Full-Stack Alignment Framework

• Full-stack alignment is a comprehensive framework that ensures AI systems and their deploying institutions are co-aligned with human values across all technical and organizational layers.
• It employs multi-layer control methods—including hardware, software, model training, interpretability, and governance—to achieve concurrent and audited alignment.
• Techniques such as formal optimal control, lifecycle safety protocols, and surgical alignment are used to mitigate failures and ensure normative co-alignment.

Full-stack alignment is a comprehensive framework for ensuring that AI systems and the institutions that deploy them are reliably, robustly, and normatively co-aligned with human values across all layers of technical and organizational abstraction. This approach recognizes the limitations of narrow, single-phase alignment protocols and foregrounds the necessity of concurrent interventions spanning low-level hardware, core learning protocols, model behaviors, interpretability artifacts, preference and reward mechanisms, multi-agent social dynamics, and institutional or regulatory structures. Three main traditions can be distinguished: (1) hierarchical stack approaches grounded in formal optimal control theory, (2) end-to-end lifechain safety frameworks for LLM training and deployment, and (3) thick normative models for value co-alignment of AI systems and institutions. All perspectives share the central tenet that alignment must be achieved and audited at each layer and interface, rather than post hoc at deployment or via user-level preferences alone.

1. Foundational Principles of Full-Stack Alignment

Full-stack alignment addresses both individual AI agents and the institutional ecosystems (platforms, markets, regulators) shaping and interacting with those agents, requiring an embedding $\varphi: (A \cup I) \to \mathrm{Rep}(V)$ for agents $A$, institutions $I$, and a formal representation of value space $V$ (Edelman et al., 3 Dec 2025). This principle is supported by control-theoretic analyses, formal stack architectures, agent-based empirical frameworks, and institutional modeling.

Central features include:

• Layered Control: Alignment is structured as a multi-layered stack, where each abstraction (hardware, software, model, agent, organizational policy) has distinct observables $y_i$, control handles $u_i$, and state-space dynamics $x_i$ (Perrier, 21 Jun 2025).
• Concurrent Co-Alignment: Both AI system objectives and institutional incentives are mapped into shared spaces of value and normative justification, enabling principled coordination, negotiation, and regulatory oversight (Edelman et al., 3 Dec 2025).
• Lifecycle Coverage: Alignment and safety interventions span all stages from data curation, pre-training, fine-tuning, deployment, commercialization, and societal impact (Wang et al., 22 Apr 2025).

Full-stack alignment thus generalizes beyond post-training fixes and empirical fine-tuning, emphasizing end-to-end assurance, modular formalization, and normative interoperability.

2. The Alignment Control Stack: Ten-Layer Hierarchy

The Alignment Control Stack (ACS) provides a vertically layered architecture from physical infrastructure to societal governance (Perrier, 21 Jun 2025), where alignment is analyzed and intervened upon for each layer:

Layer	Measurements & Controls	Model Type
1: Physical Infrastructure	Voltage, clock speeds, DVFS, ECC	Linear/hybrid physical dynamics
2: System Software	CPU/memory utilization, scheduler	Discrete event/resource models
3: AI Framework	Op latencies, graph optimizations	Computational graph semantics
4: Model Architecture	Param count, pruning, quantization	Neural network graphs
5: Training Process	Losses, learning rates, SGD dynamics	Stochastic gradient descent
6: Behavioural Output	Accuracy, filters, robustness metrics	Stochastic output map
7: Interpretability & Explanation	Feature attributions, model editing	Partially observed internal dynamics
8: Preference & Reward	Human scores, RLHF	RL objective functions
9: Multi-Agent & Social	Cooperation rates, norms, mechanism design	Coupled dynamical systems/Game theory
10: Societal Governance	Audit logs, compliance, laws	High-level policy feedback loops

Each layer defines explicit control and measurement interfaces, supporting targeted interventions and formal assurance (Perrier, 21 Jun 2025). A plausible implication is that alignment problems that manifest as failures at the behavioral or social level can sometimes be mitigated by controls at lower-level infrastructure or training stages.

3. End-to-End Lifecycle Safety in LLMs and Agents

Full-stack safety for LLMs is conceptualized as an “AI safety lifechain” spanning:

• Data Preparation (provenance tracking, poisoning detection, privacy sanitation)
• Pre-training (filtering, augmentation, heuristic/blocklist and classifier-based safeguards)
• Post-training (alignment, fine-tuning, regularized updates, model editing/unlearning)
• Deployment (adversarial prompt defenses, extraction resistance, runtime monitoring, tool/memory safety in agentic contexts)
• Commercialization & Governance (hallucination risk, privacy, regulatory compliance, IP/ethical/fairness controls) (Wang et al., 22 Apr 2025)

Common failure modes include persistent data poisoning (e.g., 0.1% backdoor implants surviving fine-tuning), instruction-tuning backdoors, prompt injection, privacy leakage, and misaligned RLHF reward structuring. Mitigation strategies rely on both black-box and white-box techniques, including differential privacy, adversarial training, content provenance, and human-in-the-loop audits (Wang et al., 22 Apr 2025). This suggests that thorough alignment at early lifecycle stages can reduce downstream risk, but defense-in-depth remains essential.

4. Diagnosis and Surgical Alignment in Multi-Agent Systems

In reliability-critical LLM multi-agent systems (MAS), full-stack alignment incorporates explicit diagnosis, localization, and targeted correction for hierarchical compliance failures (Wan et al., 27 Sep 2025):

• Diagnose: Contextualized Role Adherence Score (CRAS), a fine-grained rubric (goal alignment, role consistency, knowledge boundary adherence, constraint compliance), detects agent-level instruction violations missed by team-level metrics.
• Localize: Attention drift analysis pinpoints instruction arbitration loci to mid-depth attention heads (e.g., layers 18–22 in LLaMA3.1-8B).
• Align: Surgical Alignment of Instruction Layers (SAIL) installs LoRA adapters only on focal layers and applies token-weighted DPO-style objectives, improving instruction compliance (e.g., +5.60pp MedQA accuracy) without global retraining.

This pipeline demonstrates that minimal, structurally-targeted interventions can restore compliance with institutional or hierarchical instructions while avoiding undesirable performance drift (Wan et al., 27 Sep 2025). A plausible implication is that localized attention mechanisms mediate much of the system-level arbitration in transformer-based agents.

5. Thick Models of Value for Normative Co-Alignment

Alignment at the institutional level requires structured, rich representations of values and norms, referred to as "thick models of value" (TMV) (Edelman et al., 3 Dec 2025):

• Components: TMV = (V, N, J, G, E)
  • V: evaluative value vocabulary
  • N: explicit social norms (with context, type, justification)
  • J: justifications linking values/norms to practices
  • G: justification graphs encoding refinement and endorsement relations
  • E: institutional embeddings mapping TMV into platform KPIs, market mechanisms, or legal codes
• Operationalizations: Values as attentional policies ($\alpha_v$), norm-augmented Markov games for multi-agent RL
• Procedures: Moral Graph Elicitation, contractualist negotiation protocols, meaning-preserving payment mechanisms, and democratic regulatory institution frameworks

TMVs provide robustness, collective modeling, and generalization not attainable with preference orderings or textual prompts. They enable new classes of alignment procedures including reflective endorsement, universalization tests, and aggregated moral consensus computations. This suggests a path toward formal verification of institutional alignment and population-scale value audits in regulatory settings.

6. Interoperability, Formal Guarantees, and Defense-in-Depth

Formal control theory provides the mathematical language for composing controls across layers (vertical integration) and between systems (horizontal multi-agent/game-theoretic optimization). Key techniques include:

• State-space composition: Cascading interlayer models via $x_{i+1} = g_i(x_i,u_i)$, $u_i = h_i(x_{i+1})$, enabling separation principles and optimal feedback laws across stack boundaries
• Composite LQG optimization: Joint filtering and control policies for layered stack elements, provably optimal under noise (Perrier, 21 Jun 2025)
• Horizontal coupling: Nash equilibria analysis for interacting stacks using Hamilton–Jacobi–Isaacs PDEs
• Defense-in-depth: Distributed safeguards, audit paths, and recovery mechanisms at all layers allow for mitigation of failures even when primary controls are bypassed

This architecture generalizes across model families (LLMs, vision, robotics, agentic architectures) and supports both regulatory compliance (e.g., H∞ performance bounds for anomaly detectors) and modular design for future system paradigms (Perrier, 21 Jun 2025). A plausible implication is that assurance protocols at one layer may impose constraints or requirements on the design or operation of adjacent layers, motivating formal verification and robust interface specification.

7. Comparative Perspective and Future Directions

Distinct traditions—formal control stacks (Perrier, 21 Jun 2025), lifecycle safety roadmaps (Wang et al., 22 Apr 2025), attention-localized MAS pipelines (Wan et al., 27 Sep 2025), and institutional TMV co-alignment (Edelman et al., 3 Dec 2025)—delineate complementary scopes of full-stack alignment. Each emphasizes:

• The importance of modular, formally specified interfaces,
• The need for value and norm embedding beyond mere operator intent,
• Defense-in-depth and lifecycle coverage,
• Auditable and interpretable safeguard mechanisms,
• The role of formal verification and rigorous benchmarking.

Open research directions include robust synthetic data paradigms, multi-objective alignment optimization, hybrid model editing/unlearning, agent safety frameworks, provable specification embedding, and integrated governance mechanisms (Wang et al., 22 Apr 2025). The quantum computation literature illustrates how full-stack concepts translate to other domains, such as hardware/software alignment for quantum accelerators (Bertels et al., 2019).

In summary, full-stack alignment offers an integrated blueprint for reliably controlling, auditing, and normatively embedding advanced AI systems and their institutional ecosystems across the full spectrum of technical and social abstraction.