#chain-of-thought

5 articles

01 ·Time Series·★ MEMBER·PAPER·11 min read Paper Explained: WearableQA — Testing Whether LLMs Can Reason Over 500 Days of a Real User's Wearable Data 4,084 ten-option questions built from 200 real users' daily wearable records (up to 500 days each) plus blood panels. Separating 'answer from knowledge' from 'read it off the numbers' splits 14 models between 19.6% and 72.9% accuracy, and simply giving the model Python adds 20 points. A ground-up walkthrough of how the questions are built and what the results actually say. 02 ·FREE·PAPER·9 min read Chain-of-Thought from Scratch — What Changes When You Make a Model Show Its Work Why does adding "think step by step" make a model solve problems it otherwise fails? A ground-up tour of few-shot CoT, zero-shot CoT and self-consistency — the serialization argument for why it works, the conditions where it doesn't, and how it relates to today's reasoning models. 03 ·Large Language Models·FREE·PAPER·13 min read Test-Time Scaling — How Models Get Better by Thinking Longer The same model scores higher when you let it think longer. This article builds the idea from scratch: chain-of-thought as purchased compute steps, self-consistency by majority vote, verifiers that pick the winner, and o1-style models that learned the thinking itself — and what it means for compute to shift from training to inference. 04 ·FREE·PAPER·9 min read Paper explained: DEFT-RLVR — show a driving VLM the future trajectory too early and it fabricates its reasoning Let a teacher model peek at the ground-truth trajectory while writing chain-of-thought annotations for driving, and severe hallucinations double from 29% to 50% — the paper calls this trajectory anchoring bias. This article walks through AD-MCQ, which turns planning into a verifiable multiple-choice task, and DEFT-RLVR (arXiv:2608.01755), which reveals trajectories only after the model commits to a decision, using nothing but the paper itself as the source. 05 ·Large Language Models·FREE·PAPER·7 min read The Science of Prompt Engineering — What Is Proven and What Is Folklore Does telling a model to think step by step actually work? Working only from the Chain-of-Thought paper (Wei et al., 2022): what the experiments genuinely established — an ability that emerges only above a scale threshold — and where the line falls between that and the incantations everyone repeats.