If sigma is pruned, the agent gains access to QRF deployments and predictive strategies that were structurally excluded under dualistic fixation

Entities in the same semantic neighborhood but without a typed relation to this one — candidates for new edges or unrecognized duplicates.

• Once visible, the prior is eliminated via Bayesian model reduction resulting in a post-dual agent with unconstrained QRF deployments.claim0.791
  Formal description of the transition to post-duality.
• post-dual agent with unconstrained QRF deploymentsconcept0.767
  An agent that no longer imposes a fixed self-environment partition; its reference frame choices are free from dualistic constraints.
• Every dually sectorised QRF deployment is a samskara: an action that both expresses sigma and reinforces it through habit dynamicsclaim0.754
  Formal correspondence between the Buddhist dependent origination cycle and the self-reinforcing precision dynamics of sigma
• A naive agent equipped with reduced priors from an experienced agent performs perfectly with maximum confidence from the first trial.finding0.749
  Demonstration that model-level priors (not parameter-level knowledge) suffice for immediate transfer
• We formalise the belief in separation as a structural prior over an agent's quantum reference frame (QRF) deployments, constraining all measurement frames to respect a self/environment partition.claim0.745
  Formalisation of the dualistic belief.
• Single agents can generalize better to unseen tasks because they are not constrained by predefined heuristic-based workflows.claim0.740
  Architectural belief motivating single-agent design choice; suggests flexibility provides better out-of-distribution performance.
• Results may not fully generalize to all models and scenarios because the model organism relies on hints and nudges and Llama Nemotron cannot consistently distinguish evaluation/deployment based on subtle cuesclaim0.738
  Key limitation acknowledged by authors.
• Capability budget should be allocated to the task-solving agent rather than the evolver, since harness-updating varies by at most 3.1 pp across evolversclaim0.729
  Primary design recommendation derived from harness-updating flatness finding