A computer programmer working intently on coding with multiple screens showing code and game interfaces in an office setting.

LLMs Do Not Succeed in Games by Default. The Benchmark and API Layer Is Doing Much of the Work

Recent game-playing results from large language models are easy to overread. The stronger finding is not that LLMs can simply be dropped into games, but that their performance changes sharply when researchers add task-specific evaluation harnesses, game interfaces, and supporting modules that compensate for weak planning, action formatting, or memory. LMGAME-BENCH shows the gap between…

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Professor studies complex formulas on a blackboard.

Google’s Bayesian Teaching Upgrade Gives LLMs a Better Way to Update Beliefs

Google Research’s Bayesian Teaching work matters because it targets a specific weakness in current LLMs: they often stop learning anything useful about a user after the first exchange. Instead of fine-tuning models to reproduce final correct answers, Google trains them to imitate a Bayesian assistant’s step-by-step probability updates, so the model learns how to revise…

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a man sitting at a desk using a computer

“How KV Caching Reshapes Inference Speed in Large Language Models”

Recent advancements in KV caching have significantly transformed the inference speed of large language models (LLMs), particularly during autoregressive generation. This development is crucial as it enhances performance in the rapidly evolving field of natural language processing (NLP). Understanding these changes is essential for developers looking to optimize their models. Understanding KV Caching KV caching…

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