arxiv: 2603.12118 · v2 · submitted 2026-03-12 · 💻 cs.LG · cs.DC

Recognition: 2 theorem links

· Lean Theorem

Cornserve: A Distributed Serving System for Any-to-Any Multimodal Models

Jae-Won Chung , Jeff J. Ma , Jisang Ahn , Yizhuo Liang , Akshay Jajoo , Myungjin Lee , Mosharaf Chowdhury

Authors on Pith no claims yet

Pith reviewed 2026-05-15 12:38 UTC · model grok-4.3

classification 💻 cs.LG cs.DC

keywords Any-to-Any multimodal modelsdistributed servingtask abstractionrecord-and-replay executioncomponent disaggregationmultimodal inferencethroughput optimizationKubernetes deployment

0 comments

The pith

Cornserve uses a flexible task abstraction and record-and-replay execution to serve any-to-any multimodal models with up to 3.81 times higher throughput.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

Any-to-any multimodal models accept and produce arbitrary combinations of text, images, video, and audio, so each request can follow a unique path through the model's computation graph with components that scale differently. Cornserve introduces a task abstraction that lets users express these graphs explicitly, which supports disaggregating components across machines and scaling them independently. The runtime records data dependencies once and replays them to dispatch work and forward tensors directly between producers and consumers. This design is implemented on Kubernetes and yields measurable gains in throughput and tail latency across several model types.

Core claim

Cornserve provides a flexible task abstraction for expressing Any-to-Any model computation graphs, enabling component disaggregation and independent scaling. The distributed runtime dispatches compute to the data plane via an efficient record-and-replay execution model that keeps track of data dependencies, and forwards tensor data between components directly from the producer to the consumer. Built on Kubernetes with approximately 23K new lines of Python, Cornserve supports diverse Any-to-Any models and delivers up to 3.81× higher throughput and 5.79× lower tail latency.

What carries the argument

Flexible task abstraction for Any-to-Any computation graphs paired with a record-and-replay execution model that tracks dependencies and forwards tensors directly between components.

If this is right

Diverse Any-to-Any models with different input and output modality combinations can be served from the same deployment.
Model components can be scaled independently according to their individual compute or memory demands.
Tensor data moves directly between producer and consumer components without central buffering.
The system runs on standard Kubernetes clusters using a modest amount of new Python code.
Measured throughput improves by up to 3.81 times and tail latency drops by up to 5.79 times relative to prior serving approaches.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

Production deployments of multimodal services could adopt a single serving layer instead of maintaining separate pipelines for each modality pair.
Researchers could test novel modality combinations without first writing custom orchestration logic for each new graph.
Cloud operators might schedule heterogeneous workloads more densely by treating each model component as an independently allocatable unit.
The same abstraction pattern could later apply to other heterogeneous computation graphs outside the multimodal domain.

Load-bearing premise

The flexible task abstraction can accurately capture the computation graphs of arbitrary Any-to-Any models and the record-and-replay model can manage dependencies efficiently without introducing significant overhead.

What would settle it

A new Any-to-Any model whose inter-component data flows cannot be expressed in the task abstraction or whose execution under record-and-replay shows no throughput gain or higher tail latency than a baseline serving system.

Figures

Figures reproduced from arXiv: 2603.12118 by Akshay Jajoo, Jae-Won Chung, Jeff J. Ma, Jisang Ahn, Mosharaf Chowdhury, Myungjin Lee, Yizhuo Liang.

**Figure 1.** Figure 1: Computation graphs of (a) InternVL 3 [23], a multimodal input model, and (b) Qwen Omni [18, 19], a multimodal input and output model. Different requests invoke different components and take different paths on the graph. 𝐸 stands for Encoder, 𝐿 for LLM, and 𝐺 for Generator. 𝐿th and 𝐿ta stand for thinker and talker LLMs, respectively. inference requests with different combinations of input and output modal… view at source ↗

**Figure 3.** Figure 3: Monolith vs. Cornserve comparisons for Qwen 2.5 Omni 7B [18] throughput and latency CDF, and Qwen 3 Omni 30B [19] throughput. × indicate GPU out-of-memory. Talker 0 Talker 2 Talker 4 Talker 1 Talker 3 Generator 0 Node 0 Thinker 0 TP 0 Thinker 0 TP 1 (a) 8 GPUs (1 node) Talker & Generator 2 Talker & Generator 6 Talker & Generator 3 Talker & Generator 7 Talker & Generator 0 Talker & Generator 4 Talker & Gene… view at source ↗

**Figure 4.** Figure 4: Cornserve deployment configurations for Qwen 3 Omni on 8 and 16 GPUs. Each box represents a GPU. Model fission allows each component to scale independently: the thinker (LLM) uses tensor parallelism while talkers and generators are replicated to balance throughput. where all components run within a single executor backed by Hugging Face Transformers [14], as vLLM [7] does not support either Qwen Omni mode… view at source ↗

read the original abstract

Any-to-Any models are an emerging class of multimodal models that accept combinations of multimodal data (e.g., text, image, video, audio) as input and generate them as output. Serving these models are challenging; different requests with different input and output modalities traverse different paths through the model computation graph, and each component of the model have different scaling characteristics. We present Cornserve, a distributed serving system for generic Any-to-Any models. Cornserve provides a flexible task abstraction for expressing Any-to-Any model computation graphs, enabling component disaggregation and independent scaling. The distributed runtime dispatches compute to the data plane via an efficient record-and-replay execution model that keeps track of data dependencies, and forwards tensor data between components directly from the producer to the consumer. Built on Kubernetes with approximately 23K new lines of Python, Cornserve supports diverse Any-to-Any models and delivers up to 3.81$\times$ higher throughput and 5.79$\times$ lower tail latency. Cornserve is open-source, and the demo video is available on YouTube.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

Cornserve introduces a practical task abstraction and record-and-replay model for serving Any-to-Any multimodal models with variable paths, delivering reported throughput and latency gains.

read the letter

Cornserve presents a distributed serving system for Any-to-Any multimodal models, which accept and generate combinations of text, images, video, and audio. The main contribution is a flexible task abstraction that captures the varying computation paths these models take, along with a record-and-replay execution model in the distributed runtime. This approach enables disaggregating model components so they can scale independently based on their needs. The system forwards tensor data directly between components, avoiding unnecessary central coordination. Built on Kubernetes, it comes with roughly 23,000 lines of new Python code and is open-sourced, which allows others to inspect and build on it. The performance claims are encouraging: up to 3.81 times higher throughput and 5.79 times lower tail latency compared to whatever baselines they used. If these hold across realistic workloads, it could influence how people deploy these emerging models. That said, the abstract is short on experimental details. It does not specify the exact baselines, the diversity of models or requests tested, or any overhead measurements for the abstraction itself. These are important for assessing whether the gains are robust or specific to their setup. This paper targets engineers and researchers focused on ML inference serving, particularly those working with multimodal systems that do not follow fixed computation paths. Someone designing a new serving platform or optimizing for dynamic graphs would find the design choices relevant. I recommend sending it for peer review. The idea is practical and the implementation is non-trivial, even if the evaluation section needs more depth to fully convince.

Referee Report

2 major / 2 minor

Summary. The paper presents Cornserve, a distributed serving system for Any-to-Any multimodal models that accept and generate arbitrary combinations of text, image, video, and audio. It introduces a flexible task abstraction to express variable computation graphs, a record-and-replay runtime that tracks data dependencies while disaggregating components for independent scaling, and direct tensor forwarding between producers and consumers. Implemented on Kubernetes with ~23K lines of Python, the system claims support for diverse models and reports up to 3.81× higher throughput and 5.79× lower tail latency.

Significance. If the empirical results hold under rigorous evaluation, Cornserve would address a timely engineering challenge in serving emerging multimodal models with heterogeneous scaling needs. The open-source release and generality of the task abstraction could enable broader adoption and further research on disaggregated serving for non-uniform modality paths.

major comments (2)

[§5] §5 (Evaluation): The reported 3.81× throughput and 5.79× tail-latency gains are presented without explicit baselines, workload definitions (e.g., modality mix ratios), hardware configuration, or statistical significance across runs; these omissions make it impossible to assess whether the gains are load-bearing for the central claim of practical superiority.
[§3.2] §3.2 (Record-and-replay runtime): The claim that the replay mechanism tracks dependencies with low overhead is central to the disaggregation argument, yet no micro-benchmark isolates the replay cost versus a standard scheduler or quantifies tensor-forwarding latency under varying graph depths.

minor comments (2)

The abstract states “approximately 23K new lines of Python”; clarify whether this counts only added code or total system size, and provide a breakdown by component.
Figure captions and table headers in the evaluation section should explicitly list the Any-to-Any models and modality combinations used for each data point.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback and the recommendation for minor revision. We address each major comment below and have revised the manuscript accordingly to strengthen the presentation of results and runtime analysis.

read point-by-point responses

Referee: [§5] §5 (Evaluation): The reported 3.81× throughput and 5.79× tail-latency gains are presented without explicit baselines, workload definitions (e.g., modality mix ratios), hardware configuration, or statistical significance across runs; these omissions make it impossible to assess whether the gains are load-bearing for the central claim of practical superiority.

Authors: We agree that the evaluation section requires additional explicit details to allow readers to fully assess the reported gains. In the revised manuscript we expand §5 with a new subsection that specifies the baseline systems (monolithic serving and prior disaggregated approaches), the exact workload definitions including modality mix ratios used in each experiment, the hardware configuration (GPU/CPU counts and interconnect details), and the statistical methodology (number of runs per data point and confidence intervals). These additions directly address the concern and make the 3.81× throughput and 5.79× tail-latency claims easier to interpret. revision: yes
Referee: [§3.2] §3.2 (Record-and-replay runtime): The claim that the replay mechanism tracks dependencies with low overhead is central to the disaggregation argument, yet no micro-benchmark isolates the replay cost versus a standard scheduler or quantifies tensor-forwarding latency under varying graph depths.

Authors: We acknowledge that isolating the runtime overhead is important for validating the disaggregation benefits. The revised §3.2 now contains dedicated micro-benchmarks that measure the record-and-replay overhead relative to a standard dependency-tracking scheduler. We also report tensor-forwarding latencies for computation graphs of varying depths (2–10 components). These results are presented in a new figure and accompanying text, confirming that the overhead remains modest and scales gracefully with graph depth. revision: yes

Circularity Check

0 steps flagged

No significant circularity

full rationale

This is an engineering systems paper describing the design and implementation of Cornserve, a distributed serving system. The core contributions are a flexible task abstraction for model computation graphs and a record-and-replay runtime for dependency tracking and tensor forwarding. These are presented as design decisions implemented in ~23K lines of Python on Kubernetes, with performance claims (up to 3.81× throughput, 5.79× lower tail latency) reported as direct empirical measurements from experiments on diverse Any-to-Any models. There are no equations, parameter fittings, predictions derived from inputs, uniqueness theorems, or self-citation chains that reduce any claim to a tautology or construction. The argument is self-contained: the abstraction is shown to express variable modality paths via implementation, and overhead is validated by runtime measurements rather than derived from unverified assumptions.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

No free parameters, axioms, or invented entities are described in the abstract; the contribution is an engineering system built on existing technologies like Kubernetes.

pith-pipeline@v0.9.0 · 5521 in / 1166 out tokens · 43860 ms · 2026-05-15T12:38:14.543902+00:00 · methodology

discussion (0)

Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

IndisputableMonolith/Cost/FunctionalEquation.lean washburn_uniqueness_aczel unclear

?

unclear
Relation between the paper passage and the cited Recognition theorem.

Cornserve supports diverse Any-to-Any models and delivers up to 3.81× higher throughput and 5.79× lower tail latency.

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supports: The theorem supports part of the paper's argument, but the paper may add assumptions or extra steps.
extends: The paper goes beyond the formal theorem; the theorem is a base layer rather than the whole result.
uses: The paper appears to rely on the theorem as machinery.
contradicts: The paper's claim conflicts with a theorem or certificate in the canon.
unclear: Pith found a possible connection, but the passage is too broad, indirect, or ambiguous to say the theorem truly supports the claim.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

GENSERVE: Efficient Co-Serving of Heterogeneous Diffusion Model Workloads
cs.DC 2026-04 unverdicted novelty 6.0

GENSERVE improves SLO attainment by up to 44% for co-serving heterogeneous T2I and T2V diffusion workloads via step-level preemption, elastic parallelism, and joint scheduling.

Reference graph

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