ARXIV:2602.16603 · LLM INFRASTRUCTURE OPTIMIZATION · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: unverified proof status

FlowPrefill: Decoupling Preemption from Prefill Scheduling Granularity to Mitigate Head-of-Line Blocking in LLM Serving

arXiv

FlowPrefill optimizes LLM serving systems to improve throughput while maintaining service level objectives by innovatively managing request scheduling and execution interruption.

Blocked on Code›Score2.0Evidence unverified

Opportunity summary

Pain FlowPrefill optimizes LLM serving systems to improve throughput while maintaining service level objectives by innovatively managing request scheduling and execution interruption.

Evidence 0 refs | 0 sources | 17% coverage

Blocker Evidence unverified

Open Build Read PDF Signal Canvas Track

PROBLEM

FlowPrefill optimizes LLM serving systems to improve throughput while maintaining service level objectives by innovatively managing request scheduling and execution interruption. This exacerbates head-of-line (HoL) blocking during the compute-intensive prefill phase, where long-running requests…

METHOD

Full abstract

The growing demand for large language models (LLMs) requires serving systems to handle many concurrent requests with diverse service level objectives (SLOs). This exacerbates head-of-line (HoL) blocking during the compute-intensive prefill phase, where long-running requests monopolize resources and delay higher-priority ones, leading to widespread time-to-first-token (TTFT) SLO violations. While chunked prefill enables interruptibility, it introduces an inherent trade-off between responsiveness and throughput: reducing chunk size improves response latency but degrades computational efficiency, whereas increasing chunk size maximizes throughput but exacerbates blocking. This necessitates an adaptive preemption mechanism. However, dynamically balancing execution granularity against scheduling overheads remains a key challenge. In this paper, we propose FlowPrefill, a TTFT-goodput-optimized serving system that resolves this conflict by decoupling preemption granularity from scheduling frequency. To achieve adaptive prefill scheduling, FlowPrefill introduces two key innovations: 1) Operator-Level Preemption, which leverages operator boundaries to enable fine-grained execution interruption without the efficiency loss associated with fixed small chunking; and 2) Event-Driven Scheduling, which triggers scheduling decisions only upon request arrival or completion events, thereby supporting efficient preemption responsiveness while minimizing control-plane overhead. Evaluation on real-world production traces shows that FlowPrefill improves maximum goodput by up to 5.6$\times$ compared to state-of-the-art systems while satisfying heterogeneous SLOs.

RESULT

ScienceToStartup currently rates this 2.0/10 on the public viability pass. While chunked prefill enables interruptibility, it introduces an inherent trade-off between responsiveness and throughput: reducing chunk size improves response latency but degrades computational efficiency,…

WHY NOW

LLM Infrastructure Optimization moved forward this cycle; last verified April 2026. Public score 2.0/10.

Continue into Read for claims, analysis, references, and neighboring papers.

Opportunity summary

Score2.0

PainFlowPrefill optimizes LLM serving systems to improve throughput while maintaining service level objectives by innovatively managing request scheduling and execution interruption.

Evidence0 refs | 0 sources | 17% coverage

Blockermissing authors

Analysis summary

FlowPrefill optimizes LLM serving systems to improve throughput while maintaining service level objectives by innovatively managing request scheduling and execution interruption.

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: unverified proof status

ARXIV:2602.16603 · LLM INFRASTRUCTURE OPTIMIZATION · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: unverified proof status

FlowPrefill: Decoupling Preemption from Prefill Scheduling Granularity to Mitigate Head-of-Line Blocking in LLM Serving

arXiv

FlowPrefill optimizes LLM serving systems to improve throughput while maintaining service level objectives by innovatively managing request scheduling and execution interruption.

Blocked on Code›Score2.0Evidence unverified

Opportunity summary

Pain FlowPrefill optimizes LLM serving systems to improve throughput while maintaining service level objectives by innovatively managing request scheduling and execution interruption.

Evidence 0 refs | 0 sources | 17% coverage

Blocker Evidence unverified

Open Build Read PDF Signal Canvas Track

PROBLEM

METHOD

Full abstract

RESULT

WHY NOW

LLM Infrastructure Optimization moved forward this cycle; last verified April 2026. Public score 2.0/10.

Continue into Read for claims, analysis, references, and neighboring papers.

Opportunity summary

Score2.0

PainFlowPrefill optimizes LLM serving systems to improve throughput while maintaining service level objectives by innovatively managing request scheduling and execution interruption.

Evidence0 refs | 0 sources | 17% coverage

Blockermissing authors

Analysis summary

FlowPrefill optimizes LLM serving systems to improve throughput while maintaining service level objectives by innovatively managing request scheduling and execution interruption.

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: unverified proof status

Paper Pack

10.48550/arXiv.2602.16603

FlowPrefill: Decoupling Preemption from Prefill Scheduling Granularity to Mitigate Head-of-Line Blocking in LLM Serving

FlowPrefill optimizes LLM serving systems to improve throughput while maintaining service level objectives by innovatively managing request scheduling and execution interruption.

Abstract

Source availability

PDF linked

The paper record includes a public PDF URL.

Extraction status

Derived fallback

Read summaries are estimated from adjacent metadata, not verified extraction rows.

Proof status

unverified

0 refs; 0 sources; 17% coverage.

What was readable

linkedon filenot materializedderived fallback34 indexednot indexed

Derived fallback: Estimated from adjacent evidence; not verified from source.

Viability

2.0

Time to MVP

MVP estimate missing

Commercial

No commercial flags on file

Export

Preparing verified analysis

lens / founder

PROBLEM

METHOD

RESULT

WHY NOW

LLM Infrastructure Optimization moved forward this cycle; last verified April 2026. Public score 2.0/10.

Claim map

Abstract-backed public claims while anchored extraction refreshes.

Strong 0Mixed 0Weak 4

Evidencepartial
FlowPrefill optimizes LLM serving systems to improve throughput while maintaining service level objectives by innovatively managing request scheduling and execution interruption. This exacerbates head-of-line (HoL) blocking during the compute-intensive prefill phase, where long-running requests monopolize resources and delay higher-priority ones, leading to widespread time-to-first-token (TTFT) SLO violations.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
The growing demand for large language models (LLMs) requires serving systems to handle many concurrent requests with diverse service level objectives (SLOs). This exacerbates head-of-line (HoL) blocking during the compute-intensive prefill phase, where long-running requests monopolize resources and delay higher-priority ones, leading to widespread time-to-first-token (TTFT) SLO violations.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
ScienceToStartup currently rates this 2.0/10 on the public viability pass. While chunked prefill enables interruptibility, it introduces an inherent trade-off between responsiveness and throughput: reducing chunk size improves response latency but degrades computational efficiency, whereas increasing chunk size maximizes throughput but exacerbates blocking.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
LLM Infrastructure Optimization moved forward this cycle; last verified April 2026. Public score 2.0/10.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial

Constellation map

Paper-native neighborhood for concepts, methods, materials, markets, and competitors. Missing lanes stay labeled instead of disappearing behind commercialization gates.

Open full Signal Canvas

Concepts

not indexed

Methods

Materials

PDF linked

Markets

LLM Infrastructure Optimization

Competitors

not indexed

Competitive landscape

FlowPrefill optimizes LLM serving systems to improve throughput while maintaining service level objectives by innovatively managing request scheduling and execution interruption.

Segment

LLM Infrastructure Optimization

Adoption evidence

No public code link in the paper record yet

Commercial read

2.0/10 public viability

Direct

not classified

Adjacent

not classified

Substitute

not classified

Unknown

not classified

Buzz

No indexed public discussion is attached to 2602.16603 yet. That is a visibility signal, not a blank module: the monitor is watching the public channels below.

Hacker News

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Bluesky

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PDF

Preview the source document here, or use the hero PDF action for a new tab.

References(34)

Laser: Unlocking Layer-Level Scheduling for Efficient Multi-SLO LLM Serving

2026Jianxiong Liao, Quanxing Dong et al.

From Tokens to Layers: Redefining Stall-Free Scheduling for LLM Serving with Layered Prefill

2025Gunjun Lee, Jiwon Kim et al.

WindServe: Efficient Phase-Disaggregated LLM Serving with Stream-based Dynamic Scheduling

2025Jingqi Feng, Yukai Huang et al.

KVCache Cache in the Wild: Characterizing and Optimizing KVCache Cache at a Large Cloud Provider

2025Jiahao Wang, Jinbo Han et al.

Qwen3 Technical Report

2025An Yang, Anfeng Li et al.

semi-PD: Towards Efficient LLM Serving via Phase-Wise Disaggregated Computation and Unified Storage

2025Ke Hong, Lufang Chen et al.

Past-Future Scheduler for LLM Serving under SLA Guarantees

2025Ruihao Gong, Shihao Bai et al.

Multiplexing Dynamic Deep Learning Workloads with SLO-awareness in GPU Clusters

2025Wenyan Chen, Chengzhi Lu et al.

GPREEMPT: GPU Preemptive Scheduling Made General and Efficient

2025Ruwen Fan, Tingxu Ren et al.

Efficiently Serving Large Multimodal Models Using EPD Disaggregation

2024Gursimran Singh, Xinglu Wang et al.

Don't Stop Me Now: Embedding Based Scheduling for LLMs

2024Rana Shahout, Eran Malach et al.

Qwen2.5-Coder Technical Report

2024Binyuan Hui, Jian Yang et al.

Efficient LLM Scheduling by Learning to Rank

2024Yichao Fu, Siqi Zhu et al.

The Llama 3 Herd of Models

2024Abhimanyu Dubey, Abhinav Jauhri et al.

Mooncake: A KVCache-centric Disaggregated Architecture for LLM Serving

2024Ruoyu Qin, Zheming Li et al.

Queue Management for SLO-Oriented Large Language Model Serving

2024Archit Patke, Dhemath Reddy et al.

Taming Throughput-Latency Tradeoff in LLM Inference with Sarathi-Serve

2024Amey Agrawal, Nitin Kedia et al.

Inference without Interference: Disaggregate LLM Inference for Mixed Downstream Workloads

2024Cunchen Hu, HeYang Huang et al.

DistServe: Disaggregating Prefill and Decoding for Goodput-optimized Large Language Model Serving

2024Yinmin Zhong, Shengyu Liu et al.

Splitwise: Efficient Generative LLM Inference Using Phase Splitting

2023Pratyush Patel, Esha Choukse et al.

Showing 20 of 34 references

CITED BY

No citing papers are indexed in the public S2S graph yet. This is an explicit zero-signal state, not a hidden lookup.

Foundation

none indexed

Extension

none indexed

Commercially relevant

Higher ViabilityPreFT: Prefill-only finetuning for efficient inference

6.0

Higher ViabilityFlashPrefill: Instantaneous Pattern Discovery and Thresholding for Ultra-Fast Long-Context Prefilling

8.0

Higher ViabilityEWSJF: An Adaptive Scheduler with Hybrid Partitioning for Mixed-Workload LLM Inference

6.0

Higher ViabilityPrompt Control-Flow Integrity: A Priority-Aware Runtime Defense Against Prompt Injection in LLM Systems

7.0

Higher ViabilityRegulating Branch Parallelism in LLM Serving

6.0

Higher ViabilityPOP: Prefill-Only Pruning for Efficient Large Model Inference

8.0

Higher ViabilitySuperInfer: SLO-Aware Rotary Scheduling and Memory Management for LLM Inference on Superchips

3.0

Higher ViabilityFrontier: Towards Comprehensive and Accurate LLM Inference Simulation

3.0

Higher ViabilityScheduling LLM Inference with Uncertainty-Aware Output Length Predictions

4.0

Higher ViabilityRethinking LLMOps for Fraud and AML: Building a Compliance-Grade LLM Serving Stack

9.0

Conflicting

none indexed

Owned Distribution

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Agent drawer

5 surfaces preserved for agents. Humans can ignore.

Developer contracts, payload previews, evidence maps, and run controls stay here instead of the Read, Build, and Track workspace.

Run context

Paper: 2602.16603
Route: /paper/flowprefill-decoupling-preemption-from-prefill-scheduling-granularity-to-mitigate-head-of-line-blocking-in-llm-serving
Active tab: read
Artifact: flowprefill-decoupling-preemption-from-prefill-scheduling-granularity-to-mitigate-head-of-line-blocking-in-llm-serving

Available agents

Read extractor
Build planner
Track monitor
Competitive mapper
Related-paper scout

API/MCP endpoints

REST paper pack API/api/v1/paper/flowprefill-decoupling-preemption-from-prefill-scheduling-granularity-to-mitigate-head-of-line-blocking-in-llm-serving/paper-pack
REST build passport API/api/v1/paper/flowprefill-decoupling-preemption-from-prefill-scheduling-granularity-to-mitigate-head-of-line-blocking-in-llm-serving/build-passport
REST OpenAPI/api/openapi.json
MCP descriptor/api/mcp
MCP resourcesciencetostartup://surfaces/paper-workspace

Tool contracts

paper_packbuild_passportopportunity_kernelforesightsource_proofevidence_state

Payload preview

Inspect payload

{
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}

Schema validation

paper-r2 contract: present
JSON-LD twin: SSR emitted
OpenAPI path parity: /api/openapi.json
MCP resource parity: paper-workspace

Job trace

queued: drawer opened by user action
running: inspect or copy payload
succeeded: payload available in SSR
failed: route errors appear in evidence cards

Evidence map

sources used: page freshness, source proof anchors, JSON-LD
missing sources: exposed by PaperPack and EvidenceState chips
derived fallbacks: marked unverified before handoff

Page Freshness

Canonical route, proof status, last verified, refs, sources, and coverage.

Page Freshness

Paper proof surface

Canonical route: /paper/flowprefill-decoupling-preemption-from-prefill-scheduling-granularity-to-mitigate-head-of-line-blocking-in-llm-serving

stale

Proof freshness: stale
Proof status: unverified
Display score: 2/10
Last proof check: 2026-04-02
Score updated: 2026-04-02
Score fresh until: 2026-05-02
References: 0
Source count: 0
Coverage: 17%

This page is showing the last landed evidence receipt and score bundle because the latest proof data is outside the freshness window.

OpenAlex: pending — this preprint is not yet indexed by OpenAlex.

Agent Handoff

Endpoint list, payload shape, route context, and copyable handoff data.

Agent Handoff

FlowPrefill: Decoupling Preemption from Prefill Scheduling Granularity to Mitigate Head-of-Line Blocking in LLM Serving

Canonical ID flowprefill-decoupling-preemption-from-prefill-scheduling-granularity-to-mitigate-head-of-line-blocking-in-llm-serving | Route /paper/flowprefill-decoupling-preemption-from-prefill-scheduling-granularity-to-mitigate-head-of-line-blocking-in-llm-serving

REST example

curl https://sciencetostartup.com/api/v1/agent-handoff/paper/flowprefill-decoupling-preemption-from-prefill-scheduling-granularity-to-mitigate-head-of-line-blocking-in-llm-serving

MCP example

{
  "tool": "get_paper",
  "arguments": {
    "arxiv_id": "2602.16603"
  }
}

source_context

{
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  "mode": "paper",
  "query": "FlowPrefill: Decoupling Preemption from Prefill Scheduling Granularity to Mitigate Head-of-Line Blocking in LLM Serving",
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  "topic_slug": null,
  "benchmark_ref": null,
  "dataset_ref": null
}

Buildability Receipt

Verdict, compute envelope, blockers, signature state, and receipt links.

Paper proof page receipt window

Not build-ready: FlowPrefill: Decoupling Preemption from Prefill Scheduling Granularity to Mitigate Head-of-Line Blocking in LLM Serving

/buildability/flowprefill-decoupling-preemption-from-prefill-scheduling-granularity-to-mitigate-head-of-line-blocking-in-llm-serving

Ignoreblocked

Subject: FlowPrefill: Decoupling Preemption from Prefill Scheduling Granularity to Mitigate Head-of-Line Blocking in LLM Serving

Verdict

Ignore

Verdict is Ignore because current viability and proof state do not clear the buildability gate.

Time to first demo

Insufficient data

No first-demo timestamp, owner estimate, or elapsed demo receipt is attached to this surface.

Compute envelope

Structured compute envelope

Insufficient data

No data, compute, hardware, memory, latency, dependency, or serving requirement receipt is attached.

Evidence ids

Receipt path

/buildability/flowprefill-decoupling-preemption-from-prefill-scheduling-granularity-to-mitigate-head-of-line-blocking-in-llm-serving

Paper ref

flowprefill-decoupling-preemption-from-prefill-scheduling-granularity-to-mitigate-head-of-line-blocking-in-llm-serving

arXiv id

2602.16603

Freshness

Generated at

2026-04-02T02:30:40.136Z

Evidence freshness

stale

Last verification

2026-04-02T02:30:40.136Z

Sources

References

Coverage

17%

Hash state

Lineage hash

86df588724c71e22cb8b28bb43e5a63b7a48f86aabe60f4233a1779197059cdc

Canonical opportunity-kernel lineage hash.

Signature state

External signature

unsigned_external

No founder, registry, pilot, or production-adoption signature is attached to this receipt.

Verification

not_verified

Verification is blocked until an external signature is provided.

Blockers

Missing: repo_url
Missing: references
Missing: proof_status
Missing: distribution_readiness_scores
Missing: paper_extraction_scorecards
Unknown: distribution readiness has not been computed yet
Unknown: proof verification has not been recorded yet

Verification pending / evidence receipt incomplete

repo_url

references

Missing proof, requirement, signature, approval, adoption, or telemetry fields are blockers and must not be inferred.

Open receipt API receipt Build Loop Signal Canvas Proof divergence Divergence API Brier outcomes API

Source Proof anchors

Visual citations from the paper document graph.

JSON-LD twin

The application/ld+json payload rendered for agents.

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FlowPrefill: Decoupling Preemption from Prefill Scheduling Granularity to Mitigate Head-of-Line Blocking in LLM Serving

FlowPrefill: Decoupling Preemption from Prefill Scheduling Granularity to Mitigate Head-of-Line Blocking in LLM Serving

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