ARXIV:2602.11812 · LLM OPTIMIZATION · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE

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

Predicting LLM Output Length via Entropy-Guided Representations

arXiv

Optimize LLM inference efficiency with a novel framework leveraging entropy-guided predictions.

Blocked on Code›Score5.0Evidence unverified

Opportunity summary

Pain Optimize LLM inference efficiency with a novel framework leveraging entropy-guided predictions.

Evidence 0 refs | 0 sources | 17% coverage

Blocker Evidence unverified

Open Build Read PDF Signal Canvas Track

PROBLEM

Optimize LLM inference efficiency with a novel framework leveraging entropy-guided predictions. Existing methods rely on auxiliary models for static length prediction, but they incur high overhead, generalize poorly, and fail in stochastic "one-to-many" sampling…

METHOD

Full abstract

The long-tailed distribution of sequence lengths in LLM serving and reinforcement learning (RL) sampling causes significant computational waste due to excessive padding in batched inference. Existing methods rely on auxiliary models for static length prediction, but they incur high overhead, generalize poorly, and fail in stochastic "one-to-many" sampling scenarios. We introduce a lightweight framework that reuses the main model's internal hidden states for efficient length prediction. Our framework features two core components: 1) Entropy-Guided Token Pooling (EGTP), which uses on-the-fly activations and token entropy for highly accurate static prediction with negligible cost, and 2) Progressive Length Prediction (PLP), which dynamically estimates the remaining length at each decoding step to handle stochastic generation. To validate our approach, we build and release ForeLen, a comprehensive benchmark with long-sequence, Chain-of-Thought, and RL data. On ForeLen, EGTP achieves state-of-the-art accuracy, reducing MAE by 29.16\% over the best baseline. Integrating our methods with a length-aware scheduler yields significant end-to-end throughput gains. Our work provides a new technical and evaluation baseline for efficient LLM inference.

RESULT

ScienceToStartup currently rates this 5.0/10 on the public viability pass. On ForeLen, EGTP achieves state-of-the-art accuracy, reducing MAE by 29.16\% over the best baseline.

WHY NOW

LLM Optimization moved forward this cycle; last verified April 2026. Public score 5.0/10.

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

Opportunity summary

Score5.0

PainOptimize LLM inference efficiency with a novel framework leveraging entropy-guided predictions.

Evidence0 refs | 0 sources | 17% coverage

Blockermissing authors

Analysis summary

Optimize LLM inference efficiency with a novel framework leveraging entropy-guided predictions.

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

ARXIV:2602.11812 · LLM OPTIMIZATION · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE

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

Predicting LLM Output Length via Entropy-Guided Representations

arXiv

Optimize LLM inference efficiency with a novel framework leveraging entropy-guided predictions.

Blocked on Code›Score5.0Evidence unverified

Opportunity summary

Pain Optimize LLM inference efficiency with a novel framework leveraging entropy-guided predictions.

Evidence 0 refs | 0 sources | 17% coverage

Blocker Evidence unverified

Open Build Read PDF Signal Canvas Track

PROBLEM

METHOD

Full abstract

RESULT

ScienceToStartup currently rates this 5.0/10 on the public viability pass. On ForeLen, EGTP achieves state-of-the-art accuracy, reducing MAE by 29.16\% over the best baseline.

WHY NOW

LLM Optimization moved forward this cycle; last verified April 2026. Public score 5.0/10.

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

Opportunity summary

Score5.0

PainOptimize LLM inference efficiency with a novel framework leveraging entropy-guided predictions.

Evidence0 refs | 0 sources | 17% coverage

Blockermissing authors

Analysis summary

Optimize LLM inference efficiency with a novel framework leveraging entropy-guided predictions.

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

Paper Pack

10.48550/arXiv.2602.11812

Predicting LLM Output Length via Entropy-Guided Representations

Optimize LLM inference efficiency with a novel framework leveraging entropy-guided predictions.

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 fallback50 indexednot indexed

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

Viability

5.0

Time to MVP

MVP estimate missing

Commercial

No commercial flags on file

Export

Preparing verified analysis

lens / founder

PROBLEM

METHOD

RESULT

ScienceToStartup currently rates this 5.0/10 on the public viability pass. On ForeLen, EGTP achieves state-of-the-art accuracy, reducing MAE by 29.16\% over the best baseline.

WHY NOW

LLM Optimization moved forward this cycle; last verified April 2026. Public score 5.0/10.

Claim map

Abstract-backed public claims while anchored extraction refreshes.

Strong 0Mixed 0Weak 4

Evidencepartial
Optimize LLM inference efficiency with a novel framework leveraging entropy-guided predictions. Existing methods rely on auxiliary models for static length prediction, but they incur high overhead, generalize poorly, and fail in stochastic "one-to-many" sampling scenarios.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
The long-tailed distribution of sequence lengths in LLM serving and reinforcement learning (RL) sampling causes significant computational waste due to excessive padding in batched inference. Existing methods rely on auxiliary models for static length prediction, but they incur high overhead, generalize poorly, and fail in stochastic "one-to-many" sampling scenarios.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
ScienceToStartup currently rates this 5.0/10 on the public viability pass. On ForeLen, EGTP achieves state-of-the-art accuracy, reducing MAE by 29.16\% over the best baseline.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
LLM Optimization moved forward this cycle; last verified April 2026. Public score 5.0/10.
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 Optimization

Competitors

not indexed

Competitive landscape

Optimize LLM inference efficiency with a novel framework leveraging entropy-guided predictions.

Segment

LLM Optimization

Adoption evidence

No public code link in the paper record yet

Commercial read

5.0/10 public viability

Direct

not classified

Adjacent

not classified

Substitute

not classified

Unknown

not classified

Buzz

No indexed public discussion is attached to 2602.11812 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

Not indexed yet

PDF

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

References(50)

MaaSO: SLO-aware Orchestration of Heterogeneous Model Instances for MaaS

2025Xuan Mo, Zhang yue et al.

Zen-Attention: A Compiler Framework for Dynamic Attention Folding on AMD NPUs

2025Aadesh Deshmukh, Venkata Yaswanth Raparti et al.

PersRM-R1: Enhance Personalized Reward Modeling with Reinforcement Learning

2025Mengdi Li, Guanqiao Chen et al.

Compressing Chain-of-Thought in LLMs via Step Entropy

2025Zeju Li, Jianyuan Zhong et al.

Survey of GenAI for Automotive Software Development: From Requirements to Executable Code

2025Nenad Petrovic, Vahid Zolfaghari et al.

Towards Agentic RAG with Deep Reasoning: A Survey of RAG-Reasoning Systems in LLMs

2025Yangning Li, Weizhi Zhang et al.

Infinite Sampling: Efficient and Stable Grouped RL Training for Large Language Models

2025Liangyu Wang, Huanyi Xie et al.

Overclocking LLM Reasoning: Monitoring and Controlling Thinking Path Lengths in LLMs

2025Roy Eisenstadt, Itamar Zimerman et al.

CODEMENV: Benchmarking Large Language Models on Code Migration

2025Keyuan Cheng, Xudong Shen et al.

COMPKE: Complex Question Answering under Knowledge Editing

2025Keyuan Cheng, Zijian Kan et al.

Curriculum-RLAIF: Curriculum Alignment with Reinforcement Learning from AI Feedback

2025Mengdi Li, Jiaye Lin et al.

Is Your LLM-Based Multi-Agent a Reliable Real-World Planner? Exploring Fraud Detection in Travel Planning

2025Junchi Yao, Jianhua Xu et al.

Taming the Titans: A Survey of Efficient LLM Inference Serving

2025Ranran Zhen, Juntao Li et al.

Efficient Inference for Large Reasoning Models: A Survey

2025Yue Liu, Jiaying Wu et al.

Towards User-level Private Reinforcement Learning with Human Feedback

2025Jiaming Zhang, Mingxi Lei et al.

DeepSeek-R1 incentivizes reasoning in LLMs through reinforcement learning

2025DeepSeek-AI, Daya Guo et al.

Agent Laboratory: Using LLM Agents as Research Assistants

2025Samuel Schmidgall, Yusheng Su et al.

When Will the Tokens End? Graph-Based Forecasting for LLMs Output Length

2025G. Piotrowski, Mateusz Bystronski et al.

Token-Budget-Aware LLM Reasoning

2024Tingxu Han, Zhenting Wang et al.

Locate-then-edit for Multi-hop Factual Recall under Knowledge Editing

2024Zhuoran Zhang, Yongxiang Li et al.

Showing 20 of 50 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

Prior WorkP-EAGLE: Parallel-Drafting EAGLE with Scalable Training

5.0

Extension

Builds On ThisScheduling LLM Inference with Uncertainty-Aware Output Length Predictions

4.0

Builds On ThisFrom Rigid to Dynamic: Entropy-Guided Adaptive Inference for Long-Context LLMs

0.0

Builds On ThisLEPO: \underline{L}atent R\underline{e}asoning \underline{P}olicy \underline{O}ptimization for Large Language~Models

3.0

Commercially relevant

Higher ViabilityPredict-then-Diffuse: Adaptive Response Length for Compute-Budgeted Inference in Diffusion LLMs

7.0

Higher ViabilityEfficient Training-Free Multi-Token Prediction via Embedding-Space Probing

7.0

Higher ViabilityLLMs Encode Their Failures: Predicting Success from Pre-Generation Activations

7.0

Higher ViabilityLEAD: Length-Efficient Adaptive and Dynamic Reasoning for Large Language Models

7.0

Higher ViabilityHow LoRA Remembers? A Parametric Memory Law for LLM Finetuning

8.0

Conflicting

Competing ApproachEliciting Numerical Predictive Distributions of LLMs Without Autoregression

2.0

Related Resources

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: 2602.11812
Route: /paper/predicting-llm-output-length-via-entropy-guided-representations
Active tab: read
Artifact: predicting-llm-output-length-via-entropy-guided-representations

Available agents

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

API/MCP endpoints

REST paper pack API/api/v1/paper/predicting-llm-output-length-via-entropy-guided-representations/paper-pack
REST build passport API/api/v1/paper/predicting-llm-output-length-via-entropy-guided-representations/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/predicting-llm-output-length-via-entropy-guided-representations

stale

Proof freshness: stale
Proof status: unverified
Display score: 5/10
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

Predicting LLM Output Length via Entropy-Guided Representations

Canonical ID predicting-llm-output-length-via-entropy-guided-representations | Route /paper/predicting-llm-output-length-via-entropy-guided-representations

REST example

curl https://sciencetostartup.com/api/v1/agent-handoff/paper/predicting-llm-output-length-via-entropy-guided-representations

MCP example

{
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  "arguments": {
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  }
}

source_context

{
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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

Watch and verify: Predicting LLM Output Length via Entropy-Guided Representations

/buildability/predicting-llm-output-length-via-entropy-guided-representations

Watchwatch

Subject: Predicting LLM Output Length via Entropy-Guided Representations

Verdict

Watch

Verdict is Watch because viability or proof quality is intermediate and should be re-evaluated before execution.

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/predicting-llm-output-length-via-entropy-guided-representations

Paper ref

predicting-llm-output-length-via-entropy-guided-representations

arXiv id

2602.11812

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

ff01f6e0977489a929a073701fd8d390cb9b44fa10c3c3a888641d05b0038bfd

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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Predicting LLM Output Length via Entropy-Guided Representations

Predicting LLM Output Length via Entropy-Guided Representations

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