ARXIV:2602.15327 · MODEL EVALUATION · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE

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

Prescriptive Scaling Reveals the Evolution of Language Model Capabilities

arXiv

Develop a tool to predict language model performance from compute budgets using Proteus 2k dataset.

Blocked on Code›Score5.0Evidence unverified

Opportunity summary

Pain Develop a tool to predict language model performance from compute budgets using Proteus 2k dataset.

Evidence 0 refs | 0 sources | 17% coverage

Blocker Evidence unverified

Open Build Read PDF Signal Canvas Track

PROBLEM

Develop a tool to predict language model performance from compute budgets using Proteus 2k dataset. Using large scale observational evaluations with 5k observational and 2k newly sampled data on model performance, we estimate capability…

METHOD

Full abstract

For deploying foundation models, practitioners increasingly need prescriptive scaling laws: given a pre training compute budget, what downstream accuracy is attainable with contemporary post training practice, and how stable is that mapping as the field evolves? Using large scale observational evaluations with 5k observational and 2k newly sampled data on model performance, we estimate capability boundaries, high conditional quantiles of benchmark scores as a function of log pre training FLOPs, via smoothed quantile regression with a monotone, saturating sigmoid parameterization. We validate the temporal reliability by fitting on earlier model generations and evaluating on later releases. Across various tasks, the estimated boundaries are mostly stable, with the exception of math reasoning that exhibits a consistently advancing boundary over time. We then extend our approach to analyze task dependent saturation and to probe contamination related shifts on math reasoning tasks. Finally, we introduce an efficient algorithm that recovers near full data frontiers using roughly 20% of evaluation budget. Together, our work releases the Proteus 2k, the latest model performance evaluation dataset, and introduces a practical methodology for translating compute budgets into reliable performance expectations and for monitoring when capability boundaries shift across time.

RESULT

ScienceToStartup currently rates this 5.0/10 on the public viability pass. Together, our work releases the Proteus 2k, the latest model performance evaluation dataset, and introduces a practical methodology for translating compute budgets into reliable…

WHY NOW

Model Evaluation 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

PainDevelop a tool to predict language model performance from compute budgets using Proteus 2k dataset.

Evidence0 refs | 0 sources | 17% coverage

Blockermissing authors

Analysis summary

Develop a tool to predict language model performance from compute budgets using Proteus 2k dataset.

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

ARXIV:2602.15327 · MODEL EVALUATION · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE

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

Prescriptive Scaling Reveals the Evolution of Language Model Capabilities

arXiv

Develop a tool to predict language model performance from compute budgets using Proteus 2k dataset.

Blocked on Code›Score5.0Evidence unverified

Opportunity summary

Pain Develop a tool to predict language model performance from compute budgets using Proteus 2k dataset.

Evidence 0 refs | 0 sources | 17% coverage

Blocker Evidence unverified

Open Build Read PDF Signal Canvas Track

PROBLEM

METHOD

Full abstract

RESULT

WHY NOW

Model Evaluation 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

PainDevelop a tool to predict language model performance from compute budgets using Proteus 2k dataset.

Evidence0 refs | 0 sources | 17% coverage

Blockermissing authors

Analysis summary

Develop a tool to predict language model performance from compute budgets using Proteus 2k dataset.

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

Paper Pack

10.48550/arXiv.2602.15327

Prescriptive Scaling Reveals the Evolution of Language Model Capabilities

Develop a tool to predict language model performance from compute budgets using Proteus 2k dataset.

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 fallback51 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

WHY NOW

Model Evaluation 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
Develop a tool to predict language model performance from compute budgets using Proteus 2k dataset. Using large scale observational evaluations with 5k observational and 2k newly sampled data on model performance, we estimate capability boundaries, high conditional quantiles of benchmark scores as a function of log pre training FLOPs, via smoothed quantile regression with a monotone, saturating sigmoid parameterization.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
For deploying foundation models, practitioners increasingly need prescriptive scaling laws: given a pre training compute budget, what downstream accuracy is attainable with contemporary post training practice, and how stable is that mapping as the field evolves? Using large scale observational evaluations with 5k observational and 2k newly sampled data on model performance, we estimate capability boundaries, high conditional quantiles of benchmark scores as a function of log pre training FLOPs, via smoothed quantile regression with a monotone, saturating sigmoid parameterization.
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. Together, our work releases the Proteus 2k, the latest model performance evaluation dataset, and introduces a practical methodology for translating compute budgets into reliable performance expectations and for monitoring when capability boundaries shift across time.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
Model Evaluation 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

Model Evaluation

Competitors

not indexed

Competitive landscape

Develop a tool to predict language model performance from compute budgets using Proteus 2k dataset.

Segment

Model Evaluation

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 2602.15327 yet. That is a visibility signal, not a blank module: the monitor is watching the public channels below.

Hacker News

Not indexed yet

Bluesky

Not indexed yet

PDF

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

References(51)

Nemotron 3 Nano: Open, Efficient Mixture-of-Experts Hybrid Mamba-Transformer Model for Agentic Reasoning

2025Nvidia Aaron Blakeman, Aaron Grattafiori et al.

Nemotron-Cascade: Scaling Cascaded Reinforcement Learning for General-Purpose Reasoning Models

2025Boxin Wang, Chankyu Lee et al.

Revisiting the Scaling Properties of Downstream Metrics in Large Language Model Training

2025Jakub Krajewski, Amitis Shidani et al.

Artificial Hivemind: The Open-Ended Homogeneity of Language Models (and Beyond)

2025Liwei Jiang, Yuanjun Chai et al.

gpt-oss-120b&gpt-oss-20b Model Card

2025OpenAI Sandhini Agarwal, Lama Ahmad et al.

Language Models Improve When Pretraining Data Matches Target Tasks

2025David Mizrahi, Anders Boesen Lindbo Larsen et al.

Train-before-Test Harmonizes Language Model Rankings

2025Guanhua Zhang, Ricardo Dominguez-Olmedo et al.

Scaling Laws Are Unreliable for Downstream Tasks: A Reality Check

2025Nicholas Lourie, Michael Y Hu et al.

EvoLM: In Search of Lost Language Model Training Dynamics

2025Zhenting Qi, Fan Nie et al.

Discovering Hierarchical Latent Capabilities of Language Models via Causal Representation Learning

2025Jikai Jin, Vasilis Syrgkanis et al.

How Benchmark Prediction from Fewer Data Misses the Mark

2025Guanhua Zhang, Florian E. Dorner et al.

Knowledge or Reasoning? A Close Look at How LLMs Think Across Domains

2025Juncheng Wu, Sheng Liu et al.

Qwen3 Technical Report

2025An Yang, Anfeng Li et al.

Gemma 3 Technical Report

2025Gemma Team Aishwarya Kamath, Johan Ferret et al.

Unveiling Downstream Performance Scaling of LLMs: A Clustering-Based Perspective

2025Chengyin Xu, Kaiyuan Chen et al.

Humanity's Last Exam

2025Long Phan, Alice Gatti et al.

How Does Critical Batch Size Scale in Pre-training?

2024Hanlin Zhang, Depen Morwani et al.

Scaling Laws for Predicting Downstream Performance in LLMs

2024Yangyi Chen, Binxuan Huang et al.

Training on the Test Task Confounds Evaluation and Emergence

2024Ricardo Dominguez-Olmedo, Florian E. Dorner et al.

LiveBench: A Challenging, Contamination-Limited LLM Benchmark

2024Colin White, Samuel Dooley et al.

Showing 20 of 51 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

Builds On ThisOn Neural Scaling Laws for Weather Emulation through Continual Training

4.0

Builds On ThisTo Memorize or to Retrieve: Scaling Laws for RAG-Considerate Pretraining

4.0

Builds On ThisFast and Accurate Probing of In-Training LLMs' Downstream Performances

4.0

Builds On ThisEvaluation-driven Scaling for Scientific Discovery

4.0

Builds On ThisIsoCompute Playbook: Optimally Scaling Sampling Compute for LLM RL

2.0

Builds On ThisScaling Embeddings Outperforms Scaling Experts in Language Models

3.0

Commercially relevant

Higher ViabilityOnline Reasoning Calibration: Test-Time Training Enables Generalizable Conformal LLM Reasoning

8.0

Higher ViabilityScaling Behavior Cloning Improves Causal Reasoning: An Open Model for Real-Time Video Game Playing

7.0

Higher Viabilityq0: Primitives for Hyper-Epoch Pretraining

7.0

Higher ViabilitySeed2Scale: A Self-Evolving Data Engine for Embodied AI via Small to Large Model Synergy and Multimodal Evaluation

8.0

Conflicting

none indexed

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: 2602.15327
Route: /paper/prescriptive-scaling-reveals-the-evolution-of-language-model-capabilities
Active tab: read
Artifact: prescriptive-scaling-reveals-the-evolution-of-language-model-capabilities

Available agents

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

API/MCP endpoints

REST paper pack API/api/v1/paper/prescriptive-scaling-reveals-the-evolution-of-language-model-capabilities/paper-pack
REST build passport API/api/v1/paper/prescriptive-scaling-reveals-the-evolution-of-language-model-capabilities/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/prescriptive-scaling-reveals-the-evolution-of-language-model-capabilities

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

Prescriptive Scaling Reveals the Evolution of Language Model Capabilities

Canonical ID prescriptive-scaling-reveals-the-evolution-of-language-model-capabilities | Route /paper/prescriptive-scaling-reveals-the-evolution-of-language-model-capabilities

REST example

curl https://sciencetostartup.com/api/v1/agent-handoff/paper/prescriptive-scaling-reveals-the-evolution-of-language-model-capabilities

MCP example

{
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  }
}

source_context

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Buildability Receipt

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

Paper proof page receipt window

Watch and verify: Prescriptive Scaling Reveals the Evolution of Language Model Capabilities

/buildability/prescriptive-scaling-reveals-the-evolution-of-language-model-capabilities

Watchwatch

Subject: Prescriptive Scaling Reveals the Evolution of Language Model Capabilities

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/prescriptive-scaling-reveals-the-evolution-of-language-model-capabilities

Paper ref

prescriptive-scaling-reveals-the-evolution-of-language-model-capabilities

arXiv id

2602.15327

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

d318704380669ec503329885eff88ff593ead934670b624b5578a73d823fab5f

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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Prescriptive Scaling Reveals the Evolution of Language Model Capabilities

Prescriptive Scaling Reveals the Evolution of Language Model Capabilities

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