honestly: MLSys is annual (October deadline in the 2026 cycle). If the project peaks in March, a systems venue with a nearer deadline may serve the work better than a seven-month hold.
Vignette: three borderline projects, routed
- A learned cost model inside a query optimizer, evaluated on analytics workloads. ML-for-systems is in scope, but the reviewer pool that can judge query optimization depth sits at database venues. Route to MLSys only if the contribution is the learning-systems machinery (training-data collection, drift handling, inference latency budget); route to a DB venue if it is optimizer quality.
- An INT4 quantization method with strong accuracy and a fast custom kernel. If the
paper's spine is the accuracy-preservation insight, ML venues fit; it becomes
MLSys-shaped when the kernel/runtime co-design carries equal weight and the
evaluation leads with latency-quality tradeoffs on real serving stacks — the AWQ
pattern in
../../resources/exemplars/library.md. - A cluster scheduler for mixed training/inference jobs, evaluated in simulation only. Topic fits; evidence does not — simulation-only evaluation of a systems claim is a recognized weak pattern here. Either add a real-cluster deployment at modest scale or target a venue where simulation is the norm.
Anti-patterns that fail the fit test
- "We ported X to GPUs and it is faster" — engineering without an insight that travels.
- "Our new attention variant, with a latency table" — ML paper wearing a systems hat.
- "A survey of serving systems" — MLSys publishes measured contributions, not surveys.
- "Our company's stack, described" without benchmarks — even the industrial track demands measurement depth.
- "Faster than a two-year-old baseline" — in a field whose systems improve monthly, a stale comparison fails the fit test before the novelty question is even reached.
- "We propose a benchmark" with no run rules or quality targets — the venue that published MLPerf holds benchmark papers to benchmark-committee standards.
Output format
[Fit] strong MLSys / MLSys-possible / route elsewhere
[Co-design test] ML->system: yes/no ; system->ML: yes/no
[Track] research / industrial / workshop-first
[Best alternative venue] <venue + reason>
[Contribution compression] observed -> built -> measured -> cost
[Next action] <measurement, mechanism naming, track decision, or reroute>
Version History
- 9f86f09 Current 2026-07-19 17:00
