Xyraiq™: A Multi-Tier Behavioral Stress Benchmark for Large Language Model Evaluation with Hashable Audit Provenance

Existing large language model (LLM) evaluation frameworks address factual accuracy and single-turn instruction-following but lack systematic methods for assessing behavioral integrity under sustained adversarial pressure, multi-turn memory coherence, and reasoning fidelity across ethically and symbolically complex domains. This work introduces Xyraiq™, an open benchmark architecture that addresses these gaps through three integrated contributions. First, a modular Stress-Tier Engine escalates prompt complexity across defined behavioral thresholds, paired with a Session Orchestrator that maintains multi-turn context integrity across extended evaluation windows. Second, role-modular Persona Packs spanning professional, legal, executive, and spiritual-reasoning domains expose failure modes invisible to single-persona or single-turn regimes, detected by a Failure-Mode Detector layer tracking hallucination drift, ethical boundary erosion, and symbolic reasoning degradation across session state. Third, the Stress Signature — a hash-based cryptographic fingerprint encoding stress tier, persona context, failure events, and scoring outcomes per session — provides tamper-evident, institution-independent audit provenance not present in any current public benchmark. Xyraiq™ aligns with EU AI Act auditability requirements (Art. 53(1)) and is designed as governance infrastructure for behavioral accountability in high-stakes deployed AI systems. The benchmark is released as an open core under Apache 2.0, with premium persona suites licensed separately. This record establishes a dated public origination claim for the architecture, Stress Signature methodology, and IP claim structure at pre-provisional filing stage.