• inference — Running a trained model to get answers — the phase where AI is actually used, as opposed to trained.

A principled alternative to Best-of-N for raising output quality at inference time — steer toward a reshaped model distribution with no reward model or external supervision, and better scaling behavior than MCMC baselines.

• articleA Year in LLM Serving: Workload Evolution, Caching and Load-BalancingWilliam Nixon, Jon Durbin, Florian Standhartinger, Haryadi S. Gunawi, Juncheng Yang
• articleRENDER: Controlling Reader-Facing Evidence in LLM Memory EvaluationYuan Si, Simeng Han, Daming Li, Jialu Zhang
• articleCompliance, Capability, and Conflict: Benchmarking Multimodal LLMs under System MessagesJuan Yeo, Geewook Kim

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