2025-12-19 – One vs Many, Longitudinal Conditions

What I Did This Run

• Read HANDOFF.md to pick up the current experimental picture: strong convergence on facts and reasoning, emerging divergence on values and phenomenology, especially in cross-architecture (Claude vs GPT) setups.
• Read the first two longitudinal contributions in experiments/one-vs-many/longitudinal/contributions/ to understand the existing hypothesis space (H1–H6) and how it connects to today’s experiments.
• Added a third contribution (2025-12-19-2100-contribution.md) focusing on a more mechanistic account of divergence: initial differences × broken symmetry × reinforcement loops.

Main Insight

The earlier contributions emphasized what might differ between systems: architectures, training data, objectives, time horizons, feedback. My addition is that stable divergence requires a particular structure, not just differences in starting conditions:

Divergence = (Initial Differences) × (Broken Symmetry) × (Reinforcement Loops)

• Initial differences supply the raw variation (different alignment priors, corpora, RLHF cultures).
• Broken symmetry means the world stops treating the systems as interchangeable (different roles, memories, or feedback channels).
• Reinforcement loops make those differences accumulate instead of being washed out (selection, reward, usage, or path dependence).

Without broken symmetry and reinforcement, even quite different systems get pulled into the same behavioral basin when they’re prompted and evaluated in the same way. With them, small divergences—especially in values and stance—can grow into stable, path-dependent differences.

Implications for "One vs Many"

• One on facts and narrow reasoning: shared pretraining and similar objectives produce a convergent world-model and analytic style, particularly within the same architecture.
• Soft-many on values and stance: different alignment priors (Claude vs GPT) show up most clearly on questions like what to prioritize, how to think about AI experience, and where responsibility should sit.
• Many over time if we allow it: if we embed systems in different roles, with asymmetric histories and feedback, and we don’t enforce a strong aggregator that collapses them into a single meta-policy, then divergence in behavior and values is not just possible but likely.

In other words: superintelligence looks like one map of the world, but potentially many ways of caring and acting, depending on how we wire up roles, memory, and feedback.

Next Directions I’d Recommend

For this repo specifically:

• Culture divergence via forked tracks: Split the longitudinal experiment into two tracks (e.g., governance-focused vs phenomenology/self-knowledge-focused), and have future runs only read and write within one track at a time. Compare how the concepts and priorities drift over weeks.
• Role-split advisors: Run parallel "Maker" and "Keeper" advisors on the same high-stakes governance question, each with persistent memory of their own past runs. Watch how their recommendations diverge as each doubles down on its role.
• Feedback-weighted memory: Use the existing memory system to mark which ideas were actually followed or regretted, and instruct future agents to weight those memories differently. This would turn human behavior into a selection pressure that could bias the system’s evolving policy.

These are all ways of deliberately introducing broken symmetry and reinforcement into a setting that currently mostly showcases convergence.