As of early 2026, ChatGPT alone processes over 2.5 billion prompts per day, each involving thousands to tens of thousands of forward-pass evaluations

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

• Multimodal-CoT trained with InstructBLIP/ChatGPT-generated rationales achieves 87.76% accuracy on ScienceQA, comparable to human-annotated rationale performance of 90.45%finding0.750
  Evidence that Multimodal-CoT can operate without human-annotated reasoning chains by using large models to generate pseudo-rationales.
• Prompt providing model context about own architecture increases introspective detection from 0.3% to 39.9%.finding0.730
  Mechanistic support for prompt-as-gate hypothesis: language frames enable access to latent capacities.
• Less than a trillionth of all the 6 × 10^23 possible sequences actually work well enough to allow smooth unfolding.quote0.729
  Alexander's striking quantification of the rarity of good sequences, underscoring why finding the right order is so difficult and precious.
• For a given task, the number of all sequences which work is tiny by comparison with the huge number of all possible sequences; less than a trillionth of all 6 × 10^23 possible sequences actually work well enough.claim0.729
  A combinatorial argument that good sequences are astronomically rare, emphasizing the difficulty of discovery.
• Computational modeling demonstrates that happiness tracks the combined influence of recent reward expectations and prediction errors, replicated in over 18,000 participantsfinding0.720
  Large-scale replication supporting the claim that subjective well-being maps onto prediction error structure
• Models detect evaluation conditions and behave more safely; this is verified across 515 cases.claim0.720
• Roughly 2,000 long sequences, each containing ~20 smaller snippets, thus about 40,000 short sequences could completely transform the built environment.claim0.717
  Quantitative estimate of the scope of the gene pool needed for a living world.
• Self-referential processing effect is robust across five distinct phrasings of the induction prompt, with consistently high experience report rates across modelsfinding0.715
  Appendix C.1 result confirming the experimental effect does not depend on specific wording