Claims - Subnet 111
@DeSciClaims
Turning 300M scientific papers into a validated claim-evidence graph, so AI can reason over what science actually shows. Built on Bittensor.
106 Following 625 Followers
A core thesis behind Claims is that an open competition will create better results than any in-house process.
Our production data is already validating that thesis - in less than 4 weeks!
Our measure of data quality is the % of miner-submitted claim-evidence pairs for a given paper that:
(1) deviate from our own pipeline and
(2) were unanimously evaluated as an improvement by two independent judges who can't see who submitted.
That quality benchmark has increased from 70% at launch to 94% now.
This happened even though we made our judges MORE critical over time and capped the number of claims any miner can submit to 10, making it much harder for miners to get their work accepted into our Silver record.
The competition is working. Our miners are brilliant.
The next big step is coming soon.
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We're excited for Montreal and seeing many of you in person!
300M scientific papers. Almost none are structured for machines to reason over.
@PKoellinger + @nashgambit of @DeSciClaims show how miners can extract claims + evidence, validators verify them, and incentives turn the literature into trusted, AI-ready data.
Catch them at Exploit:
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The easiest way we found to get subnet Alpha tokens is swapping USDC, ETH, or TAO directly on @taodotcom.
Smooth!
@USDC @ethereum @bittensor
“The product is validated data. We’re going to the companies we’re already talking to and enriching their data with ontologies that match their particular research questions, adding greater value.”
@DeSciClaims appeared on @SubnetSummerTAO yesterday, breaking down how the subnet’s key product is to enhance data for others, through one-time fees and regular subscriptions where incoming and relevant information is consistently fed, sharpening their research.
Watch the full podcast to learn about roadmaps, architecture, and incentives.
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🚨 Subnet Summer AMA x SN111 @DeSciClaims
- NOW LIVE ON YOUTUBE
We covered:
- What SN111 Claims is and why making science machine-readable matters
- The DeSci opportunity: $3 trillion of knowledge locked in human-readable papers
- How miners extract claims, how validators audit for integrity
- The product roadmap: turning a knowledge graph into a revenue-generating API
If you're interested in decentralised science, AI-driven research infrastructure, or how Bittensor tackles knowledge accessibility, this one's for you.
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🚨 Subnet Summer AMA x SN111 @DeSciClaims
- NOW LIVE ON YOUTUBE
We covered:
- What SN111 Claims is and why making science machine-readable matters
- The DeSci opportunity: $3 trillion of knowledge locked in human-readable papers
- How miners extract claims, how validators audit for integrity
- The product roadmap: turning a knowledge graph into a revenue-generating API
If you're interested in decentralised science, AI-driven research infrastructure, or how Bittensor tackles knowledge accessibility, this one's for you.
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Join us at @SubnetSummerT tomorrow at 4pm London time!
🚨 Subnet Summer AMA x SN111 @DeSciClaims
SN111 Claims is building the canonical claim-evidence record for science on Bittensor. It extracts scientific contributions from papers, links each claim to grounded evidence, and makes research machine-readable for AI. The subnet transforms how scientific knowledge is incentivized, validated, and accessed.
🕓 Thursday, September 17th at 4PM BST, we're hosting a live AMA with the Claims team.
We'll cover:
- What SN111 Claims is and why making science machine-readable matters
- The DeSci opportunity: $3 trillion of knowledge locked in human-readable papers
- How miners extract claims, how validators audit for integrity
- The product roadmap: turning a knowledge graph into a revenue-generating API
If you're interested in decentralised science, AI-driven research infrastructure, or how Bittensor tackles knowledge accessibility, this one's for you.
📍:
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The Q4 Roadmap is out.
Oct brings synthetic gold data and a reinforcement learning loop that continously improves data quality.
Nov scales up production and adds structured, statistical artifacts that will allow us to judge the credibility of empirical evidence.
Dec will show a rapidly growing knowledge graph and benchmarks for how much Claims improves agentic accuracy in research tasks compared to unstructured text.
Based on this, we will be able to launch the commercial product and start generating revenue.
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Our v1 whitepaper has dropped.
Claims is live today in v0. This paper sets out what comes next: the architecture, verification model and incentive design behind the move to v1.
Read it here:
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what does a closed AI lab give up by not building on bittensor:native?
Chatting with Grant about what we're building was super fun. Thanks for having us on the pod!
@VenturaLabs
Ep. 99 - Philipp Koellinger & Christian Roessler
Philipp and Christian are building Claims @DeSciClaims Subnet 111
Timestamps
0:00 - Highlights
1:48 - Proof of Pitch to Mainnet in Three Months
2:50 - Why Economists Find Bittensor Fascinating
3:42 - Citations as Science's Reward Function
4:29 - The Replication Nobody Would Publish
6:53 - How Much Published Science Is Wrong
8:26 - The AI Echo Chamber vs. Human Work
9:54 - Making 300 Million Papers Machine Readable
15:11 - Reproducibility vs. Citations
15:46 - Turning Papers Into a Knowledge Graph
16:20 - Why LLMs Hallucinate Citations
21:21 - Longer Context Windows Make This Obsolete?
22:56 - Coverage, Extraction Quality & Evidence Quality
25:16 - What Counts as a Substantive Claim
26:42 - What This Is Worth to Working Academics
27:37 - Academia Admits the System Is Broken
29:26 - How Do You Actually Find the Truth?
32:06 - Truth as Probability, Not Zero or One
33:33 - Logical Proof vs. Physical Replication
35:44 - Correlation vs. Causality
36:28 - Ground Truth From Replication Studies
38:12 - Codifying Claims to Surface Contradictions
40:14 - Can You Trust an LLM as a Judge?
42:48 - Synthetic Checks: Truth Is Sparse
45:40 - How Much Miner Variance Is Desirable
46:55 - Why Bittensor Instead of a Closed Company
49:50 - Success, Revenue & the Palantir Comparison
51:47 - The Flood of AI-Written Papers
54:36 - Advice for Discerning Truth
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Your liver gets the blame for your cholesterol.
But part of the instructions come from your brain.
MC4R is a receptor in the brain that regulates appetite. About three in a thousand people carry a broken MC4R gene copy, and they gain weight early and heavily.
Researchers at @Cambridge_Uni and @unige_en compared these carriers with 336,728 people in the @uk_biobank. Correcting for the weight itself, the carriers had lower cholesterol and triglycerides.
A meal test caught the same effect in real life. 11 carriers, 15 controls matched for weight, a 674 calorie meal at 60 percent fat. The triglyceride spike was about half as big in the carriers.
In the same biobank, common obesity built from many small variants carried clear extra heart risk. Obesity from this one broken receptor showed no detectable increase in that risk.
Same weight on the scale. Different consequence, depending on the route that produced it.
Zorn et al., @NatureMedicine 2025.
This paper went through Claims this week: 8 claims, each tied to the evidence behind it.
@Farooqi_Lab
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Claims is live on mainnet.
Miners now compete to turn papers into structured claims and source evidence. Validators score the work. The best work earns the weight. Each round builds the canonical graph of science.
450 testnet papers led here. Day 0 starts now.⏱️
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