arxiv: 2604.11001 · v1 · submitted 2026-04-13 · 💻 cs.LG

Recognition: unknown

Flow-Controlled Scheduling for LLM Inference with Provable Stability Guarantees

Zhuolun Dong , Junyu Cao

Authors on Pith no claims yet

Pith reviewed 2026-05-10 15:48 UTC · model grok-4.3

classification 💻 cs.LG

keywords LLM inferenceflow controlschedulingstabilityKV cachethroughputlatency

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The pith

Flow-controlled scheduling stabilizes LLM inference by regulating the rate at which prompts enter the active set.

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

LLM inference systems risk instability because decode lengths are unknown ahead of time, so memory use for each request can grow without bound and overflow the KV cache. The paper introduces a flow-control framework that limits how fast new prompts join the active decoding pool, based on current memory state. It derives a necessary condition that any stable system must meet and proves that the proposed policy satisfies sufficient conditions to guarantee stability. If the claims hold, servers can sustain high request volumes without crashes or degraded performance, while experiments indicate gains in token throughput, request throughput, and both average and tail latency.

Core claim

By controlling the admission rate of prompts into the active set, the flow-control policy achieves provable stability for LLM inference. A necessary condition that every stable system must obey is established from memory and arrival considerations, and the algorithm is shown to meet sufficient conditions for stability. Compared with common practical strategies, the approach delivers higher token and request throughput, lower average and tail latency, and more stable KV cache utilization.

What carries the argument

Flow-control policy that sets the rate at which new prompts join the active decoding set according to observed memory state and arrival statistics.

If this is right

Any stable LLM inference system must satisfy the derived necessary condition relating memory capacity, arrival rates, and decode-length distributions.
The proposed algorithm meets the sufficient conditions and therefore guarantees stability whenever those conditions hold.
The method produces measurably higher token throughput and request throughput than the strategies currently used in practice.
Average and tail latency decrease while KV cache utilization stays bounded and predictable.

Where Pith is reading between the lines

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

The same admission-rate logic could be tested on other variable-length generative workloads such as image or video synthesis to see whether similar stability bounds appear.
Replacing exact memory knowledge with lightweight online estimators for arrival statistics would make the policy fully online; whether stability survives the estimation error remains open.
The necessary condition might serve as a quick diagnostic: a serving cluster that violates it can be expected to become unstable regardless of the scheduler chosen.

Load-bearing premise

The stability analysis assumes the flow-control policy can be implemented with accurate, real-time knowledge of current memory state and arrival statistics.

What would settle it

Deploy the policy on a live server where memory-state reports contain realistic measurement noise or delay, and check whether KV cache overflow or request drops still occur at loads the analysis claims are stable.

Figures

Figures reproduced from arXiv: 2604.11001 by Junyu Cao, Zhuolun Dong.

**Figure 2.** Figure 2: Distribution of prefill and decode lengths in the real-world dataset. [PITH_FULL_IMAGE:figures/full_fig_p010_2.png] view at source ↗

**Figure 3.** Figure 3: (Up) Performance metrics across scheduling algorithms under the low demand setting. [PITH_FULL_IMAGE:figures/full_fig_p010_3.png] view at source ↗

**Figure 4.** Figure 4: Performance of our scheduling algorithm against the two benchmarks. (Up) The perfor [PITH_FULL_IMAGE:figures/full_fig_p018_4.png] view at source ↗

read the original abstract

Large language models (LLMs) have been widely adopted due to their great performance across a wide range of applications. ChatGPT and Gemini now serve hundreds of millions of active users and handle billions of user requests per day, which puts optimizing LLM inference into the spotlight. A key challenge in LLM inference is that decode lengths are unknown. The memory usage for each request grows with generated tokens, which may lead to overflow and cause system instability. To address this concern, we propose a simple flow-control framework that controls the rate at which prompts join the active set. We derive a necessary condition that any stable system must satisfy and establish sufficient conditions under which our algorithm provably achieves stability. Experiments show that, compared to commonly used strategies in practice, our approach achieves higher token and request throughput, lower average and tail latency, and more stable KV cache utilization.

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

The paper maps flow control to LLM KV-cache scheduling with a necessary stability condition and sufficient conditions for their policy, plus reported gains in throughput and latency, but the guarantees look tied to perfect state information.

read the letter

The main thing to know is that this work takes the classic idea of flow control and applies it to admitting prompts in LLM inference so that KV-cache usage stays stable even when decode lengths are unknown. They state a necessary condition any stable system must meet and give sufficient conditions under which their rate-control algorithm achieves stability, then show experiments with higher token and request throughput, lower average and tail latency, and steadier cache use than common baselines.

Referee Report

2 major / 2 minor

Summary. The paper proposes a flow-control framework for LLM inference that regulates the rate at which prompts enter the active set to prevent KV-cache overflows arising from unknown decode lengths. It derives a necessary condition that any stable system must obey and sufficient conditions under which the proposed algorithm provably achieves stability, with experiments claiming higher token/request throughput, lower average/tail latency, and more stable KV-cache utilization versus common practical baselines.

Significance. If the stability analysis holds under implementable conditions, the work supplies a principled, provably stable alternative to heuristic scheduling in LLM serving systems. This addresses a load-bearing practical issue in high-volume inference and could improve reliability without sacrificing efficiency, provided the guarantees survive realistic estimation of memory state and arrivals.

major comments (2)

[stability analysis (sufficient conditions)] The sufficient conditions for provable stability (derived from first principles in the theoretical analysis) treat rate selection as using exact, instantaneous knowledge of current memory occupancy and full arrival/decode statistics. This assumption is load-bearing for the central claim of 'provable stability guarantees' but receives no treatment of online estimation error, measurement noise, or delayed feedback, leaving open whether the conditions extend to any realizable policy.
[experiments and evaluation] The necessary condition is presented as a general property any stable system must satisfy, yet the experimental evaluation does not test whether the reported throughput/latency gains persist when the flow-control rate must be inferred from partial observations rather than oracle knowledge; this weakens the link between the theorems and the claimed practical improvements.

minor comments (2)

[abstract] The abstract and high-level description provide no detail on how the admission rate is computed in practice (e.g., whether it uses running estimates of arrival rates or decode-length distributions).
[experiments] Baseline strategies are described only as 'commonly used strategies in practice' without explicit pseudocode or parameter settings, making it difficult to reproduce the reported gains.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive feedback, which helps clarify the scope of our stability results and their connection to practical implementation. We address each major comment below and outline the corresponding revisions.

read point-by-point responses

Referee: The sufficient conditions for provable stability (derived from first principles in the theoretical analysis) treat rate selection as using exact, instantaneous knowledge of current memory occupancy and full arrival/decode statistics. This assumption is load-bearing for the central claim of 'provable stability guarantees' but receives no treatment of online estimation error, measurement noise, or delayed feedback, leaving open whether the conditions extend to any realizable policy.

Authors: We agree that the sufficient conditions assume exact, instantaneous knowledge of memory occupancy and complete arrival/decode statistics. This modeling choice enables a clean derivation of the stability guarantees from first principles. The manuscript does not analyze estimation error, noise, or feedback delays. In the revised version we will add a dedicated subsection on practical estimation (using sliding-window averages for occupancy and empirical histograms for statistics) together with a robustness discussion that quantifies how bounded errors affect the sufficient conditions. This will make explicit the path from the idealized guarantees to implementable policies. revision: yes
Referee: The necessary condition is presented as a general property any stable system must satisfy, yet the experimental evaluation does not test whether the reported throughput/latency gains persist when the flow-control rate must be inferred from partial observations rather than oracle knowledge; this weakens the link between the theorems and the claimed practical improvements.

Authors: The necessary condition is derived as a general requirement that any stable system must obey, independent of knowledge assumptions. The reported experiments isolate the benefit of the flow-control policy under the model assumptions. To strengthen the link to practice, we will add an ablation study that computes the control rate from noisy or partial observations (simulating realistic memory monitoring and statistical estimation) and verify that the reported gains in token/request throughput, latency, and KV-cache stability remain intact. revision: yes

Circularity Check

0 steps flagged

No significant circularity; stability conditions derived from first principles

full rationale

The paper states it derives a necessary condition any stable system must satisfy and sufficient conditions for its flow-control algorithm to achieve stability. No equations or sections in the provided abstract or description reduce the claimed predictions or conditions to fitted inputs, self-definitions, or load-bearing self-citations. The derivation is presented as general and independent of the evaluation data or algorithm implementation details, making the central claims self-contained against external benchmarks rather than tautological.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on standard queueing and memory-growth assumptions plus the existence of a controllable admission rate; no new physical entities or heavily fitted parameters are introduced in the abstract.

axioms (1)

domain assumption Memory usage grows linearly with generated tokens and requests arrive with unknown decode lengths.
Core modeling premise stated in the problem setup.

pith-pipeline@v0.9.0 · 5439 in / 1107 out tokens · 28880 ms · 2026-05-10T15:48:08.969510+00:00 · methodology

discussion (0)

Reference graph

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