Mlsys topic selection

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.

1. Does an ML property (model structure, training dynamics, tolerance to approximation, request patterns of ML workloads) shape the system design?
2. 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>