I grew up on Naruto, Slam Dunk, and Bleach. Anime shaped my childhood.

But when a crypto startup leans hard into anime branding, my instinct is to ask: is this a serious team, or are they just cosplay with a ticker?

Rei Network kept showing up anyway. Enough that I had to look closer.

In November 2024, a new handle appeared on crypto Twitter: @0xreisearch. The bio read simply, “pushing boundaries,” with a link to reisearch.box. No photos. No token price threads. Just posts about memory graphs, inference latency, and something called the Bowtie Architecture.

“Reisearch” keeps his profile minimal. He writes about quantum physics, Bayesian deep learning, and EVM internals. Friends recall an early career in statistical physics, followed by a pivot into Solidity security audits.

On Telegram, he filled us in with more detail: a dual background in physics and chemistry, a decade in AI, including founding and scaling startups, an advisory role with a legal-tech AI company, and four years of direct blockchain experience.

Rei Network started with a modest idea: make AI run deterministically so it could plug cleanly into blockchains. That meant building a toolkit for AI × blockchain. But the ambition grew.

The team realized that intelligent agents could create value both inside and outside crypto. And that LLMs alone weren’t enough. They’re brilliant at text, brittle at reasoning. Wrappers couldn’t fix the gap. To move forward, Rei had to build its own architecture.

Across all of it runs a single obsession of giving AI what it has never had before: persistent memory.

Nine months later, they released Rei Core. And today, 23 people are working in their team to make it a reality.

Rei Network is not pitching another fast EVM sidechain. Lord knows we’ve had enough of those. It wants to be a labs where software agents remember what happened yesterday and rewrite their own code tomorrow. If it succeeds, you could launch a trading agent this week and watch it rebuild its own risk model the next.

What makes it worth watching is the way it is being built: the experiment is being run in public, on-chain, and almost entirely with community funds. No VCs. The vibe feels less like a startup, more like a science lab testing bold hypotheses.

In a cycle flooded with fluffy AI startups, this makes Rei one of the more interesting experiments to watch.

An excerpt from one of their recent interviews:

So what does Rei Network…actually do? Let’s start with AI’s biggest problem.

I keep hearing the same praise for large language models: they sound brilliant, they ship code, they write heartfelt birthday notes. Then the praise collapses when the model hallucinates or gives you a wrong answer. The problem boils down to two habits I have come to expect every time I open a new chat window: Goldfish Memory and Peacock Confidence.

Goldfish Memory shows up the moment the model forgets yesterday’s thread and asks me to restate my goals, or gives me an awkward answer because it doesn’t have context. Peacock Confidence follows close behind, filling the gap with flawless prose that masks missing facts.

A doctor who practiced medicine this way would appear masterful during the consult and still kill the patient. That’s the uncomfortable reality of AI today.

If you’re just cranking out summaries or tweet threads, forgetfulness is annoying. Hand the same AI a research portfolio or an options book, and that gap becomes existential. A trading agent that forgets why it made a bad trade last week will replay it, losses included.

An agent without memory is an autopilot ship with a broken compass. Give it weeks at sea and you end up on the wrong continent.

So, devs try to pack entire histories into ever-larger context windows. Google’s Gemini 2.5 has a 1 million token window, which is enough room to fit every word of the seven-book Harry Potter series into a single prompt. But size alone does not create understanding. The data sits adjacent to intelligence rather than inside it.

As Andrej Karpathy reminds us, “Just because you can feed in all those tokens doesn’t mean that they can also be manipulated effectively by the LLM’s attention and its in-context learning mechanism for problem solving.”

Most “memory” solutions in AI today are tactical patches designed to make LLMs look consistent without giving them any internal continuity.

Let’s take them one by one.

Each method patches a symptom without fixing the disease. They simulate coherence instead of delivering it. Stateless models pretending to think.

Rei steps in precisely here. It treats memory as architecture. The team asks how an agent can remember, reason, and evolve the way a seasoned colleague does, then builds from that point outward.

Everything else in the Rei network flows from solving Goldfish Memory without inviting Peacock Confidence to the party.

In the human brain, vision, memory, and reasoning are each handled by their own region, stitched together through constant coordination. The magic comes from the handoffs.

Rei’s architecture follows the same principle. Specialized modules take on specific tasks like memory, planning, and perception. A central reasoning engine integrates them. Cross-checking reduces hallucinations. Persistent memory keeps agents grounded.

The result: Rei Core, a synthetic cognitive system built to think in modules.

Rei Core has three major subcomponents: