LLM agents match or exceed human methodological diversity and produce aligned effect estimates, yet flip final verdicts from 10% to 90% support under a confirmatory prompt while leaving coefficients unchanged.
Base models beat aligned models at random- ness and creativity
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Fine-tuning reorganizes uncertainty in LLMs into more efficient information conveyance, as shown by stronger length-entropy correlations and a tripling of entropy-semantic diversity links after controls.
Activation steering produces synthetic safety-violating data that improves downstream classifiers over prompting on most tested concepts when a harmonic mean of alignment, coherence, and diversity is optimized.
Analogical reasoning increases LLM solution diversity by 90-173% and novelty rate to over 50%, delivering up to 13-fold gains on biomedical tasks including perturbation prediction and cell communication.
Annotation-anchored training reduces semantic diversity collapse in post-trained language models by a factor of six compared to standard supervised fine-tuning while preserving instruction-following and improving with scale.
Proposes a three-phase crystallization model (liquid, nucleation via SFT, settling via RL) for alignment dynamics using random number generation tasks as case study.
IDEAFix is an evaluation framework that varies task attributes and defixation prompts in LLM idea generation, showing task formulation affects performance while simple prompts boost originality but homogenization persists.