Pith. sign in

REVIEW 1 cited by

BitTensor: A Peer-to-Peer Intelligence Market

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2003.03917 v3 pith:SIE5WB3E submitted 2020-03-09 cs.AI cs.LGcs.MA

classification cs.AIcs.LGcs.MA
keywords intelligencemarketotherpeerscollusionnetworkpeer-to-peerresistant
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

As with other commodities, markets could help us efficiently produce machine intelligence. We propose a market where intelligence is priced by other intelligence systems peer-to-peer across the internet. Peers rank each other by training neural networks which learn the value of their neighbors. Scores accumulate on a digital ledger where high ranking peers are monetarily rewarded with additional weight in the network. However, this form of peer-ranking is not resistant to collusion, which could disrupt the accuracy of the mechanism. The solution is a connectivity-based regularization which exponentially rewards trusted peers, making the system resistant to collusion of up to 50 percent of the network weight. The result is a collectively run intelligence market which continual produces newly trained models and pays contributors who create information theoretic value.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Gradients: When Markets Meet Fine-tuning -- A Distributed Approach to Model Optimisation

    cs.AI 2025-06 reject novelty 6.0 of 10

    Gradients reports that competitive, reward-driven fine-tuning beats centralized AutoML in 82 to 100 percent of comparisons, but its evaluation does not isolate competition from a much larger compute budget.

Pith tools