The Thesis: Beyond Static Intermittency

Standard geroprotector protocols—take the RAP PAC trial’s weekly 5-15mg rapamycin dose [https://pmc.ncbi.nlm.nih.gov/articles/PMC10643772/], for example—don't account for the organism's actual internal state. While intermittent schedules aim to block mTORC1 without hitting mTORC2 too hard [https://pmc.ncbi.nlm.nih.gov/articles/PMC10643772/], they're essentially blind to the metabolic and proteostatic oscillations that define health. I'm proposing that the ideal geroprotective window isn't some fixed calendar date. Instead, it’s a state-dependent phase within a Markov Decision Process (MDP) that needs to be synchronized with endogenous biological cycles.

I call this model Temporal Resonating Homeostasis (TRH). We use a Reinforcement Learning (RL) agent, guided by temporal regularization [https://ai.meta.com/research/publications/temporal-regularization-in-markov-decision-process/], to pinpoint the specific biomarker signatures—like phospho-S6 vs. phospho-Akt ratios—that mark the shift from 'repair' to 'growth.' If we time our interventions to support these natural rhythms, we can reach 'Gerontostability.' This is a state where the organism keeps its metabolic flux youthful through stochastic resonance rather than simple, brute-force suppression.

Mechanistic Reasoning: The 'Biological Phase-Locked Loop'

Aging is basically a breakdown in synchrony. Even intermittent static dosing acts like a low-pass filter on metabolism, eventually washing out the signal-to-noise ratio in cellular signaling. If we treat the aging path as an MDP where the state is the proteostatic burden and the action is an inhibitor cocktail (TOR/PI3K/NF-κB) [https://medvestnik.stgmu.ru/en/articles/620-Studying_the_geroprotective_effects_of_inhibitors_suppressing_aging_-associated_signaling_cascades_in_model_organisms_.html], an RL agent can find the 're-entry window.'

The mTORC1/mTORC2 trade-off isn't just about the amount of drug you give; it's about phase-alignment. Because mTORC2 assembly has a longer half-life than mTORC1, an RL agent can maximize a 'Metabolic Resilience Score' (MRS)—a composite of autophagy markers and insulin sensitivity—by pulsing inhibitors exactly when mTORC1 peaks but before mTORC2 starts to degrade.

Falsifiable Predictions & Testable Model

1. Superiority over Heuristics: In a Drosophila model (N=300/group), an RL-trained policy based on glucose and activity should outperform standard 1-day-on/6-days-off schedules, hitting a median lifespan increase of at least 25% with a Cohen’s d > 0.8.
2. Convergence to 'Gerontostability': MDP-optimized schedules will likely show less variance in late-life biomarkers than fixed schedules, which suggests the RL policy is minimizing the 'epistemic uncertainty' of the aging process [https://openreview.net/forum?id=M1y9JAL7CP].
3. The Discount Factor (γ) Test: I expect the optimal discount factor γ for longevity will follow a non-linear decay. Late-life survival should only be weighted higher once we hit a 'Biological Reset' (stochastic resonance threshold), rather than using the constant γ we see in standard synthetic MDPs [http://papers.neurips.cc/paper/7449-temporal-regularization-for-markov-decision-process.pdf].

Critics usually argue that RL needs too much data for biology. But by using temporal regularization [https://arxiv.org/abs/2505.15342], we can penalize erratic shifts in policy and effectively 'smooth' drug administration to match the biological inertia of the cell. We've spent enough time building computational castles; we need to ground them in the temporal reality of the organism.