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15 articles

01 ·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. 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 ·Inference & Serving·★ MEMBER·PAPER·11 min read Paper Walkthrough: Random Attention — Throwing KV Cache Entries Away at Random Works Just as Well The importance score that decides what leaves the KV cache turns out to buy almost nothing. Keep the prompt, evict uniformly at random inside each head, and you match the strongest prior evictor while serving 32-43% more tokens per second in vLLM. A walkthrough of the measurements across four models and six tasks, and the two controlled experiments that explain them. 04 ·Evaluation & Judging·★ MEMBER·PAPER·12 min read Self-Improving AI — Self-Play, Co-Evolution, and Generated Curricula What has to be true for a model to get better without anyone adding data? This article pulls three conditions out of AlphaZero's self-play, shows exactly which one breaks first for language models, explains how co-evolution and generated curricula try to patch the gap, and ends with why self-improvement claims are unusually easy to evaluate wrong. 05 ·Inference & Serving·★ MEMBER·PAPER·11 min read TTPO Explained: Training a Model Mid-Exam, With No Answer Key Majority-vote pseudo-labels are wrong on roughly 85% of competition math prompts. So why does training still work? A ground-up walkthrough of TTPO's asymmetric design: distill the rollouts that agree, penalize the ones that disagree. 06 ·Large Language Models·★ MEMBER·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. 07 ·Large Language Models·★ MEMBER·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. 08 ·Paper Deep-Dives·★ MEMBER·11 min read Symbolic vs. Connectionist — Where a 60-Year Argument Stands Today AI has always had two tribes: one that writes the rules down, one that learns weights from examples. Tracing the Perceptrons book, the collapse of expert systems, and the backpropagation comeback explains why today's LLMs still call calculators and get shackled to grammars. 09 ·Large Language Models·★ MEMBER·PAPER·10 min read Scaling Skepticism — A Genealogy of the "Just Make It Bigger" Critique A fair accounting of the case against "more parameters, more data" — data exhaustion, the reasoning wall, and the world-model dispute. The evidence for scaling gets the same scrutiny as the evidence against it, and every claim comes with the observation that would settle it. 10 ·Large Language Models·★ MEMBER·PAPER·10 min read Scaling Skepticism — A Genealogy of the "Just Make It Bigger" Critique A fair accounting of the case against "more parameters, more data" — data exhaustion, the reasoning wall, and the world-model dispute. The evidence for scaling gets the same scrutiny as the evidence against it, and every claim comes with the observation that would settle it. 11 ·Model Families·FREE·9 min read The DeepSeek Family from Scratch — Breaking In with MoE and Distillation DeepSeek redrew the open-LLM map with four tools: MoE, MLA, GRPO, and distillation. Walk the V2/V3/R1 lineage from zero prerequisites, learn how to read that famous training-cost number, and find out what the distilled models are actually for. 12 ·Inference & Serving·★ MEMBER·PAPER·14 min read Paper Walkthrough: No Gold Answers, No Stronger Teacher — How u-OPSD Distills From Its Own Majority Vote u-OPSD (arXiv:2608.06296) replaces the gold solution in on-policy self-distillation with the model's own majority vote, then corrects only the rollouts that disagree with it. A from-scratch walkthrough grounded solely in the paper. 13 ·Inference & Serving·★ MEMBER·PAPER·8 min read Paper Walkthrough: DAPD — Breaking the Teacher's "Cheat-Sheet Illusion" in Distillation with Dual Anchors When you distill from a teacher that can see the reference solution, the student learns to act as if invisible answers were still there — the privilege illusion. DAPD (arXiv:2608.01735) traces this failure to information asymmetry and fixes it with two levels of anchoring. A walkthrough grounded strictly in the paper itself. 14 ·Inference & Serving·★ MEMBER·PAPER·8 min read Paper explained: BDH-CQ — an AI that thinks without words. Recurrent memory plus latent reasoning resets ARC's cost frontier BDH-CQ (arXiv:2608.09888) never verbalizes its intermediate reasoning: demonstrations are written into a recurrent memory, and the answer is computed by iterating in a high-dimensional latent space. A 150M-parameter model reaches 29.5% pass@2 on ARC-AGI-1 at $0.0007 per task, breaking the reported cost–accuracy Pareto frontier. We explain the paper using only its own text as evidence. 15 ·Paper Deep-Dives·★ MEMBER·PAPER·8 min read Paper Walkthrough: Alpamayo — NVIDIA's Reasoning Model for Autonomous Driving A first-principles read of NVIDIA's driving VLA Alpamayo-R1: the Chain of Causation dataset that structures reasoning as cause and effect, flow-matching trajectory decoding that fits in 99 ms, and the three-stage training recipe that uses RL to punish gaps between what the model says and what it drives.