Internal Modular Policies in Complex Systems

• Internal modular policies are mechanisms where each layer or module independently enforces policies that shape overall system behavior.
• They are applied in neural language models, distributed security frameworks, and network overlays to manage control and optimize performance.
• Recent studies show that optimizing internal policies, such as through BuPO and SPPFT, improves reasoning fidelity and enhances system security.

Internal modular policies are a class of mechanisms and structures by which complex systems—such as neural networks, distributed software, or overlay networks—implement localized policy enforcement, reasoning, or control internally within their architectural layers or modules. Unlike externally imposed global policies or network perimeters, internal modular policies operate on the principle that each compositional unit (layer, module, or network hop) can encode, evaluate, or enforce a discrete policy element that collectively shapes system-wide behavior. Recent research demonstrates that such internalized policies drive critical properties in LLMs, information-flow security in distributed computation, and fine-grained network control in distributed infrastructure.

1. Formalizations in Neural LLMs

Internal modular policies arise distinctly in Transformer-based architectures, where model layers can be treated as policy contributors to the overall output distribution. For a standard decoder-only Transformer, the final output token distribution (policy) is given by $\pi(a|x) = \mathrm{softmax}(U h_L(x))_a$ where $U$ is the unembedding matrix and $h_L(x)$ the last-layer hidden state. Internal layer policies—$$\pi^{l}_{\text{Layer}}(a|x) = \mathrm{softmax}(U h_l(x))_a$$—are decoded from intermediate hidden states, revealing that each layer implicitly generates a samplable policy over the vocabulary. Moreover, "internal modular policies" partition each layer into submodules (multi-head self-attention and feedforward network, i.e., MHSA and FFN): policies $\pi^{l}_{\text{ATTN}}(a|x)$ and $\pi^{l}_{\text{FFN}}(a|x)$ correspond to the distributional "vote" from each internal module alone (Tan et al., 22 Dec 2025).

The decomposition is:

• $h_L(x) = h_l(x) + \sum_{i=l+1}^L(A^i + F^i)$,
• $$\pi(a|x) \propto \exp(z_l(a))\,\prod_{i > l} \exp(z^i(a))$$ (in logit space), with $z^i$ the module's projected contribution.

This structure enables explicit measurement and optimization of internal modular policies, making it possible to shape intermediate representations and reasoning steps in multi-step tasks.

2. Characterization and Empirical Study of Internal Modular Policies

Empirical studies using entropy as a diagnostic show that these internal modular policies follow structured dynamics across depth:

• Early layers maintain high entropy (exploration), with $\Delta H^{l}_{\text{FFN}} > 0$.
• Middle layers integrate context with plateaued entropy transitions.
• Upper layers collapse entropy, focusing probability mass (refinement).

Variations exist across architectures: LLaMA models exhibit abrupt entropy collapse in the top layers, while Qwen3 shows a gradual, human-like reduction. This suggests that some model series distribute their intermediate policy evolution more progressively, which may be beneficial for tasks requiring stepwise reasoning (Tan et al., 22 Dec 2025).

Empirically, optimizing intermediate-layer policies early in training (the BuPO algorithm) yields higher downstream performance on multi-step reasoning benchmarks than top-layer-only policy reinforcement.

Model	BuPO Avg Gains (over base)	Collapse Pattern
LLaMA	+1–3.7 points	Abrupt top-layer collapse
Qwen3	+2.1–4.6 points	Gradual, staged collapse

3. Policy Enforcement in Distributed Systems

Internal modular policies generalize beyond neural architectures. In distributed information-flow security, "internal layer policies" appear as program-declared flow policies ($\flow{F}{M}$), describing allowable flows for localized code blocks. These declared policies interact with "external layer" policies (enforced by a domain or environment), producing a two-layer control structure:

• Internal layer: Policy $F$, downward-closure operator on a security lattice, declared and local to program fragments.
• External layer: Policy $W(d)$ is the domain's allowed-flow policy.

Security is achieved by ensuring actual information flows respect both internal and external layers, formalized in distributed non-disclosure (DND), flow-policy confinement (FPC), and their conjunction as distributed non-interference (DNI). Enforcement is via type-and-effect systems or migration controls, statically or dynamically guaranteeing that flows declared internally cannot exceed limitations set externally (Matos et al., 2019).

4. Security and Adaptation in Aligned LLMs

In aligned LLMs, internal modular policies manifest concretely as localized "safety layers"—a block of contiguous hidden layers that encode refusal policies for malicious queries. Although the input embedding is similar for both benign and harmful prompts, hidden-state vectors in the safety layers diverge when facing malicious inputs, triggering the model's refusal. Mathematical detection involves tracking angular differences $\Delta\theta^\ell$ and parameter scaling sensitivity $R_o(\alpha,[a,b])$ across layers to precisely locate these layers.

During fine-tuning, preserving the integrity of these internal policies is essential for maintaining security. "Safely Partial-Parameter Fine-Tuning" (SPPFT) addresses this by freezing the gradients of the localized safety layers, preventing security degradation while still adapting the rest of the model to new domains or backdoor data. Empirical results show that SPPFT increases the harmful query rate $R_h$ negligibly (e.g., +3 pp compared to +50 pp for full fine-tuning) while matching downstream performance, demonstrating the necessity of protecting internal refusal policies (Li et al., 30 Aug 2024).

5. Internal Modular Policy Enforcement in Network Overlays

Internal modular policies are central to emerging portable network overlays for zero-trust, multi-cloud, and multi-tenant clusters. Overlay architectures embed network policy at the L3 overlay itself:

• Each pod/VM runs an overlay component encapsulating all traffic in UDP/GUE, carrying a 32-bit authorization key linked to the source-destination policy.
• Policy enforcement points (SaPs) at each node check ACLs and re-key GUE packets during routing.
• The formal policy model treats the overlay as a directed graph $(V, E)$ with policy $P \subset V \times V$ (default-deny), enforcing that $u \to v$ traffic is allowed only if the correct key is presented, providing internal, hop-by-hop policy evaluation rather than relying on external IP filtering.

This portable approach enables consistent enforcement of L3 policies regardless of native infrastructure, integrating with existing L4–7 service mesh controls. Performance overhead remains sub-millisecond on direct flows, validating the design's practicality (Farkiani et al., 5 Oct 2025).

6. Broader Security and Policy Composition Mechanisms

Internal modular policy concepts also appear at the application boundary. Browser-based "Internal Network Policy" (INP) mechanisms formalize and enforce a separation between external and internal network requests at the browser itself. INP introduces per-request preflight approval, internal-to-internal and external-to-external allowances by default, and a server-side opt-in whitelist. This policy logic, internal to the browser's request pipeline, composes with the Same-Origin Policy (SOP) and CORS, substantially mitigating attacks such as DNS rebinding or CSRF against internal network resources—demonstrating the cross-domain utility of internal modular enforcement (Afek et al., 2019).

7. Implications and Practical Takeaways

Internal modular policies enable systems to distribute control, adaptation, and reasoning across compositional boundaries, yielding several practical implications:

• In LLMs, explicitly measuring and optimizing internal layer or modular policies can lead to improved reasoning and security.
• For distributed information-flow and network security, declaring and enforcing internal (local) and external (global) policies in tandem guarantees stronger, context-sensitive non-interference properties.
• Freezing or targeting critical internal blocks (e.g., safety layers) during adaptation preserves invariants without sacrificing flexibility.
• Overlay networks demonstrate the value of embedding cryptographic and ACL-based policies at the internal routing level, decoupling enforcement from underlying physical or cloud infrastructure.

This distributed, bottom-up approach to policy control continues to inform advances in trustworthy AI, portable cloud security, and practical system defense, as substantiated across recent studies (Tan et al., 22 Dec 2025, Li et al., 30 Aug 2024, Matos et al., 2019, Afek et al., 2019, Farkiani et al., 5 Oct 2025).