Core ResearchPatternPulseAI research examines why large language models fail, how those failures manifest in real systems, and what organizations can do to govern them responsibly. The papers below form the core framework of this work, moving from technical limits to policy and human interpretation.Evans’ Law 5.0
Establishes the formal limits of large language model coherence as context length, complexity, and recursion increase, showing why error probability inevitably overtakes correctness.Why LLMs Fail
Maps coherence limits to observable failure modes in deployed systems, including hallucination, semantic drift, compounding error, and false confidence.Policy Implications
Translates known technical failure modes into concrete risks for regulation, procurement, accountability, and institutional decision-making.AI Phenomenology
Introduces AI conversation phenomenology as a field of study, examining first-person experience, meaning formation, and user experience in dialogue with humans and language models.Research 2025-2026NEW: Significance Weighting in Large Language Models: Cross-Architecture Behavioral EvidenceNEW: Coordination, Significance and Manifold Efficiency: A Path to Transformative IntelligenceNEW Source-Grounding Does Not Prevent Hallucinations: A Controlled Replication Study of Google NotebookLMWhy Agentic AI Is Problematic: The Architectural RisksBeyond Content: Proper Nouns and Semantic Governance Failures in LLMsThe Missing Key to True LLM Intelligence 3.0: An Operational Roadmap for the S VectorTwo Missing Primitives in Contemporary Language Models: Strict Semantic Dominance and Revocable Semantic DominanceResearch Summary: A Unified Theory of LLM EvolutionNEW: Applied Research: Why the S-Vector Matters: The Missing Dimension in Enterprise AI — and What Companies Should Try includes pilot overview.The Mechanistics of Hallucination Version 3.0The S-Vector: Topographic Attention and the Architecture of IntelligenceWhy Hallucinations Happen: Fracture and Repair in Transformer Systems v1The When Where and How of LLM Failures, MeasuredDoes Agentic AI Exist? v6.0AI's Accountability Gap: A Policy Blueprint for PolicymakersAI's Unmeasured Reality: How Users Are Left BehindEvans' Law 5.0: Long-Context Degradation in Multimodal Models and the Cross-Modal Degradation TaxEvans' Law: Scaling, Coherence, and Governance Implications V4.0Evans’ Law v4.1 (Extended)Evans' Law: A Predictive Threshold for Long-Context Accuracy Collapse in Large Language Models
⸻AI Articles 2023-2026NEW: The Surrender of “Agentic AI”: Two Paths ForwardNEW: UPDATED: The Apple-Google Siri Deal Just Got Undercut by a Guy in ViennaWhen Innovation Outpaces Human Comprehension: The .AI Domain Story as AllegoryNo, This Isn’t An AI Bubble: The Anatomy of Market Bubbles Through HistoryNEW: Ethical AI Disclosure: When to Use It, What to SayClosing the Loop on AGI: From Capability Levels to Functional StabilityIf Source-Grounding Prevents Hallucinations, Why Are Agentic Systems Still Failing?Why Agent Frameworks Are Everywhere — and Why That Should Make Enterprises UneasyThe Great Fragmentation: Why and How AI Has Suddenly Changed ShapeWhat Actually Happened With the METR “Autonomy” Result, And Why the Reaction Misses the PointSymbolic AI vs. Generative AI: What We Lost, What We Gained, and Why Enterprises Are Feeling the Difference NowWhy Google’s New AI “Agents” Are Actually Agentic — And Why the Workflow Is the IntelligenceThe Authority Problem: What Enterprise AI Gets Wrong About SafetyWhy AI Hallucinates — and What Executives Need to UnderstandMicrosoft Copilot Is Struggling, And This Reveals Something Inescapable About the Future of Enterprise AIMemory, Architecture and Meaning: Why MIRAS Confirms the Significance Deficit in Modern AIWhy AI Can’t Handle Proper Nouns or Technical Jargon — And Why This Problem Is IncreasingThe Enterprise AI Era Is Splitting in TwoIs Human Feedback Breaking AI? Testing Whether “Alignment” Actually Degrades IntelligenceThe Intelligence Paradox: Why AI Researchers Can’t See What’s MissingDeepSeek-R1 Shows Reinforcement Learning Can Reshape LLM ReasoningSnowflake’s Role in the AI Ecosystem, And Two Rivals Trying to Replicate Its SuccessNEW: Why the S-Vector Matters: The Missing Dimension in Enterprise AI, and What Companies Should TryArchitectural Constraints: The Physics of AI SystemsWhy Evans Law and Evans Ratio MatterWhy AI Models Don’t Know Who the Prime Minister IsIs AGI Mastery or Systems?Why AI Sounds So Smart While Being So WrongHow Transformers Actually BreakBooking.com’s AI Agent Case StudyArchitectural Regression: How GPT-5.0 Became Less Reliable Than GPT-4.0Can Generative AI Prompt Token Usage Be Tracked Today?What AI Platforms Must Do for Safety in the Face of Evans’ LawA Conversation about Enterprise AI with the CIO of ADP (part one)ADP at Web Summit Vancouver - – Part TwoWhy Crowds Behave Like a Reinforcement LearnerRedefining AI Policy: A Critical Analysis of Global FrameworksHow AI Is Transforming Legal Practice Management SoftwareAI Innovation & the Future of Canadian Business: A Conversation with PwC's Chris Dulny6 Functional Types of Artificial IntelligenceGenerative AI in Canada: Accelerating Adoption Amid Caution and OpportunityLeading the Transformation of the Enterprise Through Generative AIHow Four B2B Tech Providers Integrate AIManaging a Big, Huge Unknown: AI Risk Management StandardsThe AI Ecosystem Wars BeginAI Crossroads: Velocity and Vision After OpenAI’s First DevDayHow Elon Musk’s Strategic Inconsistencies Will Define AI EvolutionYoshua Bengio Appointed to AI Governance Leadership RolesWhat Is a Model Context Protocol – And Why Does It Matter?Prompting 101: Revenge of the HumanitiesPrompting 101: Revenge of the Humanities (Part Two)AI and Your Communications StrategyChatGPT: Better Business Communications With AIThe Multimodal Phase: Generative AI AcceleratesCanada’s AI Code of Conduct: A CritiqueIs This Report Written in ChatGPT? Does It Matter?The Intersection of AI & Digital Marketing: A Conversation With Neil PatelServiceNow to Acquire Element AI

SERVICESNEW: AI Risk & Governance AuditA structured review of how an organization is using or planning to use AI systems, focused on failure modes, governance gaps, vendor exposure, and board-level risk. The audit is designed to surface where technical limits, deployment practices, and oversight structures may create legal, operational, or reputational exposure.Engagements are fixed-scope and fixed-duration, typically completed within 30 days, with clearly defined deliverables.The audit draws on PatternPulse research into large language model failure modes, coherence limits, phenomenology, agentic AI, and AI governance, and is independent of any specific vendor or platform.Follow on: AI Opportunity & Optimization Review
(optional; available only following an audit)Corporate AI Risk Advisory ServiceAI vendors won’t warn you when their models become unreliable. We do.Pattern Pulse AI provides enterprise subscribers with continuous monitoring of frontier AI system reliability, identifying failure modes, platform-specific risks, and mitigation strategies before they become visible market problems. Our counsel is founded on deep enterprise experience, and extensive primary research experience (commissioned and public) with frontier and corporate AI modelsDelivery and cadence:
∙Weekly updates, twice monthly deep briefs, monthly phone consultation/Q and A
∙Current-state briefings on AI reliability across major platforms
∙Emergency alerts and additional briefings when specific vulnerabilities are spotted in our research, or become public.
∙Emerging risk identification and escalation guidance
∙Vendor-neutral analysis based on primary research
∙Ready-to-use templates: internal memos, testing checklists, policy updates, vendor outreach communications, internal and external communications plansWhy this matters now:
Advertised context windows exceed reliable thresholds by 60-99%. Memory leakage creates confident but weakly grounded outputs. AI-generated content is entering systems of record without detection.
Traditional consultancies won’t document these risks until after your organization is exposed.Pricing:
Subscription-based access; monthly or annual tiered fees. Pricing for one-off reports available on request. Contact us for samples and quotes.AI Policy Consulting and DevelopmentWe provide evidence-based policy guidance and development for internal or external use by companies, not for profits and governments, inclusive of platform suitability, employee use, vendor use, agreements, SLAs, risk assessments, AI system reliability, context window limitations, and failure mode prediction. Internal AI policies make acceptable practices clear and limit risk.Our research on conversational coherence collapse (Evans' Law) informs security frameworks, procurement standards, and deployment risk assessment.Services include:
- AI system reliability auditing
- Context window testing and validation protocols
- Policy frameworks for AI use
- Policy frameworks for procurement and deployment
- Security risk assessment for agentic AI systems
- Expert testimony and technical documentationClients include enterprises, organizations deploying conversational AI at scale, public sector, regulatory bodies developing AI safety standards, and companies requiring independent verification of AI vendor claims."Your work is years ahead of the rest of the field." - US public sector client, state levelVector Database & RAG ArchitectureStrategic consulting on retrieval-augmented generation (RAG) systems and vector database implementation. I help organizations move beyond basic chatbot deployments to robust, production-grade AI systems that maintain coherence and reliability.Services include:
- Vector database selection and architecture design
- Pilot design
- RAG pipeline optimization
- Context management strategies for extended conversations
- Performance testing and degradation monitoring
- Integration planning for existing systems
- Significance coding integration into RAG including taxonomies and semantics.
Ideal for organizations building internal AI tools, companies scaling from prototype to production, and teams experiencing reliability issues with existing implementations.Fees: per project or retainer-based

LICENSINGThe S-Vector framework, Evans’ Law methodology, and related research are available for commercial licensing. Organizations interested in implementing significance-weighted architectures, using Evans’ Law for system evaluation, or incorporating Fracture-Repair analysis into their AI safety protocols can contact us to discuss licensing terms.Reading, citation, and discussion are permitted; operational, commercial, or systematic use of the frameworks requires a paid license.Available for Licensing:• S-Vector Architectural Specifications
Complete framework for implementing significance-weighted attention in transformer systems and orchestration layers• Evans’ Law Evaluation Methodology
Validated approach for measuring coherence limits, predicting degradation, and establishing functional context windows• Fracture-Repair Diagnostic Framework
Mechanistic theory for identifying hallucination onset, classifying repair behaviors, and understanding vendor-specific signatures• AI Conversational Phenomenology Methodologies
Research protocols for studying real-world, customer-specific AI system behavior and user interaction dynamicsNEW: Content Composition Analyzer (CCA)A pre-generation control layer that evaluates whether source material is structurally sufficient for reliable LLM reasoning before a model is invoked.CCA is used as an upstream gate in AI pipelines to prevent forced analysis resulting in fewer hallucinations, more predictable outputs, and tighter governance over when LLMs are allowed to reason.CCA is delivered as a formal, implementation-ready specification and is designed to be embedded directly into existing LLM orchestration, RAG, or agent pipelines.NEW: Behavioral Degradation Detector (BDD)The Behavioral Degradation Detector is a post-generation monitoring layer that inspects LLM outputs for hallucinations, coherence loss, and behavioral drift.BDD analyzes generated content across multiple failure modes — structural breakdown, reasoning collapse, repetition loops, tone instability, personalized hallucinations, and vendor-specific drift signatures. It produces severity-rated findings with evidence, enabling automated rejection, human review, or escalation workflows.BDD is delivered as a formal, implementation-ready specification designed to integrate into review pipelines, quality gates, or real-time monitoring systems.This is not a complete list. Licensing includes implementation guidance, technical documentation, and ongoing research updates.COPYRIGHT & LICENSING
© 2023-2025 Jennifer Evans / PatternPulse.AI. All rights reserved.Research Publications
Academic papers published on Zenodo are licensed under Creative Commons Attribution 4.0 International (CC BY 4.0). You may share and adapt this work with appropriate attribution.Frameworks & Methodologies
Evans’ Law, the Fracture-Repair framework, S-Vector specifications, policy frameworks, and AI Conversational Phenomenology methodologies are proprietary intellectual property. Commercial use requires licensing. Contact us to discuss terms.Website Content
All articles, analysis, and original content on this site are protected by copyright. You may link to and quote from our work with attribution, but reproduction or republication requires permission.Attribution
When citing our work, please use:Evans, Jennifer. [Title]. PatternPulse.AI, [Year]. [URL or DOI]
For licensing inquiries: [email protected]