An LLM never writes an answer all at once. It reads the prompt, proposes a probability distribution for the next token, chooses one, appends it to the context, and starts again. That sequential loop is normal inference — and it is also TensorCash mining inference.
The model is not weakened or diverted into a second mining task. Watch the same autoregressive process take three different paths:
Autoregressive generation
Every chosen token changes what comes next
PromptWhy do rainbows appear?
Normal inferenceRNG → uniform draw
orTensorCash mining inferenceSHA-256(chain + context) → uniform draw
Everything else is identicalFollow the path from left to right
Each histogram is the model’s top 50 possibilities for one step. The chosen word appears underneath. That word is then added to the context, so the histogram beside it is a genuinely new distribution conditioned on everything to its left.
The rows begin with the same question but take different valid paths. SHA seed A might choose Light; SHA seed B might choose Sunlight. Once that first token differs, every later distribution can differ too. That is autoregression: the answer is a chain of dependent choices, not a stack of independent draws.
The only difference is the coin flip
- A normal sampler asks its private random-number generator.
- TensorCash hashes the current chain state together with the answer so far.
Both produce the same thing the sampler needs: an unpredictable, uniform-looking number between 0 and 1. That number selects one token from the model’s top-50 distribution. The model, logits, probabilities and autoregressive loop are unchanged.
Why the answer quality is identical
If the model gives one token a 28% chance, both samplers select it 28% of the time. Replacing one uniform pseudorandom source with another does not change the distribution being sampled. It changes the particular path, just as clicking “regenerate” on a normal chatbot does — not the model’s quality.
Replay the figure: the two SHA paths repeat because their chain seeds are fixed; the RNG path changes. All three remain ordinary samples from the same model.
Why the draw is secure
The chain fixes the seed and the entire sequence-so-far enters each hash. A miner cannot dial in an arbitrary draw. Changing an earlier token changes the context, the next-token distribution, and every downstream draw. Trying another path therefore means performing another sequential inference path.
A verifier can replay the chain-derived numbers against the recorded distributions and confirm every selection in order. The mining proof belongs to that bound sequential inference trace; it is not a separate “ticket” minted by each token.
That is the TensorCash magic: mining inference is normal inference with a public, chain-derived source of sampling randomness. Same model, same distribution, same answer quality — now the sequential work is bound to the chain and can be verified.
For the larger consensus picture, read why useful proof-of-work keeps the same security shape.
Authored pseudonymously by Imosuke Takakuni.