Paper Deep-Dives
Landmark papers one by one, grounded in the source text
01
·Paper Deep-Dives·★ MEMBER·PAPER·12 min read
Paper Deep Dive — LoRA: Low-Rank Adaptation of Large Language Models: Why Low Rank Is Enough
A re-reading of the LoRA paper (Hu et al., 2021) grounded strictly in its own text: what BA actually means, the 21x amplification factor the authors measured, why r=1 sufficed, and the questions they left open.
02
·Paper Deep-Dives·FREE·13 min read
When Proxies Stop Being Good Enough — Reading August 2026's Eight Autonomous Driving Papers Together
A cross-cutting read of eight autonomous-driving arXiv papers from late August 2026. Three groups independently stop measuring safety in expectation, two add an observation channel outside the ego vehicle's own history, and two genuinely don't fit the story.
03
·Paper Deep-Dives·★ MEMBER·PAPER·12 min read
Paper Deep Dive — Attention Is All You Need: What Dropping Recurrence Actually Proved
A close reading of the Transformer paper grounded strictly in its own text: the scaled dot-product equation, why the square root of d_k is there, what the ablations exposed, and the limits the authors themselves flagged.
04
·Paper Deep-Dives·★ MEMBER·PAPER·9 min read
Mixture of Experts (MoE) from Scratch — Routing and Load Balancing in the Switch Transformer
A ground-up explanation of Mixture of Experts, the sparse architecture behind today's largest LLMs, built strictly from the Switch Transformer paper: the router math, expert capacity, the load-balancing loss, and the three tricks that make sparse training stable.
05
·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.
06
·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.
07
·Paper Deep-Dives·★ MEMBER·PAPER·11 min read
Paper Walkthrough: WorldClaw — Agents That Build Walkable, Editable 3D Open Worlds from a Single Sentence
Type 'a tropical pirate stronghold' and get a large-scale 3D world you can walk through — with every building still an editable, standalone asset. A walkthrough of Tencent Hunyuan's WorldClaw, from its plan → terrain → regional-objects pipeline to the render-and-inspect loops where agents check their own work.
08
·Paper Deep-Dives·★ MEMBER·PAPER·8 min read
Paper Deep Dive: AskChem — Changing the Unit of Search from Papers to Provenance-Carrying Claims
An NYU-led systems paper that swaps the retrieval unit of chemistry literature search from papers to claims grounded by a DOI and a verbatim quote. We walk through the 2.4M-claim index, evidence graph, and hybrid search design, the evaluation that took DOI resolution from 88.3% to 100%, and the paper's honest limitations.
09
·Paper Deep-Dives·★ MEMBER·PAPER·13 min read
Paper Deep Dive — Large Discovery Models: giving an LLM a value signal for what to try next
An LLM can propose candidates but cannot reliably grade its own proposals. Large Discovery Models tilts the LLM's proposal distribution by an acquisition value drawn from a Gaussian process surrogate, so search keeps working when every evaluation is expensive. From the equations to the reported numbers.
10
·Paper Deep-Dives·★ MEMBER·PAPER·11 min read
Paper walkthrough: Apodex 1.1 — scaling agents around completed work
Not a bigger model and not more thinking time — Apodex 1.1 scales two other surfaces: the environments an agent learns in, and the way work is organised across agents. A walkthrough from the task contract to the AgentOS delivery gate, the numbers, and the limits.
11
·Paper Deep-Dives·★ MEMBER·PAPER·13 min read
Paper Walkthrough: Turning Game Development into a Verifiable Trajectory Data Engine — RLHEV and AWoMo
A paper arguing that what world models lack is not data or compute but cheap grading. We work through RLHEV — post-training on game-engine checks fused with developer accept/reject decisions — from first principles to equations, interactive figures, and the reported numbers.
12
·Paper Deep-Dives·★ MEMBER·PAPER·12 min read
Paper Walkthrough — J-Zero: Growing the Challenger, the Solver, and the Judge Together from Zero Data
A model that writes its own problems, solves them, and grades them — with no external data and no human labels. J-Zero's twist is that the grader learns too, which is what lets it keep improving past the two-iteration wall where prior methods stall.
13
·Paper Deep-Dives·★ MEMBER·PAPER·11 min read
Paper Walkthrough — RoboTok: Mining the Web for Demonstrations That Move Like Yours
A ground-up walkthrough of RoboTok (arXiv:2609.03199), a data engine that pulls manipulation-relevant human demonstrations out of web video. Covers how DTW defines 'similar motion', how that ranking is distilled into an embedding for inner-product search, and what the results do and don't show.