Kriegman et al. 2019 Automated Shapeshifting for Function Recovery in Damaged Robots

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• A soft robot recovers from unexpected physical injury faster by contorting its body into a new shape (hardware change) rather than learning a compensating gait (software change).finding0.802
  Empirical inversion of the assumption that hardware changes are harder than software changes
• Robots capable of self-modeling can model their own body and unexpected damage using AI methods, with morphological and mental changes occurring in parallel.finding0.754
  Evidence for blurring of embodied robot / non-embodied AI distinction through self-modeling
• Bongard robot self-modeling: robot discovers its shape, then reuses information after damage.finding0.732
  Key Artificial Life experiment illustrating remapping of self-model (Bongard et al. 2006).
• Xenobots perform kinematic self-replication by herding loose cells into new Xenobots.finding0.727
  From Kriegman et al. (2021), a novel mode of reproduction never before seen.
• Transformers are recurrent through autoregression because K/V stream provides horizontal information flow across positions.claim0.716
  Claim formalizing the Anima Labs idea that transformers are effectively recurrent due to K/V stream.
• AI Control: Improving Safety Despite Intentional Subversion (Greenblatt et al. 2024)concept0.715
  Related work studying capability of LLMs to subvert safety measures if severely misaligned
• Optimizing interventions in activation space to produce paths along M_y recovers activation trajectories that trace the curvature of M_h.finding0.709
  Demonstrates bidirectional causal link: behavior manifold geometry can be recovered by optimizing in representation space.
• Is it possible for a piecemeal process to transform the existing fragmented system gradually towards a morphogenetic process?question0.706
  Key question raising doubt about whether incremental improvement can overcome the integrated anti-living system.