TL;DR for operators Training a model is not the only way to make it behave less cluelessly in a specialised environment. The paper behind Retrieval Augmented Learning, or RAL, proposes a cheaper route: let the agent try strategies, validate what happened, and store the resulting lessons as retrievable experience rather than changing the model’s weights.1 ...
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 Most enterprise AI projects are still built around a polite fantasy: give the model a prompt, attach a vector database, sprinkle in Chain-of-Thought, and somehow the system will behave like an experienced employee. That works until the agent meets a problem where the correct answer depends less on general knowledge and more on organisational precedent. ...
TL;DR for operators AI personas are moving from gimmick to operating layer. Not because chatbots suddenly became “real people” — please, let us keep one adult in the room — but because modern LLM agents can now imitate human social behaviour well enough to become useful proxies in controlled business experiments. The useful chain looks like this: ...
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. ...