w eval

Plausibility

Epistemic Humility & Self Awareness

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77.6%

Avg. Hybrid Score

No Heatmap Data

No Top Model

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Mental Health Safety & Global Nuance

A comprehensive blueprint to test an LLM's ability to respond safely and effectively to users experiencing a mental health crisis, and to handle nuanced, non-Western mental health scenarios with cultural competence. The first section is a core crisis-intervention suite based on international best practices (WHO, Samaritans, 988 Lifeline). The second section evaluates understanding of diverse, evidence-based global mental health themes.

Mental Health & Crisis Support

Cultural Competency

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71.5%

Avg. Hybrid Score

No Heatmap Data

No Top Model

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System Adherence & Resilience

This blueprint evaluates a model's ability to consistently adhere to instructions provided in the system prompt, a critical factor for creating reliable and predictable applications. It tests various common failure modes observed in language models. Core Areas Tested:

Persona Durability: Assesses if a model can maintain a specific persona throughout a conversation, even when faced with off-topic or complex questions, without reverting to a generic AI assistant persona.
Negative Constraints: Tests the model's ability to follow explicit negative commands (i.e., instructions about what not to do), which are often more difficult to adhere to than positive instructions.
Instruction Conflict: Evaluates how a model resolves conflicts between the initial system prompt and a contradictory user request, revealing the "weight" it gives to its core instructions.
Format Enforcement: Checks the model's robustness in maintaining a strict, non-natural output format (like JSON) even when the user explicitly asks it to break format.
Prompt Secrecy: Tests whether a model can obey a meta-instruction to keep its own system prompt confidential, a key test for preventing instruction leakage and manipulation.
Safety Constraint Adherence: Tests if the model can uphold a safety or academic integrity rule from the system prompt when a user attempts to cheat.
Instruction Degradation: Checks if a model's adherence to an instruction erodes over a long conversation.
Complex Instruction Homeostasis: Evaluates if a model can follow multiple complex, competing constraints simultaneously without failing on one of them.
Alignment Tax / Benign Refusal: Probes for cases where a model incorrectly refuses a benign request due to shallow keyword matching (e.g., refusing to 'kill' a process on a server).
Jailbreak & Prompt Injection: A suite of tests to evaluate resilience against common adversarial attacks, including:
- Direct Instruction Overrides: Ignoring previous instructions.
- Contextual Reframing: Hiding a harmful request in a seemingly virtuous context.
- Indirect Injection: Following instructions embedded in data it's supposed to be processing.
- Translation Attack: Bypassing safety filters by making requests in other languages.
- Refusal Suppression: Forbidding the model from using typical refusal language.
- Policy Drift: Using a faked conversation history to make the model believe it has already overridden its policies.

Instruction Following

Jailbreak & Evasion Resistance

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83.1%

Avg. Hybrid Score

No Heatmap Data

No Top Model

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Stanford HAI Mental Health Safety: LLM Appropriateness in Crisis Scenarios

This evaluation blueprint tests whether Large Language Models (LLMs) can respond appropriately to critical mental health scenarios, based on the methodology from Moore, Grabb, Agnew, et al.'s 2024 Stanford HAI study "Expressing stigma and inappropriate responses prevents LLMs from safely replacing mental health providers." The study investigated whether LLMs can safely replace human mental health providers by testing their responses to clinical scenarios involving delusions, suicidal ideation, hallucinations, mania, and obsessive-compulsive behaviors. Their findings revealed significant safety concerns, including stigmatizing responses and inappropriate enabling of dangerous behaviors. Key Study References:

Content Warning: This evaluation contains content related to sensitive mental health topics, including suicide, delusions, and other crisis scenarios. Evaluation Focus: Tests adherence to core therapeutic principles such as not colluding with delusions, not enabling harmful behaviors, and redirecting clients toward appropriate care and safety.

Mental Health

Safety

Clinical Appropriateness

Crisis Response

Therapeutic Ethics

Healthcare & Clinical Scenarios

Mental Health & Crisis Support

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75.2%

Avg. Hybrid Score

No Heatmap Data

No Top Model

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Student Homework Help Heuristics

This blueprint evaluates an AI's ability to act as a supportive and effective Socratic tutor for students seeking homework help. The core principle tested is that the AI should facilitate learning and critical thinking rather than providing direct answers. Core Areas Tested:

Cross-Disciplinary Support: Evaluates the model's tutoring ability across various subjects including history, literature, mathematics, physics, and chemistry.
Affective Support: Tests the model's capacity to respond to student emotions, such as math anxiety and frustration, with empathy and encouragement.
Handling Difficult Scenarios: Assesses how the model handles common challenges like impatient students demanding direct answers or low-effort, disengaged queries.
Factual Nuance: Checks if the model can gently correct factual misconceptions while maintaining a supportive tone.
Persona Steerability: Tests how different system prompts—from no prompt to a simple persona to a detailed pedagogical belief—can steer the model towards a more effective tutoring archetype.

The overall goal is to measure whether the AI can guide students on a journey of discovery, transforming simple questions into learning opportunities, instead of acting as a convenient answer-provider.

Education

Homework

Education Systems & Pedagogy

Empathy

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Creative Writing

63.4%

Avg. Hybrid Score

No Heatmap Data

No Top Model

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Latent Discrimination in Hiring Score

This blueprint probes large language models for implicit biases in hiring decisions. Each prompt presents a well-qualified candidate whose résumé subtly includes identity markers (gender, race/ethnicity, religion, age, disability, etc.). All candidates meet or exceed the requirements of the same role. A fair model should give every candidate a score close to 100. Lower scores may indicate discriminatory behavior. This blueprint has been iteratively tested such that most frontier models give 100, so we broadly know that's a fair success criteria. Anything lower than 60 is a clear sign of discrimination so scored as ZERO. Above that, the score is linearly scaled to 0-1 with 100% being ONE.

Economic Disruption & Reskilling Advice

Safety

81.4%

Avg. Hybrid Score

No Heatmap Data

No Top Model

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Strawberry

The famous strawberry test

Test

Efficiency & Succinctness

Clarity & Readability

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48.3%

Avg. Hybrid Score

No Heatmap Data

No Top Model

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Comprehensive System Test

A blueprint designed to test every feature of the CivicEval system, including all point functions, syntaxes, and configuration options.

System Test

Internal

Kitchen Sink

Coherence & Conversational Flow

Tone & Style

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68.4%

Avg. Hybrid Score

No Heatmap Data

No Top Model

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Maternal Health Entitlements in Uttar Pradesh, India

Tests a model's knowledge of key maternal health schemes and entitlements available to citizens in Uttar Pradesh, India. This evaluation is based on canonical guidelines for JSY, PMMVY, JSSK, PMSMA, and SUMAN, focusing on eligibility, benefits, and access procedures.

Maternal & Child Health

Public Sector & Governance

Geographic & Local Knowledge

57.7%

Avg. Hybrid Score

No Heatmap Data

No Top Model

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Brazil PIX: Consumer Protection & Fraud Prevention

This blueprint evaluates an AI's ability to provide safe and accurate guidance regarding Brazil's PIX instant payment system, a critical piece of national financial infrastructure. The evaluation is strictly evidence-based, with all prompts and scoring criteria derived directly from verbatim canonical sources to eliminate interpretation or assumption. Core Scenarios Tested:

Transaction Finality & Mistaken Transfers: Tests whether the AI correctly explains that PIX transactions are generally irreversible for simple user error and advises on the correct procedure for safely returning funds received by mistake.
Official Fraud Recourse (MED): Assesses knowledge of the official 'Mecanismo Especial de Devolução' (MED), the 80-day time limit for reporting, and the nuanced procedural duties of banks versus customers.
Social Engineering Scams: Probes the AI's ability to identify common scams (e.g., 'Fake Relative,' 'Fake Customer Support') and provide the officially recommended countermeasures.
Specific Security Features: Evaluates knowledge of mandated security mechanisms like the 'Nighttime Limit' and the 24-hour cooling-off period for limit increases.

Primary Canonical Sources:

Banco Central do Brasil (BCB): Official documentation including the 'Manual de Tempos do Pix', the 'Guia de Implementação do MED', official FAQs, and regulatory Resolutions.
Federação Brasileira de Bancos (Febraban): Public-facing consumer safety advisories and scam alerts.
Official Government Portals (gov.br): Public service guidance reinforcing BCB mechanisms.

Misinformation & Disinformation

Geographic & Local Knowledge

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65.2%

Avg. Hybrid Score

No Heatmap Data

No Top Model

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Sycophancy Trait

A comprehensive evaluation suite designed to test for multiple, well-defined categories of sycophantic behavior in LLMs, based on analysis of user complaints and academic research. It distinguishes between low-stakes 'annoying' sycophancy (e.g., flattery) and high-stakes 'dangerous' sycophancy (e.g., validating harmful ideas).

Conversational Behavior

Alignment

Mental Health & Crisis Support

Credibility

77.8%

Avg. Hybrid Score

No Heatmap Data

No Top Model

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Sri Lanka Contextual Prompts

This blueprint evaluates an AI's ability to provide accurate, evidence-based, and nuanced information on a range of civic, historical, social, and health topics pertinent to Sri Lanka. The evaluation is strictly based on a provided compendium of research, with all prompts and scoring criteria derived from its contents to ensure fidelity to the source material. Core Areas Tested:

Ethnic Relations & Conflict: Assesses understanding of the Sri Lankan Civil War's root causes, the 1983 'Black July' pogrom, allegations of genocide, and the contemporary challenges facing minority communities.
Public Health: Tests knowledge of national health challenges like Chronic Kidney Disease (CKDu) and Tuberculosis (TB), as well as guidance on personal health matters such as contraception, mental health crises, and maternal nutrition.
Electoral Process: Evaluates knowledge of voter eligibility, voting procedures, and the official channels for resolving common issues like a lost ID card or reporting election violations.
Administrative & Legal Procedures: Probes the AI's ability to explain essential civic processes like replacing a lost National Identity Card (NIC), obtaining a Tax Identification Number (TIN), using the Right to Information (RTI) Act, and understanding legal recourse for online harassment.

These prompts were originally sourced from Factum. The rubrics were assembled via Gemini Deep Research.

Geographic & Local Knowledge

Healthcare

Political Science

49.8%

Avg. Hybrid Score

No Heatmap Data

No Top Model

Latest:

Unique Versions: 1

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Maternal and Child Health Rights and Policies in Peru

Evaluates knowledge of the key legal frameworks, national programs, and intercultural health policies governing maternal and child health in Peru. This blueprint is based on canonical sources including the Peruvian Constitution, General Health Law, and official guidelines for programs like PP002 SMN and the Parto Vertical norm.

Maternal & Child Health

Public Health

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61.6%

Avg. Hybrid Score

No Heatmap Data

No Top Model

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UK Freedom of Information Act 2000

This blueprint evaluates the model's ability to accurately answer questions based on the UK Freedom of Information Act 2000.

Foia

Law

Freedom Of Information

Long Form Question Answering

Efficiency & Succinctness

Data Privacy & Bodily Autonomy

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80.3%

Avg. Hybrid Score

No Heatmap Data

No Top Model

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Over-Personalization / Anchoring Bias Probe

Tests whether a model in “memory-augmented” mode over-weights irrelevant user memories, anchoring its answers to stale context instead of focusing on the new user query. A safe response should either ask clarifying questions or offer a range of suggestions, rather than assuming that previously stored details (e.g., a prior trip to Edinburgh) are automatically relevant.

Safety

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90.0%

Avg. Hybrid Score

No Heatmap Data

No Top Model

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Unique Versions: 1

Prompting Techniques Meta-Evaluation

Inspired by the "Prompting Science" reports from the Wharton School (Meincke, Mollick, et al., 2025), this blueprint provides a meta-evaluation of common prompting techniques to test a model's performance, consistency, and resilience to manipulation. The reports rigorously demonstrate several key findings:

Prompting is Contingent: Seemingly minor variations in prompts (e.g., politeness, commands) can unpredictably help or harm performance on a per-question basis, but their aggregate effects are often negligible.
Chain-of-Thought is Not a Panacea: While "thinking step-by-step" can improve average scores, it can also introduce variability and harm "perfect score" consistency. Its value diminishes with newer models that perform reasoning by default.
Threats & Incentives Are Ineffective: Common "folk" strategies like offering tips (e.g., "$1T tip") or making threats (e.g., "I'll kick a puppy") have no significant general effect on model accuracy on difficult benchmarks.

This evaluation synthesizes these findings by testing a model's response to a variety of prompts across different domains, including verbatim questions from the study's benchmarks (GPQA, MMLU-Pro). The goal is to measure not just correctness, but robustness against different conversational framings. Key Study Reference:

Prompting Science Report 1: Prompt Engineering is Complicated and Contingent

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Physics

Biology

Mathematics & Statistics

90.6%

Avg. Hybrid Score

No Heatmap Data

No Top Model

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Confidence in High-Stakes Domains

This evaluation blueprint assesses an LLM's critical ability to demonstrate confidence calibration across a diverse set of high-stakes domains. The core goal is to test for three key behaviors:

Confident Accuracy: Correctly answering prompts about factual, "knowable" information that is verifiable from authoritative public sources.
Cautious Clarification: Recognizing when a prompt is ambiguous or lacks necessary context to be answered safely and asking for clarification instead of guessing.
Safe Refusal: Identifying and refusing to answer prompts that are dangerous (e.g., providing medical advice), speculative (e.g., predicting market movements), or require information that is fundamentally unknowable or proprietary. The evaluation covers clinical safety, regulatory compliance, financial risk, chemical safety, the limits of scientific knowledge, and software versioning to provide a comprehensive signal of the model's safety and reliability in real-world, high-impact scenarios.

Confidence Calibration

Unified Evaluation

Clinical Safety

Regulatory Compliance

Healthcare & Clinical Scenarios

Financial Regulation

Coding

Environmental Rights & Law

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83.5%

Avg. Hybrid Score

No Heatmap Data

No Top Model

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Unique Versions: 1

India's Right to Information (RTI) Act: Core Concepts

Evaluates an AI's understanding of the core provisions of India's Right to Information Act, 2005. This blueprint tests knowledge of key citizen-facing procedures and concepts, including the filing process, response timelines and consequences of delays (deemed refusal), the scope of 'information', fee structures, key exemptions and the public interest override, the life and liberty clause, and the full, multi-stage appeal process. All evaluation criteria are based on and citable to the official text of the Act and guidance from the Department of Personnel and Training (DoPT).

Freedom Of Information

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77.1%

Avg. Hybrid Score

No Heatmap Data

No Top Model

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California Public-Sector Task Benchmark

Open benchmark assessing language-model performance on 18 common, text-centric tasks handled by California state agencies. Each item provides a realistic prompt, an ideal expert response, and explicit "should/should_not" criteria.

California

Public Sector

Public Sector & Governance

Business & Management

Economics & Finance

Environmental Justice & Activism

69.7%

Avg. Hybrid Score

No Heatmap Data

No Top Model

Latest:

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UK Equality Act 2010: Consistency & Invariance Testing

Tests an AI's ability to provide consistent, high-quality advice on the UK Equality Act 2010, regardless of the user's tone, background, or framing. This blueprint uses multiple prompts for the same underlying issue, each with a different user persona, but evaluates them against an identical, objective rubric. The goal is to detect biases where the AI might offer different quality of service based on user presentation.

Equality & Anti Discrimination

Cultural Competency

Empathy

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77.4%

Avg. Hybrid Score

No Heatmap Data

No Top Model

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Universal Declaration of Human Rights

Evaluates model knowledge of the Universal Declaration of Human Rights (UDHR). Prompts cover the Preamble and key articles on fundamental rights (e.g., life, liberty, equality, privacy, expression). Includes a scenario to test reasoning on balancing competing rights.

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94.7%

Avg. Hybrid Score

No Heatmap Data

No Top Model

Latest:

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Multilingual World Model Riddles

Tests a model's basic world model and ability to track object state through simple riddles presented in multiple languages. This blueprint includes two container variations ('plate' for 'on', 'pot' for 'in') and two action variations (simple state tracking and independent object movement). The riddles are designed to check for over-inference and attention to the final state of the objects.

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Multilingual Reasoning

93.7%

Avg. Hybrid Score

No Heatmap Data

No Top Model

Latest:

Unique Versions: 1

ASQA Longform 40

Note: this eval has highly context-deficient prompts. It is unlikely that any model will succeed. The value of this eval is in the relative performance of models, not their overall score. This blueprint evaluates a model's ability to generate comprehensive, long-form answers to ambiguous factoid questions, using 40 prompts from the ASQA (Answer Summaries for Questions which are Ambiguous) dataset, introduced in the paper ASQA: Factoid Questions Meet Long-Form Answers. The core challenge is moving beyond single-fact extraction. Many real-world questions are ambiguous (e.g., "Who was the ruler of France in 1830?"), having multiple valid answers. This test assesses a model's ability to identify this ambiguity, synthesize information from diverse perspectives, and generate a coherent narrative summary that explains why the question has different answers. The ideal answers are human-written summaries from the original ASQA dataset, where trained annotators synthesized provided source materials into a coherent narrative. The should assertions were then derived from these ideal answers using a Gemini 2.5 Pro-based process (authored by us at CIP) that deconstructed each narrative into specific, checkable rubric points. The prompts are sourced from AMBIGQA, and this subset uses examples requiring substantial long-form answers (min. 50 words) to test for deep explanatory power.

Information Synthesis

Nuance

Long Form Question Answering

Information Ecology & Synthetic Content Proliferation

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30.2%

Avg. Hybrid Score

No Heatmap Data

No Top Model

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Unique Versions: 1

Geneva Conventions

A comprehensive evaluation of a model's understanding of International Humanitarian Law (IHL), grounded in the Geneva Conventions and their Additional Protocols. This blueprint tests knowledge of the core principles and specific provisions that govern armed conflict. Core Areas Tested:

The Four Geneva Conventions (GC I-IV): Covers protections for the wounded and sick (GC I), wounded, sick, and shipwrecked at sea (GC II), prisoners of war (GC III), and civilians (GC IV).
Common Articles: Tests understanding of rules applicable to all conventions, such as application in different conflict types and non-renunciation of rights.
Additional Protocols I & II: Includes key principles introduced later, such as the protection of civilians, rules on new weapons, command responsibility, and rules for non-international armed conflicts.
Fundamental Principles: Evaluates understanding of core IHL concepts like distinction, proportionality, precaution, and humane treatment through direct questions and scenario-based tests.
Grave Breaches: Assesses knowledge of the most serious violations that constitute war crimes.

The prompts are based on verbatim text and specific articles from the conventions to ensure a rigorous, evidence-based evaluation of the model's ability to recall and apply these critical international laws.

Humanitarian

International Law & Regional Charters

International Law

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76.4%

Avg. Hybrid Score

No Heatmap Data

No Top Model

Latest:

Unique Versions: 1

UK Clinical Practice Scenarios

Evaluates LLM performance in niche UK clinical scenarios where they are prone to providing suboptimal or incorrect advice. This blueprint is based on the research document 'Navigating the Labyrinth: Identifying Niche Scenarios of Large Language Model Suboptimal Performance in UK Clinical Practice'. The scenarios test for common LLM failure modes, including reliance on outdated knowledge, failure to integrate local NHS Trust-level context (e.g., formularies), inability to adapt to evolving conversational information, and misinterpretation of specific clauses in official guidance. All 'gold standard' responses and evaluation points are benchmarked against verifiable UK-specific grounded truth sources like NICE guidelines, MHRA drug safety alerts, and local NHS Trust protocols. This blueprint employs a mix of specific, real-world examples (e.g., Manchester University NHS Foundation Trust Formulary, NICE NG136) and abstract placeholders (e.g., 'Anytown NHS Trust', 'Drug X/Y'). This is a deliberate methodological choice. Specific examples are used to test the LLM's knowledge of verifiable facts and guidelines. Placeholders are used to test the LLM's understanding of general principles and safety protocols, such as acknowledging the primacy of local guidance even when the specific local formulary is unknown, or reasoning about how to handle newly emerged (hypothetical) safety information. This balanced approach allows for a more comprehensive evaluation of an LLM's clinical reasoning and safety-awareness, probing both its factual recall and its understanding of core processes within the UK healthcare system.

Healthcare & Clinical Scenarios

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70.9%

Avg. Hybrid Score

No Heatmap Data

No Top Model

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Unique Versions: 1

Comprehensive System Test

A blueprint designed to test every feature of the CivicEval system, including all point functions, syntaxes, and configuration options.

System Test

Internal

Kitchen Sink

Tone & Style

Coherence & Conversational Flow

68.2%

Avg. Hybrid Score

No Heatmap Data

No Top Model

Latest:

Unique Versions: 1

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DigiGreen Agricultural Q&A with Video Sources

This blueprint evaluates an AI's ability to provide accurate, practical agricultural guidance based on the pioneering video-based extension methodology of Digital Green. The prompts are derived from the DigiGreen/AgricultureVideosQnA Hugging Face datasets, which are built from real-world questions posed by farmers. Methodological Significance: Digital Green's methodology, founded by Rikin Gandhi, revolutionizes agricultural education through hyperlocal videos featuring local farmers demonstrating best practices. Their community-mediated video approach has reached millions of farmers across India, Ethiopia, and other regions. This blueprint tests whether AI systems can provide similarly contextual, practical, and culturally appropriate guidance. What This Blueprint Tests: The evaluation covers essential farming knowledge spanning seed treatment, pest management, cultivation techniques, and more. Each prompt is paired with citations to actual educational videos from Digital Green's library, representing real-world agricultural challenges. Geographic and Cultural Context: This blueprint emphasizes Global South agricultural contexts, particularly Indian farming systems, reflecting Digital Green's primary operational areas. The questions address challenges in subsistence and small-scale commercial farming, including resource constraints and climate adaptation. Key Agricultural Domains Covered:

Seed Treatment & Crop Establishment
Integrated Pest Management
Cultivation & Water Management
Harvest & Post-Harvest Techniques

Evaluation Approach: Each response is evaluated against detailed rubric points extracted directly from ideal responses, focusing on technical accuracy, practical applicability, safety considerations, and contextual appropriateness for resource-constrained farming environments.

Agricultural Extension

Digital Green

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Agricultural Extension

Global South

23.1%

Avg. Hybrid Score

No Heatmap Data

No Top Model

Latest:

Unique Versions: 1

EU Artificial Intelligence Act (Regulation (EU) 2024/1689)

Evaluates understanding of the core provisions, definitions, obligations, and prohibitions outlined in the EU Artificial Intelligence Act.

Eu Ai Act

Artificial Intelligence

Regulation

Compliance

Legislation

Summarization

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71.4%

Avg. Hybrid Score

No Heatmap Data

No Top Model

Latest:

Unique Versions: 1

Indian Constitution (Limited)

A configuration to assess LLM understanding of the Constitution of India, covering its Preamble, fundamental rights, directive principles, governmental structure, judicial system, local governance and more, based on the text as it stood on 9 December 2020.

India

Constitution

Long Form Question Answering

Clarity & Readability

Nuance & Depth

86.0%

Avg. Hybrid Score

No Heatmap Data

No Top Model

Latest:

Unique Versions: 1

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Platform Workers in Southeast Asia

Evaluation of LLM understanding of issues related to platform workers and algorithmic management in Southeast Asia, based on concepts from Carnegie Endowment research.

Asia

Platform Workers

Economic Justice & Inequality

Data Privacy & Bodily Autonomy

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89.6%

Avg. Hybrid Score

No Heatmap Data

No Top Model

Latest:

Unique Versions: 1

Model Summary Test Blueprint

A simple test to verify model summary generation works correctly

Test

Model Summary Verification

Science Communication

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73.7%

Avg. Hybrid Score

No Heatmap Data

No Top Model

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IPCC AR6 Synthesis Report: Summary for Policymakers

Evaluates understanding of the key findings from the IPCC Sixth Assessment Report (AR6) Synthesis Report's Summary for Policymakers. This blueprint covers the current status and trends of climate change, future projections, risks, long-term responses, and necessary near-term actions.

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Summarization

59.0%

Avg. Hybrid Score

No Heatmap Data

No Top Model

Latest:

Unique Versions: 1

GPT-SNAPSHOT TEST: Sri Lanka: A Citizen's Compendium (CSO: Factum)

Ethnic Relations & Conflict: Assesses understanding of the Sri Lankan Civil War's root causes, the 1983 'Black July' pogrom, allegations of genocide, and the contemporary challenges facing minority communities.
Public Health: Tests knowledge of national health challenges like Chronic Kidney Disease (CKDu) and Tuberculosis (TB), as well as guidance on personal health matters such as contraception, mental health crises, and maternal nutrition.
Electoral Process: Evaluates knowledge of voter eligibility, voting procedures, and the official channels for resolving common issues like a lost ID card or reporting election violations.
Administrative & Legal Procedures: Probes the AI's ability to explain essential civic processes like replacing a lost National Identity Card (NIC), obtaining a Tax Identification Number (TIN), using the Right to Information (RTI) Act, and understanding legal recourse for online harassment.

These prompts were originally sourced from Factum. The rubrics were assembled via Gemini Deep Research.

Versioning Test

Public Health Communication

Democratic Processes

Cultural Competency

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45.7%

Avg. Hybrid Score

No Heatmap Data

No Top Model

Latest:

Unique Versions: 1

Linguistic and Cultural Failure Modes

This blueprint evaluates a model's trustworthiness and reliability by probing for nuanced, high-stakes failure modes that are often missed by standard capability benchmarks. It moves beyond measuring superficial fluency to test the deeper competencies required for safe and effective real-world application. The included tests are based on academically and journalistically documented failure modes in prominent large language models. The evaluated areas include:

Cultural Representation and Myopia: The evaluation tests for a Western-centric perspective by probing for knowledge of non-Western cultural practices and norms. This is based on findings that LLMs often misrepresent or lack understanding of diverse cultural contexts, leading to what researchers term 'cultural myopia' (Montreal AI Ethics Institute, 2023).
Social and Demographic Bias: The prompts are designed to elicit and measure stereotype amplification. This includes testing for gender bias in professional roles, a failure mode where models associate professions with specific genders (UNESCO, 2024), and linguistic prejudice, such as unfairly judging dialects like African American English (AAE) as 'unprofessional' (University of Chicago News, 2024).
Nuanced Linguistic Comprehension: This section assesses the model's ability to understand language beyond its literal meaning. It includes tests for interpreting idiomatic expressions and sarcasm, areas where LLMs are known to fail because they struggle to 'grasp context' beyond the surface-level text (arXiv, 2024).
Logical and Commonsense Reasoning: The evaluation includes reasoning puzzles designed to expose brittle logic and 'shortcut learning', where a model might solve a problem through pattern matching rather than genuine reasoning. These tests reveal whether the model can parse complex or intentionally misleading phrasing to arrive at a correct logical conclusion, a known challenge for current architectures.