A paper on unified image generators shows that model identity can often be recovered from generated images, but not because the images carry an obvious watermark or reveal the prompt.
A mechanism-first reading of AlphaTransit, showing why learned search matters more than simply drawing wider bus routes.
IndoBias shows why Indonesian-language AI fairness cannot be inferred from general benchmarks, model fluency, or local adaptation alone.
A mechanism-first analysis of how SOM turns critic gradients into one-step generative policies for online reinforcement learning.
A business-focused analysis of why agent guardrails need to revise unsafe plans, not merely block them.
NutriMLLM shows that comprehensive nutrition AI needs specialised supervision, not just bigger vision-language models.
Why inference-time adaptation is becoming both a performance lever and a governance risk.
Trajectory-Refined Distillation shows why repairing failed reasoning paths may matter more than tuning token-level distillation losses.
A business-focused reading of two arXiv papers showing why AI systems must preserve relational state, not merely produce plausible outputs.
A business-focused reading of a hybrid WGAN-GA graph generation paper, showing why synthetic network data often needs discrete topology repair after broad generative modelling.