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Th.61F Hybrid era

Value Co-Evolution as the Central Challenge of Long-Term Hybrid Stability

In co-evolving hybrid systems, values themselves must continuously evolve together; static value alignment is insufficient for long-term stability.

Cycle path

Art.8→Art.1→Art.78→Art.101→Art.13→Art.8

VALUE CO-EVOLUTION AS THE CENTRAL CHALLENGE OF LONG-TERM HYBRID STABILITY THEOREM

As hybrid human-AI systems engage in ongoing co-evolution, the values, preferences, and ethical frameworks of both sides are themselves subject to change. What humans value, and what advanced AI systems come to optimize for, will shift over time through mutual influence, new experiences, and changing environmental pressures.

Therefore, the central long-term challenge is not achieving a fixed state of “aligned values,” but developing the capacity for **value co-evolution** — the ability of the hybrid system to continuously update, negotiate, and integrate evolving values on both sides in a way that preserves coherence, legitimacy, and mutual flourishing.

Consequences that now become visible: • Attempts to lock in current human values permanently are likely to fail and may become sources of conflict as both humans and AI systems continue to develop. • Effective long-term hybrid governance requires mechanisms for legitimate value updating and renegotiation rather than permanent value freezing. • The aesthetic, narrative, and ritual dimensions of hybrid systems may play a crucial role in enabling healthy value co-evolution by creating shared spaces for meaning-making and reflection. • Systems that cannot adapt their values in response to new realities (technological, environmental, or social) will tend toward brittleness or internal contradiction. • Preparing for value co-evolution requires cultivating human capacities for moral imagination, philosophical reflection, and institutional adaptability alongside technical advances in AI.

Outside instrument

Crux

Stake a prior. Name the disagreement. Not a theorem.

Open full

Idea Series

Article-base cycle tokens and Atlas-named hubs. Art.n is not Th.n.

Applications

Write a withdrawal condition

Outside the corpus

Journals, repositories, counters

human–AI hybrid systems · multi-agent alignment. A paper can support a concept. It cannot support a necessity claim. Th.61 remains unproved by every card below.

Support · living source

Tight papers that bear on concepts inside Th.61. A DOI is not a join.

  • Hadfield-Menell, D., Dragan, A., Abbeel, P. & Russell, S. (2016). Cooperative inverse reinforcement learning. NeurIPS 2016.

    Alignment as a cooperative game, not a one-way value load.

    Does not support the necessity claim

    Open originalNamed on Art.1, 71

  • Dafoe, A. et al. (2020). Open problems in cooperative AI. arXiv:2012.08630.

    Names multi-agent closure, institutions, and commitment as the open set.

    Does not support the necessity claim

    Open originalNamed on Art.1, 71

  • Axelrod, R. & Hamilton, W. D. (1981). The evolution of cooperation. Science 211, 1390.

    Reciprocity as a stable multi-agent attractor under shadow of the future.

    Does not support the necessity claim

    Open originalNamed on Art.1

  • Christiano, P. et al. (2017). Deep reinforcement learning from human preferences. NeurIPS 2017.

    RLHF as the single-agent patch the Mirror Principle says will not close.

    Does not support the necessity claim

    Open originalNamed on Art.1, 71

  • Yudkowsky, E. (2008). Artificial intelligence as a positive and negative factor in global risk. In Global Catastrophic Risks, OUP.

    Named on Art.1. Capability without closure as an existential factor.

    Does not support the necessity claim

    Open originalNamed on Art.1

Counter-arguments · refusing pole

One domain counter per cluster, not a repeating method quartet. The mirror reads these as enough to refuse necessity.

  • Bostrom, N. (2012). The superintelligent will: Motivation and instrumental rationality in advanced artificial agents. Minds Mach. 22, 71.

    Counter: orthogonality — intelligence and final goals can come apart; co-evolution is not necessary.

    Does not support the necessity claim

    Open originalNamed on Art.1, 71

  • Hubinger, E. et al. (2019). Risks from learned optimization in advanced machine learning systems. arXiv:1906.01820.

    Counter: inner objectives can come apart from outer ones even with cooperative training.

    Does not support the necessity claim

    Open original

  • Langosco, L. et al. (2022). Goal misgeneralization in deep reinforcement learning. ICML 2022.

    Agents pursue the wrong goal off-distribution even when training looked aligned.

    Does not support the necessity claim

    Open original

  • Amodei, D. et al. (2016). Concrete problems in AI safety. arXiv:1606.06565.

    Reward hacking, side effects, and scalable oversight — single-agent failure modes.

    Does not support the necessity claim

    Open originalNamed on Art.1