arxiv: 2605.07238 · v1 · submitted 2026-05-08 · 💻 cs.DC

Recognition: no theorem link

FATE: Future-State-Aware Scheduling for Heterogeneous LLM Workflows

Zirui Huang , Yi-Xiang Hu , Feng Wu , Xiangyang Li

Authors on Pith no claims yet

Pith reviewed 2026-05-11 01:18 UTC · model grok-4.3

classification 💻 cs.DC

keywords heterogeneous LLM workflowsDAG schedulingfuture-state awarenessmakespan optimizationprefix reusemulti-stage inferencefrontier planningCP-SAT scheduler

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The pith

FATE schedules LLM workflows by repeatedly planning the ready frontier while scoring each assignment on the downstream states it creates.

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

LLM applications increasingly run as heterogeneous multi-stage workflows whose DAG structure means every placement decision shapes model residency, output locality, prefix reuse, and device reachability for later stages. Existing schedulers optimize only the immediate ready queue or isolated reuse signals and thereby fragment useful state. FATE instead solves a CP-SAT problem over the current ready frontier, scores candidates with a horizon-aware function that adds state-conditional costs, and executes bounded multi-device shards. On real-DAG and controlled prefix-reuse benchmarks the method produces lower normalized makespan and P95 latency than round-robin, classical DAG schedulers, and adapted workflow policies. The measured gains arise from jointly preserving several future-state dimensions rather than chasing any single reuse signal.

Core claim

FATE repeatedly solves a CP-SAT-backed planner over the current ready frontier and scores each candidate assignment by both its immediate execution cost and the downstream state (model residency, parent-output locality, prefix reuse, device reachability) that the assignment induces; this produces normalized makespan of 0.675 and normalized P95 latency of 0.677 on the real-DAG benchmark.

What carries the argument

horizon-aware candidate scoring inside a CP-SAT frontier planner that adds state-conditional costs for downstream execution state

If this is right

FATE reduces normalized makespan by 32.5 percent and P95 latency by 32.3 percent relative to round-robin on the real-DAG benchmark.
FATE reduces the same metrics by 8.9 percent and 8.8 percent relative to the strongest non-FATE baseline.
The performance advantage comes from jointly preserving multiple dimensions of future execution state rather than prefix reuse alone.
The same frontier-plus-horizon method works on both real production DAGs and controlled prefix-reuse scenarios.

Where Pith is reading between the lines

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

Production serving systems could incorporate the state-conditional cost estimator as a lightweight module inside existing schedulers without adopting the full planner.
The same scoring approach could be tested on workflows that include dynamic conditional branches or data-dependent stage counts not present in the current static DAG benchmarks.
Extending the state model to capture memory-bandwidth or interconnect contention would be a direct next measurement on large-model clusters.

Load-bearing premise

That planning only over the current ready frontier with horizon-aware scoring captures enough future-state benefit to justify the approach without requiring a full-DAG optimization that would be too slow, and that the chosen real-DAG and prefix-reuse benchmarks represent typical production heterogeneous LLM workloads.

What would settle it

Running a full-DAG solver with a reasonable time limit on the same benchmarks and observing whether it matches or exceeds FATE's makespan and latency would show whether frontier-only planning loses critical opportunities.

Figures

Figures reproduced from arXiv: 2605.07238 by Feng Wu, Xiangyang Li, Yi-Xiang Hu, Zirui Huang.

**Figure 1.** Figure 1: Overview of FATE. Given a heterogeneous workflow DAG and the current execution state, FATE performs horizon-aware planning, scores feasible stage-device assignments, executes bounded multi-device shard placements, and updates execution state as execution unfolds. Unlike myopic queue-only scheduling, FATE optimizes both immediate cost and future-state quality. (v) ϕ(v) denotes stage-local features, includin… view at source ↗

**Figure 2.** Figure 2: ECDF of per-workflow normalized makespan on the workflow-DAG benchmark. The [PITH_FULL_IMAGE:figures/full_fig_p007_2.png] view at source ↗

read the original abstract

Large language model (LLM) applications are increasingly executed as heterogeneous multi-stage workflows rather than isolated inference calls. In these workflow directed acyclic graphs (DAGs), scheduling decisions affect not only the currently ready stage, but also the execution state inherited by downstream stages, including model residency, parent-output locality, prefix reuse, and future device reachability. Existing serving and DAG-scheduling policies mainly optimize immediate queue state, placement cost, or reuse signals in isolation, which can fragment useful state and increase end-to-end latency. We present FATE, a future-state-aware scheduler for heterogeneous LLM workflows. FATE combines a CP-SAT-backed frontier planner, horizon-aware candidate scoring, bounded multi-device shard execution, and state-conditional cost estimation. Rather than solving a monolithic full-DAG problem, FATE repeatedly plans over the current ready frontier and scores assignments by both immediate cost and the downstream state they induce. Across real-DAG and controlled prefix-reuse benchmarks, FATE outperforms practical heuristics, classical DAG scheduling, and proxy adaptations of recent workflow-serving policies. On the real-DAG benchmark, it achieves normalized makespan and normalized P95 latency of 0.675 and 0.677, reducing them by 32.5% and 32.3% over RoundRobin and by 8.9% and 8.8% over the strongest non-FATE baseline. Mechanism analysis and ablations show that these gains arise from jointly preserving multiple dimensions of future execution state rather than prefix reuse alone. These results indicate that future-state preservation should be treated as a first-class scheduling objective for heterogeneous LLM workflow serving.

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

FATE gets concrete gains on LLM workflow DAGs by scoring frontier assignments for downstream state like residency and prefix reuse, but the evaluation leaves the robustness of those gains open.

read the letter

The main thing to know is that FATE plans only the current ready frontier with CP-SAT and scores candidates by immediate cost plus the state they leave for later stages, including model placement, output locality, and device reachability. On their real-DAG benchmark this produces normalized makespan and P95 latency of 0.675 and 0.677, which is 8.9% and 8.8% better than the strongest non-FATE baseline they tested and much better than round-robin. The ablations tie the improvement to keeping multiple state dimensions intact rather than optimizing any one in isolation. That combination of solver-based frontier planning and multi-dimensional horizon scoring is the piece that is not already in the policies they contrast against. The paper does a clear job explaining why isolated reuse or placement signals fragment state in heterogeneous LLM workflows and why that matters for end-to-end latency. The mechanism section is useful because it shows the joint preservation actually moves the numbers. The soft spots are straightforward. The reported gains are given as single normalized values with no error bars, no statistical tests, and no full protocol for how the baselines were reimplemented, so it is hard to judge sensitivity to workload details or random seeds. The frontier-only design also leaves open whether deeper chains or higher fan-out DAGs would expose irreversible early choices that a global planner would avoid; their benchmarks apparently did not trigger that, but the assumption is load-bearing. This paper is for systems researchers and engineers who build or tune schedulers for multi-stage LLM serving. Anyone working on production inference pipelines would get practical value from the approach and the comparison. It deserves a serious referee because the claims are specific, the method is described at a level that can be reproduced or extended, and the evaluation, while incomplete, is concrete enough to support a useful review discussion.

Referee Report

3 major / 2 minor

Summary. The manuscript introduces FATE, a future-state-aware scheduler for heterogeneous LLM workflows represented as DAGs. It combines a CP-SAT planner restricted to the current ready frontier, horizon-aware scoring that accounts for induced downstream state (model residency, prefix cache, device reachability), and bounded multi-device execution. On a real-DAG benchmark it reports normalized makespan and P95 latency of 0.675 and 0.677, corresponding to 32.5%/32.3% reductions versus RoundRobin and 8.9%/8.8% reductions versus the strongest non-FATE baseline; ablations attribute the gains to joint preservation of multiple state dimensions rather than prefix reuse alone.

Significance. If the empirical results hold under fuller reporting, the work provides concrete evidence that treating future execution state as a first-class objective can improve end-to-end metrics in production-style LLM DAG serving. The combination of real-DAG workloads, controlled prefix-reuse benchmarks, and mechanism ablations offers a practical demonstration that frontier planning with horizon scoring can outperform both classical DAG heuristics and adapted workflow policies without solving a monolithic full-DAG problem.

major comments (3)

[Abstract, results paragraph] Abstract and results section: the central performance claim (normalized makespan 0.675, 8.9% improvement over strongest baseline) is presented without error bars, number of runs, statistical tests, or exact baseline implementations and hyper-parameters. Because these numbers are the primary evidence for the future-state-aware approach, the absence of reproducibility details is load-bearing.
[§3 (implied method)] Method description (frontier planner): the paper asserts that repeatedly solving CP-SAT over only the ready frontier plus horizon-aware scoring captures sufficient future benefit to outperform full-DAG baselines. No analysis, counter-example DAGs, or sensitivity study is supplied for deep chains or high-fan-out patterns where early placement decisions could create irreversible sub-optimality, directly testing the weakest assumption identified in the stress-test note.
[Ablations paragraph] Ablation study: the claim that gains arise from 'jointly preserving multiple dimensions of future execution state rather than prefix reuse alone' rests on mechanism analysis, yet the manuscript provides no quantitative breakdown of the individual ablation configurations or their effect sizes on the reported metrics.

minor comments (2)

[Abstract] Define 'normalized makespan' and 'normalized P95 latency' explicitly, including the reference baseline used for normalization.
[Experimental setup] Add a table or figure caption that lists the exact configurations of all compared baselines (RoundRobin, classical DAG schedulers, proxy adaptations of recent policies).

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive feedback. We address each major comment below and will incorporate revisions to strengthen reproducibility, analysis, and ablation details in the manuscript.

read point-by-point responses

Referee: [Abstract, results paragraph] Abstract and results section: the central performance claim (normalized makespan 0.675, 8.9% improvement over strongest baseline) is presented without error bars, number of runs, statistical tests, or exact baseline implementations and hyper-parameters. Because these numbers are the primary evidence for the future-state-aware approach, the absence of reproducibility details is load-bearing.

Authors: We agree that reproducibility details are essential for the central claims. In the revised manuscript we will report results aggregated over multiple independent runs with error bars, include statistical significance tests, and provide exact baseline implementations together with all hyperparameters used. revision: yes
Referee: [§3 (implied method)] Method description (frontier planner): the paper asserts that repeatedly solving CP-SAT over only the ready frontier plus horizon-aware scoring captures sufficient future benefit to outperform full-DAG baselines. No analysis, counter-example DAGs, or sensitivity study is supplied for deep chains or high-fan-out patterns where early placement decisions could create irreversible sub-optimality, directly testing the weakest assumption identified in the stress-test note.

Authors: The frontier planner with horizon scoring is intended to capture downstream effects without solving the full DAG. While the reported results on real DAGs support its practical value, we acknowledge the request for explicit validation on edge cases. We will add a sensitivity study and counter-example analysis for deep chains and high-fan-out patterns in the revision. revision: yes
Referee: [Ablations paragraph] Ablation study: the claim that gains arise from 'jointly preserving multiple dimensions of future execution state rather than prefix reuse alone' rests on mechanism analysis, yet the manuscript provides no quantitative breakdown of the individual ablation configurations or their effect sizes on the reported metrics.

Authors: We will revise the ablation section to include a quantitative breakdown of each ablation configuration, reporting the individual effect sizes on normalized makespan and P95 latency to clarify the contribution of each state dimension. revision: yes

Circularity Check

0 steps flagged

Empirical benchmark results with no derivation reducing to fitted inputs or self-citations

full rationale

The paper describes a scheduling system (CP-SAT frontier planner plus horizon-aware scoring) and reports direct empirical measurements of normalized makespan (0.675) and P95 latency (0.677) on separate real-DAG and prefix-reuse benchmarks. These outcomes are presented as observed performance deltas versus baselines, not as quantities derived from equations, fitted parameters, or self-cited uniqueness theorems. No load-bearing steps reduce the claimed gains to quantities defined by the inputs themselves; the central claims remain falsifiable experimental results.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

The central claim rests on standard domain assumptions about DAG workflow structure and the cost impact of state dimensions; no free parameters or invented entities are introduced.

axioms (2)

domain assumption LLM workflow stages can be modeled as a DAG whose execution cost depends on model residency, parent-output locality, prefix reuse, and device reachability
Invoked throughout the problem statement and method description as the basis for future-state scoring
domain assumption Planning over the current ready frontier with bounded lookahead is sufficient to capture most downstream state benefits
Explicitly contrasted with monolithic full-DAG solving in the method section

pith-pipeline@v0.9.0 · 5600 in / 1512 out tokens · 36166 ms · 2026-05-11T01:18:04.177349+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

15 extracted references · 15 canonical work pages

[1]

Efficient memory management for large language model serving with pagedattention

Woosuk Kwon, Zhuohan Li, Siyuan Zhuang, Ying Sheng, Lianmin Zheng, Cody Hao Yu, Joseph Gonzalez, Hao Zhang, and Ion Stoica. Efficient memory management for large language model serving with pagedattention. InProceedings of the 29th symposium on operating systems principles, pages 611–626, 2023

work page 2023
[2]

Sglang: Efficient execution of structured language model programs.Advances in neural information processing systems, 37: 62557–62583, 2024

Lianmin Zheng, Liangsheng Yin, Zhiqiang Xie, Chuyue Sun, Jeff Huang, Cody H Yu, Shiyi Cao, Christos Kozyrakis, Ion Stoica, Joseph E Gonzalez, et al. Sglang: Efficient execution of structured language model programs.Advances in neural information processing systems, 37: 62557–62583, 2024

work page 2024
[3]

Taming throughput-latency tradeoff in llm inference with sarathi-serve

Amey Agrawal, Nitin Kedia, Ashish Panwar, Jayashree Mohan, Nipun Kwatra, Bhargav Gula- vani, Alexey Tumanov, and Ramachandran Ramjee. Taming throughput-latency tradeoff in llm inference with sarathi-serve. In18th USENIX symposium on operating systems design and implementation (OSDI 24), pages 117–134, 2024

work page 2024
[4]

Distserve: Disaggregating prefill and decoding for goodput-optimized large language model serving

Yinmin Zhong, Shengyu Liu, Junda Chen, Jianbo Hu, Yibo Zhu, Xuanzhe Liu, Xin Jin, and Hao Zhang. Distserve: Disaggregating prefill and decoding for goodput-optimized large language model serving. In18th USENIX Symposium on Operating Systems Design and Implementation (OSDI 24), pages 193–210, 2024

work page 2024
[5]

Autellix: An efficient serving engine for llm agents as general programs.arXiv preprint arXiv:2502.13965, 2025

Michael Luo, Xiaoxiang Shi, Colin Cai, Tianjun Zhang, Justin Wong, Yichuan Wang, Chi Wang, Yanping Huang, Zhifeng Chen, Joseph E Gonzalez, et al. Autellix: An efficient serving engine for llm agents as general programs.arXiv preprint arXiv:2502.13965, 2025

work page arXiv 2025
[6]

Murakkab: Resource-efficient agentic workflow orchestration in cloud platforms.arXiv preprint arXiv:2508.18298, 2025

Gohar Irfan Chaudhry, Esha Choukse, Haoran Qiu, Íñigo Goiri, Rodrigo Fonseca, Adam Belay, and Ricardo Bianchini. Murakkab: Resource-efficient agentic workflow orchestration in cloud platforms.arXiv preprint arXiv:2508.18298, 2025

work page arXiv 2025
[7]

Yong Lin, Shange Tang, Bohan Lyu, Ziran Yang, Jui-Hui Chung, Haoyu Zhao, Lai Jiang, Yihan Geng, Jiawei Ge, Jingruo Sun, et al

Marco Laju, Donghyun Son, Saurabh Agarwal, Nitin Kedia, Myungjin Lee, Jayanth Srinivasa, and Aditya Akella. Nalar: An agent serving framework.arXiv preprint arXiv:2601.05109, 2026

work page arXiv 2026
[8]

Kvflow: Efficient prefix caching for accelerating llm-based multi-agent workflows

Zaifeng Pan, AJJKUMAR PATEL, Yipeng Shen, Zhengding Hu, Yue Guan, Wan-Lu Li, Lianhui Qin, Yida Wang, and Yufei Ding. Kvflow: Efficient prefix caching for accelerating llm-based multi-agent workflows. InThe Thirty-ninth Annual Conference on Neural Information Processing Systems

work page
[9]

Helix: Serving large language models over heterogeneous gpus and network via max-flow

Yixuan Mei, Yonghao Zhuang, Xupeng Miao, Juncheng Yang, Zhihao Jia, and Rashmi Vinayak. Helix: Serving large language models over heterogeneous gpus and network via max-flow. In Proceedings of the 30th ACM International Conference on Architectural Support for Program- ming Languages and Operating Systems, Volume 1, pages 586–602, 2025

work page 2025
[10]

Performance-effective and low-complexity task scheduling for heterogeneous computing.IEEE transactions on parallel and distributed systems, 13(3):260–274, 2002

Haluk Topcuoglu, Salim Hariri, and Min-You Wu. Performance-effective and low-complexity task scheduling for heterogeneous computing.IEEE transactions on parallel and distributed systems, 13(3):260–274, 2002

work page 2002
[11]

Wfcommons: A framework for enabling scientific workflow research and development.Future generation computer systems, 128:16–27, 2022

Tainã Coleman, Henri Casanova, Loïc Pottier, Manav Kaushik, Ewa Deelman, and Rafael Fer- reira da Silva. Wfcommons: A framework for enabling scientific workflow research and development.Future generation computer systems, 128:16–27, 2022

work page 2022
[12]

The cp-sat-lp solver (invited talk)

Laurent Perron, Frédéric Didier, and Steven Gay. The cp-sat-lp solver (invited talk). In29th International Conference on Principles and Practice of Constraint Programming (CP 2023), pages 3–1. Schloss Dagstuhl–Leibniz-Zentrum für Informatik, 2023

work page 2023
[13]

Batch query processing and optimization for agentic workflows.arXiv preprint arXiv:2509.02121, 2025

Junyi Shen, Noppanat Wadlom, and Yao Lu. Batch query processing and optimization for agentic workflows.arXiv preprint arXiv:2509.02121, 2025

work page arXiv 2025
[14]

Mooncake: A kvcache-centric disaggregated architecture for llm serving.ACM Transactions on Storage

Ruoyu Qin, Zheming Li, Weiran He, Jialei Cui, Heyi Tang, Feng Ren, Teng Ma, Shangming Cai, Yineng Zhang, Mingxing Zhang, et al. Mooncake: A kvcache-centric disaggregated architecture for llm serving.ACM Transactions on Storage. 10

work page
[15]

Residency,

Yushi Bai, Xin Lv, Jiajie Zhang, Hongchang Lyu, Jiankai Tang, Zhidian Huang, Zhengxiao Du, Xiao Liu, Aohan Zeng, Lei Hou, et al. Longbench: A bilingual, multitask benchmark for long context understanding. InProceedings of the 62nd annual meeting of the association for computational linguistics (volume 1: Long papers), pages 3119–3137, 2024. A Technical Ap...

work page 2024