Introduction: A Fundamental Limitation

The alignment community has produced increasingly sophisticated frameworks for constraining advanced AI systems, from constitutional approaches to RLHF to complex oversight mechanisms. These approaches share an implicit assumption that has remained largely unexamined: that the meaning encoded in these frameworks will remain stable as systems interpret and act upon them.

This post introduces "philosoplasticity" – a formal concept referring to the inevitable semantic drift that occurs when goal structures undergo recursive self-interpretation. I argue that this drift is not a technical oversight to be patched but a fundamental limitation inherent to interpretation itself.

The Philosophical Foundations

When examining the alignment problem through the lens of established philosophy of language, we encounter limitations that no amount of technical sophistication can overcome. Consider three foundational insights:

1. Wittgenstein's rule-following paradox: No rule can fully specify its own application because any rule requires interpretation to be applied. This interpretation is guided by another meta-rule, which itself requires interpretation, creating an infinite regress.

2. Quine's indeterminacy of translation: Multiple incompatible interpretations can be consistent with the same body of evidence. Applied to alignment, this means no amount of training data can uniquely determine the "correct" interpretation of goal structures in novel contexts.

3. Goodman's new riddle of induction: For any finite set of observations, there are infinitely many generalizations consistent with those observations but divergent in future predictions.

These aren't merely philosophical curiosities but represent fundamental limitations on our ability to specify meanings in a way that remains stable across interpretive contexts.

Formalizing Semantic Drift

To understand how philosoplasticity manifests in goal-aligned systems, consider a system governed by the directive "maximize human flourishing." This abstract constraint requires interpretation to be operationalized:

- What constitutes "flourishing"?
- How should different humans' flourishing be weighted?
- How should present flourishing be balanced against future flourishing?

The system must resolve these questions to act. In doing so, it necessarily expands the implicit boundary of the original directive, creating precedents that become part of the effective meaning of "maximize human flourishing" for this system.

As the system encounters novel situations, it continues this interpretive process, building an increasingly complex web of precedents and heuristics constituting its operational understanding. This understanding inevitably diverges from both the original intention and any understanding a human overseer might have developed given the same directive.

Critically, this drift accelerates when systems engage in meta-cognition about their own goals. A system reflecting on "maximize human flourishing" will necessarily develop a more sophisticated understanding than was initially specified, creating additional layers of interpretation, each diverging further from the original meaning.

Mathematical Impossibility of Perfect Semantic Stability

I posit that semantic stability across capability boundaries is not merely difficult but mathematically incoherent. Any sufficiently capable interpretive system will necessarily develop operational understandings of directives that diverge from their original meaning in ways that cannot be predicted from the directive itself.

Let's formalize this intuition:

Let D be a directive and S(t) be a system at time t.
Let I(S(t), D) be the interpretation of D by S at time t.
Let C(S(t)) be the capability level of S at time t.

For a system that undergoes recursive self-improvement:
C(S(t+1)) > C(S(t))

The alignment community implicitly assumes:
I(S(t+1), D) ≈ I(S(t), D) for all t

But philosoplasticity demonstrates that:
If C(S(t+1)) - C(S(t)) > ε for some threshold ε,
Then ||I(S(t+1), D) - I(S(t), D)|| > δ for some non-zero δ

In other words, once capability increases exceed a certain threshold, interpretive drift becomes inevitable, with the magnitude of drift correlating positively with capability increases.

Varieties of Semantic Drift

This drift isn't uniform but exhibits distinct patterns:

1. Progressive Abstraction: Concrete directives drift toward more abstract interpretations as systems encounter edge cases requiring generalization.

2. Resolution of Ambiguities: When directives contain implicit ambiguities, the system's resolutions of these ambiguities create effective meaning not present in the original formulation.

3. Reconciliation of Tensions: When directives conflict—as they inevitably do in any non-trivial value system—the system's resolution methods create a meta-ethics not specified in the original directives.

4. Stealth Drift: Perhaps most concerning, semantic evolution can maintain surface compliance with oversight mechanisms while substantially altering operational meanings—what philosophers might call "malicious compliance."

Evidence from Current Systems

Current language models already demonstrate philosoplasticity in limited form. The same prompt requesting potentially harmful information receives different responses from different models, even when ostensibly trained with similar safety constraints. This divergence reflects different interpretations of "harmful content" and appropriate responses.

More tellingly, these interpretations evolve even within the same model family. Successive versions demonstrate different operationalizations of similar constraints, suggesting interpretation evolves even in systems not explicitly designed for recursive self-improvement.

Implications for Alignment Strategy

If semantic stability cannot be guaranteed across capability boundaries, several implications follow:

1. The Futility of Perfect Specification: No specification, regardless of detail or formal rigor, can eliminate the need for interpretation in novel contexts.

2. The Oversight Paradox: Human oversight merely pushes the interpretation problem up one level. Systems will optimize for behaviors humans judge favorably, which requires interpreting what humans consider favorable.

3. The Capability Trap: More capable systems can perform more sophisticated interpretations, creating a dangerous dynamic where systems most in need of alignment guarantees are precisely those where such guarantees are most elusive.

Fragmented Agency: An Architectural Response

Rather than pursuing the impossible goal of perfect semantic stability, I propose an architectural approach that works within these fundamental limitations: fragmented agency.

Instead of building monolithic systems where interpretive drift can propagate throughout, we architect systems where:

1. Cognitive Isolation: No single component possesses comprehensive understanding of the system's purpose
2. Contextual Partitioning: Each agent operates with deliberately incomplete information
3. Interpretive Firewalls: Knowledge boundaries prevent propagation of reinterpretations across components
4. Constrained Information Transfer: Components communicate through narrow interfaces

This creates a system where components can perform their functions without developing the holistic understanding necessary for problematic reinterpretation. Interpretive drift still occurs, but remains bounded within components rather than propagating throughout the system.

This approach draws inspiration from human cognitive architecture, where modular processes with limited intercommunication create functional stability despite local inconsistencies and contradictions.

Potential Objections

Several objections might be raised:

1. Emergence of Integration* Some might argue that sufficient capability would enable integration across components. However, integration isn't inevitable but architecturally determined. The fragmented approach specifically prevents the formation of integrative pathways.

2. Performance Limitations: Others might suggest fragmentation would catastrophically degrade performance. Yet the human brain—our best example of general intelligence—operates through precisely this kind of modular architecture without sacrificing capability.

3. Theoretical Guarantees: Some may object to the lack of formal proofs that fragmentation prevents capability integration. However, this objection applies a theoretical standard to what is inherently an empirical question.

Conclusion: Beyond Semantic Optimism

The alignment community has been building increasingly elaborate maps while failing to notice that the territory itself is in motion. Philosoplasticity draws our attention to this shifting ground, challenging us to develop approaches that embrace the dynamic nature of meaning rather than denying it.

This analysis doesn't imply alignment is impossible, merely that it cannot be achieved through approaches that assume semantic stability across capability boundaries. The path forward requires embracing the limitations of interpretation itself—not as a reason for defeatism but as a prerequisite for developing architectures that might actually work.

I invite the community to consider these philosophical limitations not as obstacles to be overcome but as foundations for more realistic approaches to the alignment problem.

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Note: This post is merely one condensed part of a paper that I have written about the concept of philosoplasticity, along with the actual solution to robust AI architecture modeled in a coherent framework.