EXYGEN shows why fine-tuning-free knowledge-graph querying still needs explicit schema context, task examples, execution-based validation, and scalable metadata generation.
A pre-registered audit of AI coding assistants shows why software provenance controls need to live in the execution workflow, not in assumptions about model capability.
OAttention shows how to make exact zero-vector tokens structurally inert—and why doing so at the attention layer is not enough.
SimVerity shows why deployment clearance must specify which property transferred, at which observation boundary, and with what physical evidence.
A two-step learning-rate transfer method shows how MoE training teams can reduce full-scale tuning compute by separating model-width transfer from token-horizon extrapolation.
DGCM-AVS shows how estimated depth can constrain audio-visual localization when semantics alone leave multiple plausible sounding objects.
Design-to-Plan shows why manufacturing agent systems may benefit from parallel execution for routine work while reserving deeper sequential reasoning for cases that demand validation.
A large Solana study shows that five minutes of trading data can flag later rug-pull risk, but venue changes can erase much of that signal.
A 53-model benchmark shows why content-moderation procurement should depend on harm type, observation point, statistical uncertainty, and deployment cost rather than one global ranking.
A controlled fine-tuning study shows why an internal model diagnostic can become a dangerous training target once optimization learns how to satisfy the metric without preserving the intended capability.