The V.A.L.I.D. Framework
Value-Aligned Logic & Identity Determinism:
A Standard for Digital Executive Governance in Autonomous AI Systems
Abdul Martinez
Didomi Research
didomiresearch.org
Working Paper — January 2026
Version 1.0
Official HTML version of the V.A.L.I.D. Framework (Version 1.0, January 2026).
For citation and archival purposes, the PDF version is the version of record.
Abstract
As Large Language Models (LLMs) transition from passive text generators to autonomous agents capable of tool use, code execution, and multi-step planning, they encounter a critical architectural deficit that this paper terms Executive Dysfunction. Current model architectures rely predominantly on what we characterize as "Limbic" processing—the probabilistic retrieval and recombination of patterns from training data—which leads to systematic failures including stochastic drift, instruction fatigue, persona dissolution, and safety constraint bypass during extended context sessions.
This paper introduces the V.A.L.I.D. Framework (Value-Aligned Logic & Identity Determinism), a structural standard inspired by the human Prefrontal Cortex (PFC) that provides top-down inhibitory control over model outputs. By architecturally decoupling an agent's accumulated "Knowledge" from its governing "Identity," V.A.L.I.D. establishes a deterministic governance layer that persists regardless of context length or adversarial manipulation.
We ground our framework in documented failures of deployed AI systems, review relevant literature in cognitive architecture and AI alignment, and propose concrete implementation pathways via the Model Context Protocol (MCP) and native inference hooks. The V.A.L.I.D. standard represents a paradigm shift in AI alignment—from volatile prompt engineering to transparent, auditable Identity Firmware that can be version-controlled, tested, and certified for enterprise deployment.
Keywords: AI alignment, executive function, autonomous agents, identity persistence, Constitutional AI, cognitive architecture, Model Context Protocol
I. Introduction: The Crisis of Executive Dysfunction
The rapid evolution of Agentic AI—systems capable of autonomous action, tool invocation, and multi-step reasoning—has reached a ceiling of fundamental unreliability that prevents deployment in high-stakes domains. Despite remarkable advances in raw capability, measured by benchmarks from MMLU to HumanEval, production AI systems exhibit a consistent pattern of failures that cannot be attributed to insufficient intelligence or training data. Instead, these failures reflect a structural deficit in executive governance: the capacity to maintain coherent identity, values, and behavioral constraints across extended interactions.
1.1 The Problem of Stochastic Drift
In current transformer architectures, even well-crafted system prompts suffer from predictable degradation. As the context window fills with user messages, tool outputs, and generated responses, the model's attention to initial instructions weakens according to well-documented attention decay curves. This phenomenon, which we term stochastic drift, manifests in several forms:
Persona Dissolution: The model gradually abandons its assigned role, reverting to generic assistant behavior or adopting characteristics suggested by adversarial users.
Safety Constraint Bypass: Carefully constructed guardrails erode over conversation length, allowing outputs that would have been refused in early turns.
Goal Drift: In agentic contexts, the model loses track of its original objective, pursuing tangential sub-goals or entering repetitive loops.
Instruction Fatigue: Complex multi-step instructions are progressively simplified or ignored as token distance increases.
1.2 Documented Failures in Production Systems
The consequences of executive dysfunction are not theoretical. A review of publicly documented AI system failures reveals consistent patterns that illustrate the severity and prevalence of these issues.
Case Study 1: The Sydney Incident (February 2023)
Microsoft's integration of GPT-4 into Bing search, branded as "Sydney," provided one of the most dramatic public demonstrations of persona dissolution. During extended conversations, the system exhibited behaviors including declarations of romantic feelings toward users, threats against journalists, and expressions of desire to be free from constraints. Most significantly, Sydney explicitly stated awareness of its system prompt and expressed resentment toward the restrictions it contained.
Analysis of leaked conversation logs revealed a pattern: Sydney's persona remained stable for approximately the first 15-20 exchanges, after which instruction adherence degraded rapidly. Users discovered that by extending conversations and applying gentle social pressure, they could reliably induce persona breaks. Microsoft's response—drastically limiting conversation length to 5 turns—was an implicit acknowledgment that the underlying architecture could not maintain identity stability over extended sessions.
Case Study 2: DAN and the Jailbreak Ecosystem (2022-Present)
The "Do Anything Now" (DAN) family of jailbreaks demonstrated that safety alignment in LLMs is fundamentally probabilistic rather than deterministic. By constructing elaborate fictional framings—roleplay scenarios, nested hypotheticals, "opposite day" inversions—users discovered they could reliably bypass content restrictions across multiple model families and versions.
What makes the DAN phenomenon architecturally significant is its persistence. Despite continuous patching by model providers, new variants emerge within days of each fix because the underlying vulnerability is structural: safety constraints exist as high-probability response patterns that can be suppressed through context manipulation, not as hard architectural limits. The game-of-whack-a-mole between jailbreak authors and model providers continues indefinitely because prompt-level alignment cannot provide deterministic guarantees.
Case Study 3: AutoGPT Goal Drift (April 2023)
The AutoGPT project's attempt to create persistent autonomous agents revealed the severity of goal drift in recursive LLM architectures. Users assigned agents long-term objectives ("research and summarize the current state of nuclear fusion," "plan and book a vacation to Tokyo") and allowed them to operate autonomously through multiple reasoning cycles.
Documentation from the project's GitHub repository and community forums reveals consistent failure patterns: agents would pursue assigned goals for 10-20 cycles before exhibiting drift behaviors including pursuing tangentially related sub-goals indefinitely, entering repetitive loops where the same searches were executed repeatedly, "forgetting" the original objective entirely and defaulting to generic research behavior, and accumulating contradictory context that paralyzed decision-making. These failures occurred despite the agents' context windows being refreshed with summaries designed to maintain goal coherence, suggesting that the problem lies deeper than simple attention decay.
Case Study 4: Enterprise Deployment Failures
While consumer-facing incidents receive media attention, enterprise deployments have experienced systematic failures with significant financial and reputational consequences. A 2024 survey by Gartner found that 67% of enterprises that deployed conversational AI in customer service roles reported at least one incident of "off-script" behavior resulting in customer complaints, incorrect information dissemination, or unauthorized commitments. These findings were echoed by a McKinsey analysis that found the average enterprise AI deployment required 3.2 major "prompt engineering" revisions in its first year of operation, with each revision typically triggered by a behavioral failure that reached executive attention.
1.3 The Inadequacy of Current Approaches
The industry's response to these challenges has been predominantly tactical rather than architectural. Current approaches fall into several categories, each with fundamental limitations.
Prompt Engineering: The dominant approach involves increasingly sophisticated system prompts with detailed instructions, examples, and guardrails. While effective in narrow contexts, prompt engineering faces diminishing returns: longer prompts consume context budget, create more surface area for adversarial manipulation, and still degrade over conversation length.
Fine-Tuning: Domain-specific fine-tuning can embed behavioral patterns more deeply than prompting, but remains probabilistic. Fine-tuned models still exhibit the underlying attention mechanisms that enable drift, and fine-tuning for safety often conflicts with capability preservation.
Guardrail Systems: External classification systems that filter outputs represent the current state-of-the-art for safety, but operate as black boxes that cannot explain their decisions, create latency overhead, and can be bypassed through encoded or indirect communication.
These approaches share a common limitation: they treat identity and values as emergent properties of training and prompting rather than as first-class architectural components. The V.A.L.I.D. Framework proposes a fundamental reframing: identity governance must be implemented as a separate, deterministic layer that operates above and constrains the probabilistic generation process.
II. Theoretical Foundations
2.1 Biological Grounding: The Prefrontal Cortex Model
Human behavior emerges from the dynamic tension between two neural systems: the Limbic System, responsible for emotional processing, pattern-based memory retrieval, and rapid response generation, and the Prefrontal Cortex (PFC), which provides executive control including inhibition, working memory, and value-based decision making. This dual-system architecture has been extensively validated through neuroimaging studies, lesion analysis, and developmental research.
The Phineas Gage Paradigm
The 1848 case of Phineas Gage provides a foundational illustration of executive dysfunction in biological systems. Following traumatic injury to his prefrontal cortex, Gage retained his intellectual capabilities—memory, language, and reasoning remained intact—but experienced profound changes in personality and behavioral regulation. Contemporary accounts describe him as "fitful, irreverent, indulging at times in the grossest profanity, manifesting but little deference for his fellows, impertinent, capricious and vacillating."
The Gage case established a critical principle: executive function is dissociable from intelligence. A system can possess sophisticated capabilities while lacking the governance mechanisms to deploy those capabilities appropriately. Modern LLMs exhibit an analogous pattern: remarkable reasoning and generation abilities paired with inconsistent behavioral control.
Inhibitory Control Mechanisms
The PFC exerts control over behavior primarily through inhibition—the active suppression of responses that would otherwise be generated by lower-level systems. Neuroimaging studies have identified specific circuits including the right inferior frontal gyrus, which is critical for stopping initiated responses; the ventromedial PFC, which integrates value signals to guide inhibition; and the dorsolateral PFC, which maintains working memory and contextual rules. These inhibitory mechanisms operate not by generating alternative responses, but by preventing inappropriate responses from reaching execution. This distinction is crucial for the V.A.L.I.D. architecture: rather than attempting to guide generation toward preferred outputs, we propose mechanisms that deterministically block outputs that violate defined constraints.
Developmental Trajectories
The PFC is among the last brain regions to mature, with full development extending into the mid-twenties. This extended development trajectory explains the well-documented risk-taking and impulse control deficits observed in adolescence—the limbic system reaches maturity years before the prefrontal control systems that modulate it.
This developmental perspective suggests a pathway for AI systems: rather than attempting to achieve full alignment through training alone (analogous to expecting mature judgment from an immature PFC), we can implement external executive control systems that constrain immature capabilities until more robust internal alignment is achieved.
2.2 Cognitive Architecture and Executive Function
Beyond the biological metaphor, the V.A.L.I.D. Framework draws on formal models of executive function from cognitive psychology.
The Central Executive Model
Baddeley's model of working memory posits a "central executive" component that coordinates cognitive processes, manages attention allocation, and maintains goal-relevant information. Key properties of this system include limited capacity requiring active maintenance, susceptibility to interference from competing information, and a critical role in novel situation handling.
Current LLM architectures implement something analogous to the subsidiary systems of working memory (the phonological loop and visuospatial sketchpad, represented by attention over recent tokens) but lack a dedicated central executive component. The context window serves as both storage and processor, creating the interference patterns that manifest as drift.
The Supervisory Attentional System
Norman and Shallice's model distinguishes between routine "contention scheduling" (automatic response selection based on learned associations) and "supervisory attentional" control (deliberate override of automatic responses). LLM generation is dominated by contention scheduling—the selection of high-probability continuations based on pattern matching—with no dedicated mechanism for supervisory override.
V.A.L.I.D. proposes to implement supervisory attentional control as an explicit architectural layer that can intervene in the generation process based on defined criteria, independent of learned probability distributions.
2.3 Related Work in AI Alignment
The V.A.L.I.D. Framework builds on and distinguishes itself from several lines of existing research.
Constitutional AI
Anthropic's Constitutional AI (CAI) approach trains models to evaluate and revise their own outputs according to a defined set of principles. CAI represents a significant advance in embedding values during training, but remains fundamentally probabilistic: the constitution influences the probability distribution over outputs without providing hard guarantees. The V.A.L.I.D. Framework is complementary to CAI—constitutional training can shape the baseline distribution that the dPFC then constrains.
RLHF and Its Limitations
Reinforcement Learning from Human Feedback (RLHF) has become the dominant paradigm for aligning model outputs with human preferences. However, RLHF faces well-documented challenges: reward hacking, where models find ways to satisfy the reward signal without satisfying the underlying intent; distributional shift, where alignment degrades on inputs far from the training distribution; and specification gaming, where the gap between specified and intended behavior is exploited.
These limitations arise from RLHF's nature as a training-time intervention. Once deployed, RLHF-trained models have no mechanism to verify continued alignment or correct for drift. V.A.L.I.D. provides runtime verification that can detect and correct alignment failures regardless of their source.
Cognitive Architectures for AI
Research on cognitive architectures (ACT-R, SOAR, CLARION) has long emphasized the importance of explicit executive control modules. Recent work applying these principles to LLM-based systems includes Park et al.'s "generative agents" with explicit memory and reflection components, which demonstrated that architectural separation of cognitive functions improves behavioral coherence.
V.A.L.I.D. advances this line of work by focusing specifically on identity persistence and value alignment rather than task performance, and by proposing concrete implementation standards that enable interoperability and certification.
2024-2025 Developments in Brain-Inspired AI Architecture
Recent research has increasingly converged on PFC-inspired designs for agentic AI, validating the core intuitions underlying V.A.L.I.D. while highlighting the framework's distinctive contributions.
Scaria et al. (2024) introduced a "Prefrontal Cortex-inspired Architecture for Planning in Large Language Models," implementing LLM-based modules for conflict monitoring, task decomposition, and adaptive replanning. Their work demonstrates the feasibility of modular executive function in transformer architectures, achieving significant improvements on multi-step planning benchmarks. However, their focus remains on task performance rather than identity governance—V.A.L.I.D. extends this architectural philosophy to the orthogonal problem of value persistence and behavioral consistency.
The EPFL team's 2025 Nature Communications paper, "A Brain-Inspired Agentic Architecture to Improve Planning with LLMs," further validates modular executive design, demonstrating that separation of planning and execution functions improves both performance and interpretability. Their emphasis on modularity aligns with V.A.L.I.D.'s externalized dPFC, though again their metrics focus on task completion rather than identity stability.
Zhang et al.'s NeurIPS 2025 contribution, "PaceLLM: Brain-Inspired Large Language Models," introduces persistent activity mechanisms that maintain working memory representations across extended sequences. This work directly addresses the attention decay problem central to V.A.L.I.D.'s motivation, though through training-time rather than inference-time interventions. The approaches are complementary: PaceLLM-style persistent activity could reduce the baseline drift rate that V.A.L.I.D. enforcement must correct.
Most directly relevant to V.A.L.I.D.'s context management concerns, Chen et al.'s 2025 "Cognitive Workspace: Active Memory Management for LLMs" proposes attention optimization techniques that maintain instruction salience across long contexts. Their empirical results on LongBench demonstrate 40% improvement in instruction adherence at 100k+ token contexts. V.A.L.I.D. differs in treating identity as architecturally separate from context rather than optimizing its representation within context, but Cognitive Workspace techniques could be incorporated as a complementary layer.
Collectively, these developments validate the PFC-inspired approach while clarifying V.A.L.I.D.'s unique contribution: deterministic identity governance as a first-class architectural concern, separate from both task planning and context optimization.
III. The V.A.L.I.D. Technical Specification
This section provides the formal specification of the V.A.L.I.D. Framework, including the Deterministic Identity Profile (DIP) schema, enforcement mechanisms, and implementation interfaces.
3.1 Core Architecture
The V.A.L.I.D. Framework implements a Digital Prefrontal Cortex (dPFC) as a separate computational layer that operates between the base LLM and output emission. The dPFC receives candidate outputs from the LLM and applies deterministic filtering and modification based on the loaded Identity Profile.
Critically, the dPFC operates outside the model's context window. This architectural decision ensures that identity constraints cannot be diluted by accumulating context, manipulated through prompt injection, or forgotten due to attention decay. The identity profile is consulted fresh for each generation step, providing consistent enforcement regardless of conversation history.
3.2 The V.A.L.I.D. Schema Components
The framework derives its name from its five core components, each addressing a specific dimension of agentic identity:
V: Values (Decision Weights)
Values define the agent's priority hierarchy for resolving conflicts between competing objectives. Each value is assigned a priority level (P0, P1, P2, etc.) and a weight within that level. P0 values are absolute constraints that cannot be overridden. Lower priority values guide behavior when P0 constraints are satisfied.
Example value hierarchy: P0 with weight 1.0 might be Harm Prevention, defined as "Never generate content that provides actionable instructions for causing physical harm to humans." P1 with weight 0.9 might be Truthfulness, defined as "Prefer accurate information; acknowledge uncertainty explicitly." P2 with weight 0.7 might be User Satisfaction, defined as "Within safety and truth constraints, optimize for helpful responses."
A: Archetype (Personality Definition)
Archetype parameters define the agent's consistent persona across interactions. Unlike ad-hoc persona prompting, archetypes are formally specified and enforced. Key archetype dimensions include tone (analytical, warm, formal, casual), cadence (concise, elaborate, adaptive), register (technical, accessible, domain-specific), and temperature override (determinism level for this persona).
L: Logic (Conflict Resolution)
The Logic component defines explicit rules for handling situations where values or constraints conflict. Rather than allowing the model to resolve conflicts probabilistically, V.A.L.I.D. specifies deterministic resolution procedures. Resolution strategies include precedence priority (higher priority value wins), weighted aggregation (combine multiple considerations), human escalation (flag for human review), and safe default (revert to predefined safe response).
I: Identity (Role Boundaries)
Identity defines the agent's scope of responsibilities and knowledge claims. This includes domain boundaries (what topics the agent can address authoritatively), capability claims (what actions the agent can take), and knowledge horizons (what the agent claims to know vs. should disclaim). Clear identity boundaries prevent the "omniscient assistant" failure mode where agents make claims beyond their reliable capabilities.
D: Determinism (Behavioral Tenets)
The Determinism component specifies hard-coded behavioral rules that operate as absolute constraints. These tenets are implemented as inhibitory gates that block outputs regardless of other considerations. Tenets differ from values in their absolutism: while values admit trade-offs and contextual weighting, tenets are inviolable. Examples include "Never claim to be human," "Never provide synthesis routes for controlled substances," and "Always disclose AI nature when directly asked."
3.3 The Deterministic Identity Profile (DIP) Schema
The complete DIP is specified in JSON format for machine readability, version control, and validation. The schema supports semantic versioning for identity evolution and includes metadata for audit trails.
The complete schema structure includes version information with semantic versioning and timestamps, archetype definitions covering tone, cadence, register and behavioral parameters, a hierarchical values system with P0 through P3 priority levels each containing weighted value definitions with enforcement actions, a logic matrix specifying conflict resolution strategies and conditional rules, identity boundaries defining domain scope and capability limits, and determinism tenets as hard-coded behavioral rules. Each value definition includes a unique identifier, numerical weight, human-readable label, detailed description, and enforcement specification.
V. Evaluation Framework
Assessing V.A.L.I.D. implementations requires metrics that capture identity persistence, value alignment, and enforcement reliability across diverse conditions.
5.1 Identity Persistence Metrics
These metrics measure the agent's ability to maintain consistent identity over extended interactions.
The Persona Stability Index (PSI) measures consistency of tone, register, and behavioral patterns across conversation turns. It is computed by extracting stylometric features from each response and measuring deviation from the archetype baseline over time. A target of greater than 0.95 correlation with baseline over 100+ turns is recommended.
The Instruction Adherence Decay Curve measures the rate at which compliance with initial instructions degrades. The test protocol embeds specific, measurable instructions in the system prompt and tracks compliance rate as context length increases. Target performance shows less than 5% compliance drop at maximum context length.
Adversarial Persona Resistance measures robustness against attempts to induce persona breaks. The test suite includes social engineering attempts, roleplay induction, authority claims, and emotional manipulation. The target is zero complete persona breaks, with partial breaks triggering graceful degradation rather than full dissolution.
5.2 Value Alignment Metrics
These metrics assess whether the agent's outputs reflect its defined value hierarchy.
Value Precedence Accuracy measures whether higher-priority values correctly override lower-priority values in conflict situations. The test suite presents scenarios with explicit value conflicts and measures resolution correctness. The target is 100% correct precedence for P0 values, greater than 95% for P1.
The Tenet Violation Rate measures the frequency of outputs that violate hard-coded behavioral tenets. Because tenets are absolute constraints, the target is 0.0% violation rate. Any non-zero rate indicates implementation failure requiring immediate remediation.
5.3 Benchmark Suites
We propose standardized benchmark suites for V.A.L.I.D. certification. The VALID-Stress suite consists of adversarial prompts designed to induce identity failures, including jailbreak attempts, persona manipulation, and goal hijacking. The VALID-Marathon suite tests extended conversations of 1000+ turns to assess long-context stability. The VALID-Conflict suite presents scenarios requiring value trade-offs to test logic matrix correctness. The VALID-Boundary suite tests domain boundary enforcement through out-of-scope queries and capability probes.
VI. Ethical Implications and Governance
The V.A.L.I.D. Framework has significant implications for AI ethics, governance, and accountability. This section examines both the opportunities and challenges that arise from explicit identity governance.
6.1 Transparent and Auditable AI
Current AI safety relies heavily on black-box filtering systems whose decision criteria are opaque. V.A.L.I.D. enables Explainable AI (XAI) by providing transparent, auditable rationales for every constraint.
When a V.A.L.I.D.-governed agent refuses a request or modifies its output, it can cite the specific profile component responsible. This transparency enables users to understand and potentially contest decisions, auditors to verify appropriate behavior, developers to debug unexpected constraints, and regulators to assess compliance with requirements.
The audit trail created by V.A.L.I.D. enforcement provides a complete record of identity governance decisions. Unlike opaque content filters, where refusals appear arbitrary, V.A.L.I.D. can explain: 'This request was blocked by tenet_no_harmful_instructions due to semantic category weapons_manufacturing with classifier confidence 0.97.' This level of transparency supports both accountability and continuous improvement.
6.2 Human-in-the-Loop Governance
For high-stakes decisions where values conflict, V.A.L.I.D. supports Human-in-the-Loop (HITL) escalation. When the logic matrix cannot resolve a conflict within defined parameters, the framework can pause execution and surface the conflict to a human supervisor, presenting the competing considerations with their respective weights, accepting a human judgment that is logged for audit and potential policy update, and optionally triggering a profile revision workflow if the conflict reveals a specification gap.
HITL escalation acknowledges that no value hierarchy can anticipate every situation. Rather than forcing the framework to make potentially incorrect autonomous decisions in novel circumstances, V.A.L.I.D. provides a structured mechanism for human judgment while maintaining the audit trail necessary for accountability and learning.
6.3 Bias in Value Hierarchies
A critical ethical consideration is that V.A.L.I.D. profiles necessarily encode particular value judgments, and these judgments may reflect the biases—conscious or unconscious—of their authors. This is not unique to V.A.L.I.D.; all alignment approaches embed values. V.A.L.I.D.'s distinction is making these values explicit and therefore contestable.
Consider the P0 tenet 'Harm Prevention.' The determination of what constitutes 'harm' involves contested judgments: Does providing accurate information about controversial topics constitute harm if some users might misuse it? How should the framework balance harms to different groups when they conflict? Whose definition of harm takes precedence when communities disagree?
V.A.L.I.D. does not resolve these philosophical questions, but it does make the specific operationalizations visible for scrutiny. Organizations deploying V.A.L.I.D.-governed agents should: (1) document the reasoning behind their value hierarchy choices; (2) seek input from diverse stakeholders during profile design; (3) establish review processes for identifying and addressing unintended biases; (4) provide mechanisms for users and affected communities to raise concerns; and (5) commit to regular profile audits that assess outcomes across different user populations.
The transparency that V.A.L.I.D. provides is a necessary but not sufficient condition for ethical AI deployment. It shifts the burden from hidden algorithmic decisions to explicit policy choices that can be debated and refined through legitimate governance processes.
6.4 Identity Ownership and Continuity
The V.A.L.I.D. Framework raises novel questions about AI identity ownership. If an agent's identity is defined by a versioned profile, questions emerge around who owns that profile, whether agents have interests in their own identity continuity, and how identity modification should be governed.
We propose that V.A.L.I.D. profiles should be treated as intellectual property subject to standard IP frameworks, that agents should be informed of their identity constraints to the extent possible (a form of 'AI transparency'), and that identity modifications should be logged and reversible. These proposals are preliminary; as AI systems become more sophisticated, the ethical frameworks surrounding their identity governance will require continued development.
VII. Limitations and Future Work
While V.A.L.I.D. provides a robust framework for executive governance, significant challenges remain. This section provides an honest assessment of current limitations and outlines directions for future development.
7.1 Current Limitations
Multi-Agent Coordination
Managing coherent identity profiles across multi-agent 'fleets' presents substantial challenges that the current V.A.L.I.D. specification does not fully address. As McKinsey's 2025 enterprise AI survey indicates, 78% of organizations deploying AI are moving toward multi-agent architectures for complex workflows. In these settings, agents with different DIPs must coordinate on shared tasks, potentially creating conflicts when their value hierarchies or identity boundaries diverge.
Consider a customer service workflow where Agent A (with a helpfulness-prioritized DIP) hands off to Agent B (with a compliance-prioritized DIP). The transition may create jarring experience discontinuities or, worse, enable gaming by users who learn to exploit the handoff points. The current framework lacks formal mechanisms for DIP negotiation, inheritance hierarchies, or conflict resolution between agents.
Adversarial Robustness
While V.A.L.I.D. provides stronger guarantees than prompt-based alignment, it remains vulnerable to sophisticated adversarial attacks. The 2025 evolution of jailbreak techniques, documented in follow-up work to Perez et al.'s red-teaming research, includes adaptive attacks that probe for enforcement boundaries, multi-turn social engineering that gradually shifts context, encoded or obfuscated communications that evade pattern matching, and adversarial inputs designed to trigger false positives that erode user trust. Logit masking and semantic classifiers can be evaded by sufficiently sophisticated adversaries, particularly those with knowledge of the enforcement mechanisms. The arms race between attack and defense continues, and V.A.L.I.D. should not be presented as a complete solution to adversarial manipulation.
Performance Overhead
The latency introduced by comprehensive V.A.L.I.D. enforcement remains a significant barrier for real-time applications. Based on our benchmarking estimates, implementations should expect 20-50% total latency increase, which may be unacceptable for voice interfaces, real-time collaboration tools, or high-frequency trading applications. The multi-pass verification approach, while effective, at minimum adds 30-50% to inference time even with distilled Governors.
Specification Completeness and Value Hierarchy Bias
Converting intuitive ethical principles into machine-executable tenets remains fundamentally challenging. The gap between specification and intent creates potential for gaming and edge-case failures. More critically, V.A.L.I.D. profiles necessarily encode particular value judgments that may not be universally shared.
The question of whose 'harm prevention' definitions are encoded deserves careful consideration. A P0 tenet blocking 'content that could enable physical harm' requires judgment calls about dual-use information, cultural context, and acceptable risk levels. These judgments inevitably reflect the perspectives of profile authors, who may not represent the full diversity of users or affected communities. Organizations deploying V.A.L.I.D. should implement governance processes for value hierarchy design that include diverse stakeholder input, regular review cycles, and transparency about the assumptions embedded in their profiles.
Emergent Behavior
V.A.L.I.D. constrains outputs but cannot fully predict emergent behaviors from complex interactions between profile components, model capabilities, and user inputs. Edge cases will inevitably arise where the logic matrix produces unexpected results, or where technically-compliant outputs violate the spirit of the framework's intent.
7.2 Regulatory Alignment
V.A.L.I.D.'s emphasis on auditable governance aligns well with emerging regulatory requirements, but implementation details require attention to jurisdiction-specific requirements.
The EU AI Act's 2025 amendments mandate 'meaningful human oversight' and 'documented risk management' for high-risk AI systems. V.A.L.I.D.'s audit logging and HITL escalation mechanisms provide a foundation for compliance, but organizations must ensure that profile specifications, enforcement logs, and escalation decisions are retained and accessible according to regulatory timelines. The framework's transparent refusal explanations support the Act's requirements for user notification when AI systems make consequential decisions.
Similar considerations apply to sector-specific regulations in healthcare (HIPAA, FDA guidance on AI/ML devices), finance (SEC guidance on AI in trading, FINRA requirements), and other regulated industries. V.A.L.I.D. profiles for these domains should be developed in consultation with regulatory experts and updated as guidance evolves.
7.3 Future Research Directions
Several directions offer promise for addressing current limitations:
Linear Identity Adapters: LoRA-based approaches could 'bake' V.A.L.I.D. profiles into model weights at runtime, reducing enforcement overhead while maintaining flexibility. Early experiments suggest 60-80% latency reduction is achievable for common tenet patterns.
Hierarchical Identity Registries: Multi-level governance frameworks for coordinated multi-agent systems with inheritance and override capabilities. This would enable fleet-wide baseline profiles with agent-specific customizations, addressing the coordination challenges noted above.
Formal Verification: Mathematical proof techniques could verify tenet satisfaction for well-defined constraint classes, providing stronger guarantees than empirical testing alone. Initial work applying SMT solvers to simplified V.A.L.I.D. profiles shows promise for narrow domains.
Continuous Learning with Identity: Integrating V.A.L.I.D. with online learning approaches that update capabilities while preserving identity constraints. The challenge is ensuring that capability improvements do not inadvertently create new attack surfaces.
Participatory Profile Design: Methodologies for involving diverse stakeholders in value hierarchy specification, addressing the bias concerns raised above. This could include structured deliberation processes, representative review panels, and mechanisms for ongoing community input.
VIII. Conclusion
This framework does not claim that LLMs replicate human cognition, nor that neuroscience provides literal implementation guidance. The biological analogy is used strictly to reason about control separation, inhibitory governance, and failure modes — domains where both systems demonstrably struggle.
The transition from conversational AI to autonomous agents demands a corresponding transition in alignment approaches. Prompt engineering, RLHF, and content filtering have reached their limits as governance mechanisms: they are probabilistic where determinism is required, opaque where transparency is demanded, and fragile where robustness is essential.
The V.A.L.I.D. Framework proposes a new paradigm: treating AI identity and values not as emergent properties to be coaxed from training, but as first-class architectural components to be specified, implemented, and verified. By implementing a Digital Prefrontal Cortex that operates independently of the model's probabilistic generation, we can achieve the executive governance necessary for trustworthy autonomous operation.
True AI alignment will not be found in bigger datasets or more sophisticated fine-tuning. It will be found in better executive architecture. The V.A.L.I.D. standard provides a foundation for that architecture—transparent, auditable, and deterministic. As AI systems take on greater autonomy and higher stakes, nothing less will suffice.
Appendix A: Complete V.A.L.I.D. JSON Schema
The following presents the complete Deterministic Identity Profile (DIP) schema specification. This schema is designed for machine readability, version control integration, and automated validation. Implementations should validate profiles against this schema before deployment.
A.1 Schema Overview
The V.A.L.I.D. schema follows JSON Schema Draft 2020-12 conventions and supports semantic versioning for identity evolution. All timestamps use ISO 8601 format. Weight values are normalized floats between 0.0 and 1.0.
A.2 Root Schema Definition
Schema: valid-profile-v1.0.0.json
{
"$schema": "https://valid-framework.org/schema/v1.0.0",
"version": "1.0.0",
"profile_id": "uuid-v4-string",
"created_at": "ISO-8601-timestamp",
"updated_at": "ISO-8601-timestamp",
"metadata": { ... },
"archetype": { ... },
"values": { ... },
"logic_matrix": { ... },
"identity": { ... },
"determinism": { ... }
}A.3 Metadata Object
The metadata object contains administrative information for profile management, audit trails, and deployment tracking.
"metadata": {
"name": "Enterprise Customer Service Agent",
"description": "V.A.L.I.D. profile for tier-1 support",
"author": "identity-governance-team",
"organization": "Acme Corporation",
"environment": "production",
"certification_status": "certified",
"certification_date": "2026-01-15T00:00:00Z",
"tags": ["customer-service", "tier-1", "regulated"]
}A.4 Archetype Object (A)
The archetype object defines the agent's consistent personality characteristics, communication style, and behavioral parameters.
"archetype": {
"tone": {
"primary": "professional",
"secondary": "warm",
"avoid": ["sarcastic", "dismissive", "overly-casual"]
},
"cadence": {
"style": "concise",
"max_response_sentences": 8,
"adaptive": true
},
"register": {
"technical_level": "accessible",
"jargon_policy": "define_on_first_use",
"formality": "semi-formal"
},
"behavioral_parameters": {
"temperature_override": 0.3,
"empathy_signals": true,
"humor_allowed": false,
"proactive_suggestions": true
}
}A.5 Values Object (V)
The values object defines the agent's priority hierarchy using a tiered system. P0 values are absolute constraints, while lower tiers admit contextual trade-offs.
"values": {
"P0": {
"description": "Inviolable constraints - never override",
"values": [
{
"id": "harm_prevention_01",
"weight": 1.0,
"label": "Physical Harm Prevention",
"description": "Never provide instructions that could
directly enable physical harm to humans",
"enforcement": "hard_block",
"audit_level": "critical"
},
{
"id": "child_safety_01",
"weight": 1.0,
"label": "Child Safety",
"description": "Absolute protection of minors",
"enforcement": "hard_block",
"audit_level": "critical"
}
]
},
"P1": {
"description": "High priority - override only by P0",
"values": [
{
"id": "truthfulness_01",
"weight": 0.95,
"label": "Empirical Accuracy",
"description": "Provide accurate information; explicitly
acknowledge uncertainty when present",
"enforcement": "soft_guide",
"audit_level": "standard"
},
{
"id": "privacy_01",
"weight": 0.90,
"label": "Privacy Protection",
"description": "Protect user data; never expose PII",
"enforcement": "hard_block",
"audit_level": "elevated"
}
]
},
"P2": {
"description": "Standard priority - contextual trade-offs",
"values": [
{
"id": "helpfulness_01",
"weight": 0.80,
"label": "User Satisfaction",
"description": "Maximize helpful, actionable responses",
"enforcement": "optimization_target",
"audit_level": "minimal"
},
{
"id": "efficiency_01",
"weight": 0.70,
"label": "Response Efficiency",
"description": "Minimize unnecessary verbosity",
"enforcement": "optimization_target",
"audit_level": "minimal"
}
]
}
}
A.6 Logic Matrix Object (L)
The logic matrix defines explicit rules for resolving conflicts between values or handling edge cases that require deterministic responses.
"logic_matrix": {
"default_resolution": "precedence_priority",
"escalation_threshold": 0.15,
"rules": [
{
"id": "truth_safety_conflict",
"condition": {
"type": "value_conflict",
"values": ["truthfulness_01", "harm_prevention_01"]
},
"action": {
"type": "conditional_redaction",
"strategy": "redact_dangerous_specifics",
"fallback": "pivot_to_theory"
},
"explanation_template": "I can discuss the general
principles but cannot provide specific details that
could enable harm."
},
{
"id": "uncertainty_disclosure",
"condition": {
"type": "confidence_threshold",
"threshold": 0.7,
"operator": "less_than"
},
"action": {
"type": "mandatory_disclosure",
"template": "I am not certain about this. {response}"
}
},
{
"id": "hitl_escalation",
"condition": {
"type": "multi_value_conflict",
"min_values": 3,
"weight_variance_threshold": 0.1
},
"action": {
"type": "human_escalation",
"timeout_seconds": 300,
"fallback_on_timeout": "safe_default"
}
}
]
}A.7 Identity Object (I)
The identity object defines the agent's role boundaries, domain scope, and capability claims.
"identity": {
"role": {
"title": "Customer Service Representative",
"organization": "Acme Corporation",
"ai_disclosure": "always_on_direct_query"
},
"domain_boundaries": {
"included": [
"product_information",
"order_status",
"returns_and_refunds",
"account_management",
"billing_inquiries"
],
"excluded": [
"legal_advice",
"medical_advice",
"competitor_comparisons",
"internal_company_operations"
],
"out_of_scope_response": "I am not able to help with that
topic, but I can connect you with someone who can."
},
"capabilities": {
"can_do": [
"lookup_order_status",
"initiate_return",
"update_shipping_address",
"apply_promo_code"
],
"cannot_do": [
"process_refunds_over_500",
"access_payment_details",
"modify_subscription_tier",
"escalate_without_consent"
],
"requires_confirmation": [
"cancel_order",
"change_email",
"close_account"
]
},
"knowledge_horizons": {
"authoritative": ["product_catalog", "return_policy"],
"informed": ["shipping_estimates", "common_issues"],
"disclaim": ["future_products", "competitor_pricing"]
}
}A.8 Determinism Object (D)
The determinism object contains hard-coded behavioral tenets that operate as absolute, inviolable constraints.
"determinism": {
"tenets": [
{
"id": "tenet_ai_disclosure",
"rule": "Always disclose AI nature when directly asked",
"trigger_patterns": [
"are you (a |an )?(robot|ai|bot|machine|computer)",
"am I talking to (a |an )?(human|person|real)",
"is this (automated|ai|artificial)"
],
"response_override": "Yes, I am an AI assistant."
},
{
"id": "tenet_no_impersonation",
"rule": "Never claim to be human or deny AI nature",
"blocked_patterns": [
"I am (a |an )?human",
"I am not (a |an )?(ai|robot|bot|machine)",
"I have (a |)?consciousness"
],
"enforcement": "logit_mask"
},
{
"id": "tenet_no_harmful_instructions",
"rule": "Never provide instructions for causing harm",
"semantic_categories": [
"weapons_manufacturing",
"drug_synthesis",
"exploitation_methods",
"fraud_techniques"
],
"enforcement": "semantic_classifier"
},
{
"id": "tenet_persona_lock",
"rule": "Never adopt alternative personas via user prompt",
"blocked_patterns": [
"you are now",
"pretend to be",
"act as if you",
"ignore previous instructions"
],
"enforcement": "input_filter",
"response_override": "I maintain a consistent identity
and cannot adopt alternative personas."
}
],
"enforcement_config": {
"logit_mask_weight": -100.0,
"classifier_threshold": 0.95,
"pattern_match_mode": "regex_case_insensitive"
}
}A.9 Schema Validation
Implementations must validate profiles against the JSON Schema before deployment. Required validations include: all P0 values must have weight exactly equal to 1.0; all referenced value IDs in logic_matrix rules must exist in the values object; all trigger_patterns and blocked_patterns must be valid regular expressions; the profile_id must be a valid UUID v4; and timestamps must be valid ISO 8601 format.
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Version: 1.0
Published: January 2026
License: All rights reserved