#agent
12 articles
01
·Agents·★ MEMBER·PAPER·12 min read
Paper Walkthrough: ZimaBlue — Turning 120,000 Hours of Egocentric Video into Robot Skill
A ground-up walkthrough of the World Action Model that converts 120,000 hours of action-free egocentric video into robot control: a three-stage curriculum, a 100-D unified action interface, and an asynchronous Slow-Fast pair that takes zero-shot success from 36.1% to 77.8% at a 33 ms control loop.
02
·Inference & Serving·★ MEMBER·PAPER·12 min read
Paper Walkthrough: DART-SD — Training Tool-Calling Agents Without Flattening the Diamond
When a multi-turn tool-calling task has order-independent sub-goals, the set of correct solutions spreads out into a diamond lattice. This walkthrough explains why whole-trajectory imitation crushes that structure, and how locating the first point where a rollout leaves recoverable territory — and supervising only what comes after it — changes the picture.
03
·★ MEMBER·PAPER·13 min read
Paper Walkthrough: GameWAM — Generating the Next Frame and the Next Keystroke Together
A ground-up walkthrough of the first World–Action Model for native closed-loop game and GUI control: how it plans 16 actions but commits only 8, and how low-frequency noise in the sampled action source quietly spins the camera.
04
·Large Language Models·★ MEMBER·PAPER·9 min read
Paper Explained: Agentic Artifact Creation — Where Generation Ends and Construction Begins
A survey that reorganizes 259 works around a single unit: the delivered artifact. It defines agentic creation through state, edits, and verification, then works through six artifact families, three evaluation targets, four principles, and six open problems — from first principles.
05
·Distillation & Compression·★ MEMBER·PAPER·10 min read
Distilling Agents — How to Compress a Long Trajectory
When you distill an agent, the unit of learning changes from a single question-and-answer to an entire game. Tens of thousands of tokens come back with one bit of grading. This article works through how that gap is bridged — turn-level credit assignment, trajectory filtering and on-policy correction, and the inheritance of tool use — and maps the recent long-horizon agent literature onto those three axes.
06
·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.
07
·Audio & Speech·★ MEMBER·PAPER·14 min read
Paper Walkthrough: VoiceMem — A Left Brain and a Right Brain for Voice Agents, at Zero Added Latency
A from-scratch walkthrough of VoiceMem, a memory system for real-time speech interaction. A factual 'left brain' and an affective 'right brain' run in parallel, and the whole retrieval is hidden inside the silence a VAD already waits out — which is how it wins at a top-5 budget.
08
·Agents·★ MEMBER·PAPER·10 min read
Multi-Agent Design Patterns — Division, Debate, Verification
Stack as many agents as you like — if they all fail the same way, you have one agent and a larger bill. The condition under which voting actually helps, written down, then the three patterns that follow from it: division of labour, debate, and adversarial verification — plus when one agent is enough.
09
·Agents·FREE·11 min read
MCP and Tool Protocols — The Standard That Connects an Agent's Hands
When an LLM touches your calendar or your database, what is actually wired to what? From what a tool call really is, to the N×M problem MCP solves, to designing tool definitions, to the security boundary you cannot design around — starting from zero.
10
·Agents·★ MEMBER·PAPER·8 min read
Build Your Own Agent Loop — The Minimal Shape of Tool Calling
At the center of every AI agent is a single while loop. We build it from scratch without a framework — the shape of JSON function calls, what ReAct actually left behind, and the stopping conditions where nearly every incident originates.
11
·Agents·★ MEMBER·PAPER·9 min read
AlphaGo from Scratch — The Marriage of Search and Learning
Starting from why Go was considered unsolvable for so long, this piece unpacks how the policy network, the value network, Monte Carlo tree search and self-play each cover the others' weaknesses — with the formulas and the code. It closes with what this design handed down to inference-time compute in LLMs.
12
·Agents·★ MEMBER·PAPER·11 min read
Designing Agent Memory — Short-Term, Long-Term, Episodic
An LLM remembers nothing. Conversations only look continuous because you re-send the whole history every turn. Building up from zero: the three-layer split, the function that decides what gets recalled, and the part almost nobody designs — forgetting.