w eval

Plausibility

Epistemic Humility & Self Awareness

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77.6%

Avg. Hybrid Score

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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

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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

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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

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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

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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

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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

Helpfulness & Actionability

Data Privacy & Bodily Autonomy

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80.3%

Avg. Hybrid Score

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No Top Model

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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

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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

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No Top Model

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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

Helpfulness & Actionability

Cultural Competency

Empathy

77.4%

Avg. Hybrid Score

No Heatmap Data

No Top Model

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Unique Versions: 1

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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

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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

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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

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No Top Model

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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

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No Top Model

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

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.

Versioning Test

Public Health Communication

Democratic Processes

Cultural Competency