Agentic autonomy is essential for large language models (LLMs) to function as agentic AI systems, yet existing benchmarks often emphasize operational aspects, or HOW an agent behaves, such as tool use. The ability to reconsider the user’s underlying purpose, or WHY the agent acts, remains underexplored. We define this capability as "purpose-reflective autonomy" and introduce CaseInterviewBench, an interactive, interview-style benchmark inspired by consultant recruitment. The benchmark contains 104 predefined scenarios, each with public background context, an initial request, hidden facts, and ground-truth problem formulations and resolutions. The target LLM asks questions to uncover hidden facts, then identifies the actual problem and proposes a resolution, enabling us to observe how it infers, revises, and refines its understanding of the scenario’s underlying purpose in a structured setting. We evaluate hidden-fact disclosure, final problem formulation, and resolution using rule-based metrics and LLM-as-a-judge assessments. Across several frontier and open-weight models, we found substantial room for improvement. We also observed severe violations in which some models fabricated facilitator responses during the benchmark flow, potentially creating serious risks in agentic execution.

We present a tendency of large language models (LLMs) to generate absurd patterns despite their clear inappropriateness in a simple task of identifying regularities in number series. Several approaches have been proposed to apply LLMs to complex real-world tasks, such as providing knowledge through retrieval-augmented generation and executing multi-step tasks using AI agent frameworks. However, these approaches rely on the logical consistency and self-coherence of LLMs, making it crucial to evaluate these aspects and consider potential countermeasures. To identify cases where LLMs fail to maintain logical consistency, we conducted an experiment in which LLMs were asked to explain the patterns in various integer sequences, ranging from arithmetic sequences to randomly generated integer series. While the models successfully identified correct patterns in arithmetic and geometric sequences, they frequently over-recognized patterns that were inconsistent with the given numbers when analyzing randomly generated series. This issue was observed even in multi-step reasoning models, including OpenAI o3, o4-mini, and Google Gemini 2.5 Flash Preview Thinking. This tendency to perceive non-existent patterns can be interpreted as the AI model equivalent of Idola Tribus and highlights potential limitations in their capability for applied tasks requiring logical reasoning, even when employing chain-of-thought reasoning mechanisms.