ARXIV:2602.24055 · AI EVALUATION FRAMEWORK · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE

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

CIRCLE: A Framework for Evaluating AI from a Real-World Lens

arXiv

CIRCLE offers a new framework to evaluate AI in real-world contexts, enhancing decision-makers' ability to gauge AI technologies' real-world impacts beyond theoretical benchmarks.

Blocked on Code›Score5.0Evidence unverified

Opportunity summary

Pain CIRCLE offers a new framework to evaluate AI in real-world contexts, enhancing decision-makers' ability to gauge AI technologies' real-world impacts beyond theoretical benchmarks.

Evidence 0 refs | 0 sources | 17% coverage

Blocker Evidence unverified

Open Build Read PDF Signal Canvas Track

PROBLEM

CIRCLE offers a new framework to evaluate AI in real-world contexts, enhancing decision-makers' ability to gauge AI technologies' real-world impacts beyond theoretical benchmarks. While existing frameworks like MLOps focus on system stability and benchmarks…

METHOD

Full abstract

This paper proposes CIRCLE, a six-stage, lifecycle-based framework to bridge the reality gap between model-centric performance metrics and AI's materialized outcomes in deployment. While existing frameworks like MLOps focus on system stability and benchmarks measure abstract capabilities, decision-makers outside the AI stack lack systematic evidence about the behavior of AI technologies under real-world user variability and constraints. CIRCLE operationalizes the Validation phase of TEVV (Test, Evaluation, Verification, and Validation) by formalizing the translation of stakeholder concerns outside the stack into measurable signals. Unlike participatory design, which often remains localized, or algorithmic audits, which are often retrospective, CIRCLE provides a structured, prospective protocol for linking context-sensitive qualitative insights to scalable quantitative metrics. By integrating methods such as field testing, red teaming, and longitudinal studies into a coordinated pipeline, CIRCLE produces systematic knowledge: evidence that is comparable across sites yet sensitive to local context. This can enable governance based on materialized downstream effects rather than theoretical capabilities.

RESULT

ScienceToStartup currently rates this 5.0/10 on the public viability pass. This can enable governance based on materialized downstream effects rather than theoretical capabilities.

WHY NOW

AI Evaluation Framework 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

PainCIRCLE offers a new framework to evaluate AI in real-world contexts, enhancing decision-makers' ability to gauge AI technologies' real-world impacts beyond theoretical benchmarks.

Evidence0 refs | 0 sources | 17% coverage

Blockermissing authors

Analysis summary

CIRCLE offers a new framework to evaluate AI in real-world contexts, enhancing decision-makers' ability to gauge AI technologies' real-world impacts beyond theoretical benchmarks.

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

ARXIV:2602.24055 · AI EVALUATION FRAMEWORK · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE

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

CIRCLE: A Framework for Evaluating AI from a Real-World Lens

arXiv

CIRCLE offers a new framework to evaluate AI in real-world contexts, enhancing decision-makers' ability to gauge AI technologies' real-world impacts beyond theoretical benchmarks.

Blocked on Code›Score5.0Evidence unverified

Opportunity summary

Pain CIRCLE offers a new framework to evaluate AI in real-world contexts, enhancing decision-makers' ability to gauge AI technologies' real-world impacts beyond theoretical benchmarks.

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. This can enable governance based on materialized downstream effects rather than theoretical capabilities.

WHY NOW

AI Evaluation Framework 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

PainCIRCLE offers a new framework to evaluate AI in real-world contexts, enhancing decision-makers' ability to gauge AI technologies' real-world impacts beyond theoretical benchmarks.

Evidence0 refs | 0 sources | 17% coverage

Blockermissing authors

Analysis summary

CIRCLE offers a new framework to evaluate AI in real-world contexts, enhancing decision-makers' ability to gauge AI technologies' real-world impacts beyond theoretical benchmarks.

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

Paper Pack

10.48550/arXiv.2602.24055

CIRCLE: A Framework for Evaluating AI from a Real-World Lens

CIRCLE offers a new framework to evaluate AI in real-world contexts, enhancing decision-makers' ability to gauge AI technologies' real-world impacts beyond theoretical benchmarks.

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 fallback53 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. This can enable governance based on materialized downstream effects rather than theoretical capabilities.

WHY NOW

AI Evaluation Framework 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
CIRCLE offers a new framework to evaluate AI in real-world contexts, enhancing decision-makers' ability to gauge AI technologies' real-world impacts beyond theoretical benchmarks. While existing frameworks like MLOps focus on system stability and benchmarks measure abstract capabilities, decision-makers outside the AI stack lack systematic evidence about the behavior of AI technologies under real-world user variability and constraints.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
This paper proposes CIRCLE, a six-stage, lifecycle-based framework to bridge the reality gap between model-centric performance metrics and AI's materialized outcomes in deployment. While existing frameworks like MLOps focus on system stability and benchmarks measure abstract capabilities, decision-makers outside the AI stack lack systematic evidence about the behavior of AI technologies under real-world user variability and constraints.
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. This can enable governance based on materialized downstream effects rather than theoretical capabilities.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
AI Evaluation Framework 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

AI Evaluation Framework

Competitors

not indexed

Competitive landscape

CIRCLE offers a new framework to evaluate AI in real-world contexts, enhancing decision-makers' ability to gauge AI technologies' real-world impacts beyond theoretical benchmarks.

Segment

AI Evaluation Framework

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 2602.24055 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(53)

The AI Invisibility Effect: Understanding Human-AI Interaction When Users Don't Recognize Artificial Intelligence

2026Obada Kraishan

Simulating Psychological Risks in Human-AI Interactions: Real-Case Informed Modeling of AI-Induced Addiction, Anorexia, Depression, Homicide, Psychosis, and Suicide

2025Chayapatr Archiwaranguprok, Constanze Albrecht et al.

Measuring what Matters: Construct Validity in Large Language Model Benchmarks

2025Andrew M. Bean, R. Kearns et al.

Ask What Your Country Can Do For You: Towards a Public Red Teaming Model

2025Wm. Matthew Kennedy, Çigdem Patlak et al.

Delusional Experiences Emerging From AI Chatbot Interactions or “AI Psychosis”

2025Alexandre Hudon, Emmanuel Stip

Attention to Non-Adopters

2025Kaitlyn Zhou, Kristina Gligori'c et al.

AI Hasn't Fixed Teamwork, But It Shifted Collaborative Culture: A Longitudinal Study in a Project-Based Software Development Organization (2023-2025)

2025Qing Xiao, Xinlan Emily Hu et al.

Understanding Teen Overreliance on AI Companion Chatbots Through Self-Reported Reddit Narratives

2025M. Namvarpour, Brandon Brofsky et al.

Red Teaming AI Red Teaming

2025Subhabrata Majumdar, Brian Pendleton et al.

Limits of Safe AI Deployment: Differentiating Oversight and Control

2025David Manheim, Aidan Homewood

A Different Approach to AI Safety: Proceedings from the Columbia Convening on Openness in Artificial Intelligence and AI Safety

2025Camille Franccois, Ludovic Peran et al.

Frameworks, Methods and Shared Tasks: Connecting Participatory AI to Trustworthy AI Through a Systematic Review of Global Projects

2025María Isabel Magaña, Katie Shilton

Secondary Stakeholders in AI: Fighting for, Brokering, and Navigating Agency

2025L. Ajmani, Nuredin Ali Abdelkadir et al.

Generative AI and Organizational Structure in the Knowledge Economy

2025Fasheng Xu, Jing-Min Hou et al.

Reality Check: A New Evaluation Ecosystem Is Necessary to Understand AI's Real World Effects

2025Reva Schwartz, Rumman Chowdhury et al.

Measurement to Meaning: A Validity-Centered Framework for AI Evaluation

2025Olawale Salaudeen, Anka Reuel et al.

Exploring How LLMs Capture and Represent Domain-Specific Knowledge

2025Mirian Hipolito Garcia, Camille Couturier et al.

The Cybernetic Teammate: A Field Experiment on Generative Ai Reshaping Teamwork and Expertise

2025Fabrizio Dell’Acqua, Charles Ayoubi et al.

Toward an Evaluation Science for Generative AI Systems

2025Laura Weidinger, Deborah Raji et al.

Position: Evaluating Generative AI Systems is a Social Science Measurement Challenge

2025Hanna Wallach, Meera Desai et al.

Showing 20 of 53 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 WorkTowards Apples to Apples for AI Evaluations: From Real-World Use Cases to Evaluation Scenarios

5.0

Prior WorkDialogical Reasoning Across AI Architectures: A Multi-Model Framework for Testing AI Alignment Strategies

5.0

Extension

Builds On ThisTRACE: A Metrologically-Grounded Engineering Framework for Trustworthy Agentic AI Systems in Operationally Critical Domains

3.0

Builds On ThisPrinciples and Guidelines for Randomized Controlled Trials in AI Evaluation

3.0

Builds On ThisFrom Accuracy to Readiness: Metrics and Benchmarks for Human-AI Decision-Making

4.0

Builds On ThisBeyond Behavior: Why AI Evaluation Needs a Cognitive Revolution

0.0

Builds On ThisEvaluation Cards: An Interpretive Layer for AI Evaluation Reporting

3.0

Builds On ThisTowards More Standardized AI Evaluation: From Models to Agents

4.0

Commercially relevant

Higher ViabilityBEAMS: Benchmarking and Evaluating AI for Modeling and Simulation

7.0

Higher ViabilityRISED: A Pre-Deployment Safety Evaluation Framework for Clinical AI Decision-Support Systems

7.0

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: 2602.24055
Route: /paper/circle-a-framework-for-evaluating-ai-from-a-real-world-lens
Active tab: read
Artifact: circle-a-framework-for-evaluating-ai-from-a-real-world-lens

Available agents

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

API/MCP endpoints

REST paper pack API/api/v1/paper/circle-a-framework-for-evaluating-ai-from-a-real-world-lens/paper-pack
REST build passport API/api/v1/paper/circle-a-framework-for-evaluating-ai-from-a-real-world-lens/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/circle-a-framework-for-evaluating-ai-from-a-real-world-lens

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

CIRCLE: A Framework for Evaluating AI from a Real-World Lens

Canonical ID circle-a-framework-for-evaluating-ai-from-a-real-world-lens | Route /paper/circle-a-framework-for-evaluating-ai-from-a-real-world-lens

REST example

curl https://sciencetostartup.com/api/v1/agent-handoff/paper/circle-a-framework-for-evaluating-ai-from-a-real-world-lens

MCP example

{
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  "arguments": {
    "arxiv_id": "2602.24055"
  }
}

source_context

{
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  "mode": "paper",
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  "topic_slug": null,
  "benchmark_ref": null,
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}

Buildability Receipt

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

Paper proof page receipt window

Watch and verify: CIRCLE: A Framework for Evaluating AI from a Real-World Lens

/buildability/circle-a-framework-for-evaluating-ai-from-a-real-world-lens

Watchwatch

Subject: CIRCLE: A Framework for Evaluating AI from a Real-World Lens

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/circle-a-framework-for-evaluating-ai-from-a-real-world-lens

Paper ref

circle-a-framework-for-evaluating-ai-from-a-real-world-lens

arXiv id

2602.24055

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

4a9769fe6cfcde7559895c5f86934aaf1b69ab59ccf0b1f8ec06e87dbb8d83f7

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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CIRCLE: A Framework for Evaluating AI from a Real-World Lens

CIRCLE: A Framework for Evaluating AI from a Real-World Lens

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Claim map

Constellation map

Competitive landscape

Buzz

PDF

References(53)

Related Papers

Related Resources

Subscribe to the weekly brief

Build artifacts

Brief

Experiment plan

Validation checklist

Scientific founder

Translational engineer

Domain operator

GTM lead

Regulatory/clinical advisor

Timeline