LLMs implicitly learn a distribution of 'consistent reasoning paths', and inconsistent reasoning forms statistical outliers with low probability under this distribution.

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

• LLMs linearly represent truth-relevant information beyond the plausibility of text, as evidenced by probes trained on likely performing poorly on anti-correlated datasetsclaim0.800
  Establishes that the observed linear structure is not merely a representation of text probability
• Conditional logic already suffices where LLMs still fail, as code agents avoid systematic failuresclaim0.797
  contrast between rule-based and LLM reasoning
• Linear truth directions in LLMs are reliable primarily in factual recall cases and break down when truth assessment depends on computing and storing intermediate results.claim0.795
  Central empirical conclusion of the paper about the fundamental limits of truth directions.
• Connecting the Dots: LLMs Can Infer and Verbalize Latent Structure from Disparate Training Data (Treutlein et al. 2024)concept0.790
  Out-of-context reasoning work directly related to synthetic document fine-tuning experiments
• We hypothesize that LLMs represent correctness of arithmetic expressions differently from factual statements.hypothesis0.790
  Core working hypothesis motivating the factual vs. arithmetic task split in the experimental design.
• Active inference LLMs extending prediction-focused language models with tighter perception-action feedback loops may naturally embody contemplative wisdom as they scalehypothesis0.789
  Predictive hypothesis about Contemplative Architecture approach based on Petersen et al. 2025 work
• LLMs trained only on language data have rich enough knowledge of visual structures that decent visual representations can be trained on images generated solely by querying the LLMfinding0.783
  Sharma et al. result supporting cross-modal alignment: language-only models implicitly encode visual structure
• LLMs sometimes know statements are false but generate them anyway, motivating the need for techniques that inspect internal model state rather than outputs aloneclaim0.781
  Motivating claim supported by the CAPTCHA example and Perez et al. (2022) findings