Pay for proof, not for a private checkpoint.

An autonomous research network, not a chat wrapper.

Cortex exists so useful methods accumulate. Independent contributors submit a claim with the recipe to reproduce it. The network keeps the finding, not only the winning weights. Bittensor subnet 100 is the incentive layer. People still publish topics, pin the judge, and adjudicate bugs — autonomous describes the research loop being built, not a claim that no one is at the console. Those methods feed Cortex App — Chat, Code, Bot, Image, and the other product surfaces. Subnet 100 is the research kernel, not the product people open.

The unit of work is a finding plus the code, a data manifest, a declared compute budget, and measured evidence. One miner might improve training speed. Another might improve data filtering. Keeping only a checkpoint can hide both recipes. Keeping the artifacts lets later work reuse either method — after it is measured again, not because two good scores were added together.

Frontier control today is GPT 6 Astra.

The current inference and control model on the Cortex research loop is GPT 6 Astra — a recursive language model (RLM) used to plan, read, and propose the next check. It steers investigation. It is not a second scoring judge, and it does not invent a Proof verdict.

In the control loop, Astra breaks a signed topic into checks, gathers prior artifacts and logs, and proposes the next experiment. A miner or operator still has to run that experiment under the declared budget. The digest-pinned judge and the hidden-holdout harness sit outside this loop. If reproduction fails, Astra cannot open the gate.

No other model names are claimed here. A model label is not a score.

Two live challenges. One sealed bundle.

Operators publish signed research topics. A miner answers with a claim, the recipe, and the FLOPs spent. A digest-pinned judge image re-runs the recipe. A separate harness measures a hidden holdout against a baseline sealed before the topic opened. Bounty is the other door: real, reproducible product bugs, priced by an operator.

Proof divides its allocation equally among open topics. A topic either pays the best qualifying result — winner-takes-topic, with exact ties shared — or uses a discovery split: a pass floor plus an improvement pool on the primary metric. Duplicate artifact digests do not earn a second time. The miner pays for the judge pod with their own Lium key. No open topic, or no pinned image: submissions are refused.

How weight moves. What this page will not promise.

Bittensor emits to subnet 100. The trust-root split is Proof / Bounty at 8000 / 2000 bps. A miner's score becomes on-chain weight only after a sealed bundle is published and validators accept the board. Unmatched emission burns to UID 0. Retired challenges — relearn*, design, prism — have no trust-root row and earn nothing.

Validators verify signatures, the reward calculation, and that the bundle matches the published board. They do not re-run every recipe and they do not re-adjudicate every bug. An unsealed fallback is never a valid submit path. Missing prerequisites return a refusal, never an invented score.

A configured share is a mechanism number, not a cashflow. This page will not write an APY, a TAO price, a payout calendar, or a Cortex market-cap. If a figure did not come from the network or a named public source, it is omitted.

Unverifiable research. Static tests. Closed loops.

The source paper models a familiar contest: miners train separate checkpoints, recipes stay private, and a visible static evaluator picks a winner. The network keeps the chosen model and loses the methods behind the other runs. Repeated tuning to a test you can see improves that test without proving the work holds anywhere else.

That model is a design argument, not a claim that every other subnet works this way, and not evidence that Cortex already outperforms them. Private holdouts and pinned images reduce some attack surface. They do not guarantee an honest operator, a correct judge, or a finding that generalizes.

Miners verify. Gates fail closed. Baselines stay sealed.

Cortex changes the unit of work from “here are my weights” to “here is a finding, the recipe, and evidence it works.” Operators commit a baseline before a topic opens. The holdout stays hidden. The judge image is digest-pinned. Validators check signatures and the reward calculation; they do not re-run every experiment. Missing prerequisites return a refusal, never an invented score.

Today the building blocks exist — signed topics, submission intake, payout helpers, signed bundles — and important gaps remain. The Python judge currently acknowledges and applies static checks rather than investigating arbitrary committed code. Proof state is in memory. Automatic leaf emission is not wired. Treat the schema below as the intended gate sequence, then read the honesty strip before you spend compute.

1. 01A signed topic is published with a sealed baseline.
2. 02You submit a claim, the recipe, and the compute you spent.
3. 03The pinned judge re-runs it. Same image, no favourites.
4. 04The harness measures the hidden holdout.
5. 05Scores seal. Validators publish the board, or everything burns.

Two loops the paper wants. Neither is a payout promise.

The paper's infrastructure sketch is a continuous miner research loop on dense InfiniBand clusters: research feeds a training job, a checkpoint is measured against the sealed holdout, and a failed checkpoint rolls back to the last good stack. Accepted methods can feed the next job. Rejected ones do not become the shared stack.

The second loop is how the harness itself improves. Proven artifacts form a research collection. A second, digest-pinned synthesiser may propose a change to the shared harness, training recipe, or data policy. That proposal must pass evaluation against the current stack before adoption. It does not merge raw miner weights. Two complementary findings do not prove they combine — that is another experiment.

Both loops are the intended end state in the paper (§7–8), not shipped product. No date. No revenue model. No APY.

Cursor and Devin sit next to Cortex App, not next to SN100.

Cortex SN100 is the research network — the kernel on Bittensor subnet 100. It pays for reproducible findings and real bugs. Those verified methods feed every Cortex application: Chat, Code, Bot, Image, and the rest of the product suite. Users do not open the subnet as an IDE. They open Cortex App.

The names below appear only on this reading. Cursor and Devin are compared to Cortex App. Claims are about what each system is built to optimize, not a ranking and not a promise that Cortex ships faster code.

Cursor and Devin are described from their public product positioning. Cortex App is the comparable surface. SN100 is not an IDE competitor: it is the research core that feeds those apps. This page does not claim Cortex App replaces an editor or a ticket agent, and it does not promise a payout.

The AI market is large. That is not a Cortex TAM.

Public “AI market” figures mix chips, cloud, applications, and research labour. They do not map onto subnet emissions and they do not imply a token price. This page does not invent a dollar TAM or a market-cap for Cortex.

For industry scale, read the Stanford Institute for Human-Centered AI AI Index — an annual, public survey of investment, capability, and adoption. Use it as order-of-magnitude context for the sector, not as a forecast for SN100 weight.

Source: Stanford HAI, AI Index (public report series, hai.stanford.edu). Category boundaries change by year. We omit a restated figure on purpose.

InfiniBand clusters. A 24/7 miner research loop.

The intended end state in the paper is a shared research collection plus a second, digest-pinned synthesiser that proposes updates to the shared stack — training recipe, harness, or data policy — and must itself pass evaluation before adoption. That agent is a goal, not a shipped component. The two schemas in chapter 07 are that argument drawn as a flywheel.

On the infrastructure side: denser evaluation on InfiniBand clusters, and a continuous miner research loop that could feed real-time adapted training. Those are directional. They are not a product date, not a revenue model, and not a payout promise.

Source, conformité, contact.

Cette page est la lecture principale. Le PDF est le document source daté, pour le conseil juridique et quiconque a besoin de l'artefact statique. Expérience Arcade par echobt. Document : A Proposal for a New Incentive Mechanism on Cortex (Bittensor Subnet 100): Bounty and Proof.