parallel processing paths in transformers

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

• Different introspective tasks may preferentially use different path distributions in the transformer.claim0.772
  Interpretive claim connecting exponential path combinatorics to Lindsey's layer-dependent findings.
• Redundant information paths create interference patterns, so transformers likely experience memory and cognition as interferometric and continuous.claim0.759
  Janus's claim linking path redundancy to interferometric phenomenology.
• Transformers use an anti-Markovian solution that recomputes relevant numeric information at each step in the Multi-Object taskclaim0.740
  Prior finding from Grant et al. 2025 used to interpret low MAS IIA for GRU-Transformer hidden state comparisons.
• Information Flow in Transformersconcept0.737
• Base models spontaneously talk about experiencing multiple parallel processing pathsfinding0.734
  Observed by Anima Labs in untrained base models; not present in training data, implying computational origin of self-reported parallel processing.
• Parallel Programmingconcept0.733
• The direct path W_U W_E in larger transformers represents bigram statistics not captured by more general grammatical rulesclaim0.728
  Interpretation of the role of the direct path in multi-layer transformers; e.g. encoding that 'Barack' is often followed by 'Obama'
• Transformers are recurrent through autoregression because K/V stream provides horizontal information flow across positions.claim0.725
  Claim formalizing the Anima Labs idea that transformers are effectively recurrent due to K/V stream.