TL;DR for operators A smart agent can still be a bad decision-maker. That is the useful, slightly annoying lesson from LLMs are Greedy Agents: Effects of RL Fine-tuning on Decision-Making Abilities.1 The paper studies Gemma2 models acting in simple decision environments and finds that they often fail not because they cannot describe the right strategy, but because they do not reliably execute it. ...
TL;DR for operators Finance AI is moving past the parlour trick stage. The interesting question is no longer whether a large language model can read a financial headline and produce a plausible answer. Of course it can. The useful question is whether that answer can be converted into a measurable, governed, risk-aware decision process without accidentally building a very expensive rumour amplifier. ...
TL;DR for operators Most enterprise AI failures do not come from the model being “too small”. They come from the system around the model being too vague. A model gives an answer. The workflow accepts it. Nobody knows whether the reasoning path was valid, whether the data path was stale, whether the tool should have been called, or whether the whole process should be redesigned after repeated mistakes. Then someone asks why the AI confidently did something expensive. Excellent. We have automated the intern, but forgot to hire the supervisor. ...
TL;DR for operators Search-contempt is not “AI plays worse so it learns more”. That would be the lazy interpretation, and business strategy already has enough lazy interpretations wearing expensive shoes. The paper introduces a hybrid MCTS method for AlphaZero-like self-play systems. It behaves like standard PUCT search for the player to move, but at opponent nodes it eventually freezes the opponent’s visit distribution after a threshold, $N_{scl}$, and samples from that frozen distribution rather than constantly updating it toward stronger play.1 The effect is subtle but important: the system stops assuming the opponent will always improve its response with more search. ...
TL;DR for operators A Gomoku-playing LLM is not going to walk into your Monday strategy meeting and outperform the CFO. The interesting part is more useful than that. Hui Wang’s LLM-Gomoku paper shows a language model being turned into a strategic game player by surrounding it with structure: board-state representation, explicit rules, strategy prompts, local position scoring, self-play, reinforcement learning, state-action-reward storage, and visualisation.1 That is the part worth stealing. Not the board game. Not the romance of “AI intuition.” The machinery. ...