Would experienced meditators rank model responses differently from LLM scorers?

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

• LLMs can predict their own responses more accurately than external observers, implying privileged internal knowledgefinding0.782
  Binder et al. finding cited as evidence that LLMs possess introspective capacity analogous to mindfulness
• The better an LLM is at language modeling, the more it aligns with vision models, and vice versa — linear relationship between language modeling score and vision-language alignmentfinding0.757
  Core cross-modal empirical result: larger and better language models align better with vision models
• Standardized LLM self-assessments reflect learned communication postures rather than genuine capabilities (Jackson et al. 2025)claim0.755
  Skeptical prior work motivating validation framework
• An LLM is a far more limited entity than a buddha, yet it can convincingly play buddha-like beings.claim0.755
  Comparison between Buddhist ideals and AI capabilities.
• Toxic LLMs show higher IIA when compared to other toxic models than when compared to nontoxic models using stepwise MASfinding0.754
  Proof-of-principle that MAS can detect model misalignment in DeepSeek-R1-Qwen-1.5B fine-tuned models.
• LLMs demonstrate stronger persona fidelity for clearly defined and socially desirable high-level personas than for neutral or low-level personasclaim0.748
  Observed across multiple models and tasks; attributed to RLHF training preference for helpful/harmless/honest responses
• Instruction-tuned models consistently outperform base models on all atomic-level persona fidelity scores across 12 LLMsfinding0.748
  Comprehensive model comparison showing tuning benefit for persona fidelity
• The consistency with which LMs exhibit character traits varies with model size, fine-tuning, and prompting.claim0.747
  Finding replicated across multiple experiments.