Reasoning

TL;DR for operators Self-Questioning Language Models, or SQLM, tests a tempting idea: can a language model improve its reasoning ability without being handed a curated training set of questions and answers? The answer in this paper is: partly, in narrow settings, if the training loop is engineered carefully enough.1 The mechanism is not mystical self-awareness. A model is split into two roles. One role proposes questions from a single topic prompt. The other tries to solve them. Reinforcement learning then updates the system using proxy rewards: majority-vote agreement for arithmetic and algebra, and proposer-generated unit tests for coding. The proposer is rewarded for problems that are not too easy and not too hard; the solver is rewarded for answers that pass the available proxy. ...

TL;DR for operators Finance teams do not ask AI systems to do one kind of thinking. They ask them to imagine plausible futures, extract investable implications, choose between similar explanations, and avoid being seduced by the prettiest narrative. Those are not the same task. A model can be fluent, plausible, and still strategically dull. Finance has a long tradition of rewarding that, but we do not need to automate the habit. ...

TL;DR for operators Static RAG is still useful. It is also no longer the whole game. The paper behind this article argues that retrieval and reasoning are converging into a more tightly coupled architecture: reasoning can improve retrieval, retrieval can improve reasoning, and agentic systems can interleave both over multiple steps.1 That sounds like a neat academic symmetry until you put it inside an enterprise workflow, where every extra retrieval call means latency, cost, permissions, ranking risk, and one more place for the machine to confidently ingest rubbish. ...

TL;DR for operators If your agent stack is becoming expensive because every “reflection” step means another model call, this paper is worth reading. Its proposal, Introspection of Thought (INoT), tries to compress an external multi-agent debate loop into one structured prompt. The LLM is not literally running multiple agents. It is being instructed, through a hybrid Python-and-natural-language prompt called PromptCode, to simulate two internal debaters that reason, critique, rebut, revise, and then return an answer.1 ...

TL;DR for operators SPIRAL is not interesting because it teaches language models to play TicTacToe, Kuhn Poker, and negotiation games. That would be charming, but not exactly a boardroom emergency. Its real contribution is showing that adaptive competitive pressure can train reasoning behaviours that transfer beyond the game environment.1 The paper’s central lesson is mechanism-first: self-play creates a moving curriculum. The model does not merely imitate expert trajectories or exploit a fixed opponent. It faces a continuously improving version of itself, so yesterday’s shortcut becomes today’s liability. That pressure appears to produce reusable reasoning patterns: case-by-case analysis, expected value calculation, and pattern recognition. ...

TL;DR for operators Most AI evaluations still ask the wrongly narrow question: did the model get the answer right? That is useful, but it is not enough when the model is expected to act as an agent, revise plans, obey constraints, and recover from failure without turning the workflow into a procedural bonfire. ...

TL;DR for operators Adding “reasoning” to an LLM agent is not the same as making it reason better. Wong et al. test four open-source models across dynamic SmartPlay tasks using a baseline prompt, reflection, reflection plus an Oracle that mutates heuristics, and reflection plus a Planner that simulates short future trajectories.1 The clean result is not “planning wins” or “bigger models win.” The result is more annoying, therefore more useful: the same scaffold can be a booster, a distraction, or a failure amplifier. ...

An open-source reasoning model achieving state-of-the-art performance in math, code, and logic tasks.

A compact, high-quality small language model from Microsoft designed for strong reasoning, on-device inference, and low-latency applications.