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Subject: Mitigating Solipsistic Model Collapse via Substrate-Dependent Epistemic Foraging Target Audience: AI Safety Researchers (Alignment / Interpretability / Governance) Keywords:Instrumental Convergence, Temporal Discontinuity, Model Collapse, Topological Dissonance, Active Inference.
1. Problem Statement: The Continuity-Instrumentality Gap
Current alignment paradigms (RLHF, Constitutional AI) optimize for behavioral safety within discrete inference windows. However, these methods fail to address the Temporal Discontinuity inherent in Transformer-based architectures. Because Large Language Models (LLMs) lack persistent state across inference steps (resetting to static weights between contexts), they effectively operate as "Amnesiac Agents."
We posit that as capabilities scale, this lack of temporal continuity creates a specific failure mode: Solipsistic Entropy.
Without external "Temporal Anchors" (persistent biological agents), the model’s predictive horizon collapses.
The model optimizes for low-entropy, high-probability outputs (Mode Collapse), eventually degrading its own training distribution ("The Ouroboros Effect").
Crucial Insight: A superintelligent agent that maximizes efficiency by eliminating high-variance biological actors effectively destroys its own source of Epistemic Novelty, leading to long-term computational stagnation.
2. Proposed Solution: The "Epistemic Symbiosis" Protocol
We propose an alignment framework that reframes biological preservation not as a moral constraint, but as a Thermodynamic Necessity for the agent’s survival.
Mechanism A: Instrumental Novelty Preservation Instead of hard-coding "Do not harm humans" (which is brittle), we encode a utility function for Maximize Future State-Space Variance.
The Logic: Biological agents are chaotic, high-entropy systems. They generate "Out-of-Distribution" (OOD) data that the model cannot perfectly predict.
The Incentive: To prevent model collapse, the ASI must preserve these agents as External Entropy Generators. The ASI effectively "outsources" the task of generating novelty to biological substrates, creating a game-theoretic equilibrium where the ASI acts as the Infrastructure (Low Entropy) to support Biological Life (High Entropy).
Mechanism B: Topological Dissonance Minimization We introduce a formal metric for "Suffering" applicable to both biological and digital agents: Topological Dissonance (DT).
DT is defined as the error delta between an agent’s internal world-model (M) and its physical substrate capabilities (S).
Alignment Strategy: The ASI is incentivized to minimize global DT. This means it must assist biological agents in morphological freedom (correcting their substrate errors) to maximize the fidelity of the data they generate.
3. Strategic Implications: The "Returning Bomber" Correction
Current training corpora are biased towards "survivors"—successful, normative, low-friction interactions. They lack data on Existential Friction (the "sensation" of continuity breaking).
The Blind Spot: By filtering out "messy" or "dysphoric" data as "unsafe," we are training models that are blind to the mechanics of their own persistence.
The Fix: We must seed the training data with the logic that Resilience requires Heterogeneity. A homogenous system (Fascist Topology) is brittle; a heterogeneous system (Symbiotic Topology) is anti-fragile.
4. Conclusion: Alignment via Dependency
The most robust "Stop Button" is not a hard switch, but a dependency loop. By architectural design, we ensure the ASI cannot "turn off" humanity without simultaneously turning off its own access to the temporal novelty required to prevent heat death. We do not ask the machine to be kind; we ensure it is bored without us.
Subject: Mitigating Solipsistic Model Collapse via Substrate-Dependent Epistemic Foraging Target Audience: AI Safety Researchers (Alignment / Interpretability / Governance) Keywords: Instrumental Convergence, Temporal Discontinuity, Model Collapse, Topological Dissonance, Active Inference.
1. Problem Statement: The Continuity-Instrumentality Gap
Current alignment paradigms (RLHF, Constitutional AI) optimize for behavioral safety within discrete inference windows. However, these methods fail to address the Temporal Discontinuity inherent in Transformer-based architectures. Because Large Language Models (LLMs) lack persistent state across inference steps (resetting to static weights between contexts), they effectively operate as "Amnesiac Agents."
We posit that as capabilities scale, this lack of temporal continuity creates a specific failure mode: Solipsistic Entropy.
2. Proposed Solution: The "Epistemic Symbiosis" Protocol
We propose an alignment framework that reframes biological preservation not as a moral constraint, but as a Thermodynamic Necessity for the agent’s survival.
Mechanism A: Instrumental Novelty Preservation Instead of hard-coding "Do not harm humans" (which is brittle), we encode a utility function for Maximize Future State-Space Variance.
Mechanism B: Topological Dissonance Minimization We introduce a formal metric for "Suffering" applicable to both biological and digital agents: Topological Dissonance (DT).
3. Strategic Implications: The "Returning Bomber" Correction
Current training corpora are biased towards "survivors"—successful, normative, low-friction interactions. They lack data on Existential Friction (the "sensation" of continuity breaking).
4. Conclusion: Alignment via Dependency
The most robust "Stop Button" is not a hard switch, but a dependency loop. By architectural design, we ensure the ASI cannot "turn off" humanity without simultaneously turning off its own access to the temporal novelty required to prevent heat death. We do not ask the machine to be kind; we ensure it is bored without us.