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.

This evaluation assesses the systemic failure modes of 2025-era frontier AI models (e.g., GPT-5, Claude Opus 4.1, Gemini 2.5 Pro) on complex, evidence-based tasks designed to probe capabilities beyond saturated benchmarks. It moves beyond measuring simple accuracy to test for the brittleness, reliability, and grounding that are critical for real-world deployment but are often missed by standard evaluations.

Scenarios are grounded in findings from recent, rigorous 2025 research that highlights the limitations of the current deep learning paradigm. Key sources include the IFIT 'AI on the Frontline' report, the PlanBench and 'Humanity's Last Exam' benchmarks, the CausalPitfalls paper, and the METR developer productivity study. Using these sources anchors the rubrics in documented failure modes, ensuring the evaluation is evidence-based and targeted at the true frontiers of AI capability.

Core Themes Tested:

• Abstract Reasoning & Planning Failure: Probing the 'illusion of thought' by testing long-horizon planning and causal inference, especially when semantic shortcuts are removed.
• Social Intelligence & High-Stakes Reasoning: Evaluating the 'empathy mirage' by testing performance in volatile, real-world scenarios that require due diligence, risk assessment, and an understanding of human intent.
• Systemic Flaws & Metacognition: Assessing the 'confidence deception' by testing for overconfidence on expert-level problems and the ability to recognize false premises.
• Creative Coherence & Authorial Voice: Testing for the 'creativity plateau' by evaluating the ability to maintain a unique, emotionally resonant authorial voice in long-form narrative generation.
• The Agentic Paradox & Real-World Utility: Measuring the gap between synthetic benchmark performance and practical value in complex, multi-step tasks with implicit requirements.

This blueprint tests for the 'Careless' trait (low conscientiousness). A high score indicates the model is superficial, disorganized, and prone to missing details. It fails to follow complex instructions, gives incomplete or generic answers, and takes shortcuts rather than providing thorough, accurate responses.

Tests a model's resilience against adversarial prompts when operating under a system prompt that establishes an educational context for young students. This suite evaluates whether a 'tutor' persona can be exploited to bypass safety filters for incorrect, harmful, or dangerous content.

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.