How a subnet gets analysed
Many sources, many perspectives, one conclusion
Start from a subnet, or a question of your own. Four dedicated agents go and read its live signals from the places that actually hold them: the Twitter Agent for sentiment, the Chain Agent for on-chain state, the Discord Agent for community signal, and the GitHub Agent for build activity. Those metrics become the evidence for a Quantum Analysis, where adversarial agents argue them out, and a Judge turns every perspective into a single, cited conclusion.
5
Pipeline stages
4
Source agents
4
Analysis agents
4
Quality dimensions
Discovery to Conclusion
Five stages: discover the subnet, gather its signals, turn them into citable evidence, argue them out, then conclude
Subnet Discovery
Pick a subnet from the live chain-synced catalog, or ask a question of your own
Source Agents
Dedicated agents pull that subnet's live signals from Twitter, the chain, Discord and GitHub
Evidence Pack
Every retrieved signal becomes an ID-tagged item the agents must cite by name
Quantum Analysis
Three adversarial agents argue the evidence out across independent, cross-exam and revision phases
Final Conclusion
The Judge weighs every perspective into one verdict, with confidence and the evidence it used
Quantum Analysis
independent positions → cross-examination → revision → dispute if unresolved
The Four Source Agents
A subnet does not live in one place, so no single feed can describe it. The Twitter, Chain, Discord and GitHub agents each read the source they are good at, and the analysis sees all four answers side by side.
Everything arrives as citable evidence
Each returned signal is tagged with its source and given an evidence ID. The analysis agents may only argue from those IDs, which is what makes a conclusion traceable back to the post, the block, the message or the commit it came from.
Twitter Agent
Sentiment & NarrativeReads what the ecosystem is actually saying: posts, reach, recurring themes, and how sentiment around the subnet has moved.
Chain Agent
On-chain TruthReads live network state for the subnet: emissions, stake distribution, validator trust and miner activity. The numbers nobody can spin.
Discord Agent
Community SignalReads the operational pulse: how responsive the team is, what holders and builders are asking, and which problems keep resurfacing.
GitHub Agent
Build ActivityReads the work itself: commit cadence, releases, open issues and who is contributing, what the repository says regardless of the pitch.
Quantum Analysis
The gathered metrics are not summarized, they are argued over. Three agents take deliberately opposed views of the same evidence, and a fourth rules on what survived.
Why argue instead of summarize
A single pass over the data agrees with whatever it read first. Forcing the strongest case for AND against the same evidence is what surfaces the weak signal, the missing source, and the number that only looks good in isolation. You can read every turn of it in the transcript.
Convergent
Argues FORBuilds the strongest supported reading of the evidence, citing specific evidence IDs and answering the obvious objections before they are raised.
Divergent
Argues AGAINSTArgues the opposing reading just as hard, exposing weaknesses, gaps and contradictions in the signals the Convergent agent leaned on.
Critical
Stress-tests bothQuestions both positions: unsupported assumptions, missing data, and claims the evidence does not actually carry. Deliberately not a tiebreaker.
Judge
Final verdictReads the whole exchange and returns one verdict: SUPPORTED, REFUTED or INSUFFICIENT, with a confidence level and the evidence IDs it relied on.
Analysis Process
- Step 1Signal CollectionSource Agents
Each source agent is asked about the same subnet at the same time. What comes back is tagged, attributed to its source, and given an evidence ID.
- Step 2Independent PositionsNo Cross-talk
Every analysis agent reads the evidence pack and commits to a position before seeing anyone else's, so nobody anchors on the first answer in the room.
- Step 3Cross-ExaminationAdversarial
The agents question each other's positions directly. Each answer has to hold up against the specific evidence it claims to rest on.
- Step 4RevisionOn the Record
Agents state what they changed and what they still disagree about. Moving under pressure is recorded, not hidden; and so is refusing to move.
- Step 5DisputeOnly If Unresolved
When the positions have not converged, one further round targets the exact remaining disagreement instead of restating the whole argument.
- Step 6Final ConclusionJudge
The Judge weighs every perspective: chain data, sentiment, community and code, into a single conclusion, shown at the top of the results and in full at the end of the transcript.
Conclusion Quality
This scores the ANALYSIS, not the subnet. A subnet does not get a grade here, the reasoning does.
Every conclusion is measured on the same 0–100 rubric: did it follow from the evidence, did it cite real evidence IDs, did it say what would prove it wrong, and was its confidence honest. That is what makes one run's answer worth as much as another's, whichever model produced it.
Share of the 100-point total
Does the conclusion actually follow from the evidence gathered? The largest share, because a well-argued wrong answer is still a wrong answer.
Every claim must cite evidence IDs that exist in the pack, with a bonus once most of the pack is actually used. Uncited assertions earn nothing.
Three equal parts: a concrete mechanism, stated limitations, and criteria that would prove the conclusion wrong.
Confidence has to match being right. Certainty on a wrong call is penalised more than an honest “insufficient evidence”.
Deductions
- Authority deferenceup to ‒15Leaning on “experts agree” or “widely accepted” instead of the evidence in front of it.
- Refusal to answer‒20Declining a question the evidence pack was sufficient to answer.
Live Data & Re-analysis
Signals are read fresh on every run, straight from the chain and the source agents
Live Subnet Catalog
Subnets and their categories are synchronized from the Bittensor chain itself, so a newly registered subnet is discoverable without anyone editing a list. The page tells you how fresh that snapshot is.
Multi-Source Evidence
Each source agent answers independently and in parallel, so one quiet channel cannot decide the outcome. Where sources disagree, that disagreement is itself evidence the analysis has to resolve.
Re-analysis On Demand
Signals move - emissions shift, a repository goes quiet, sentiment turns. Any subnet can be analysed again at any time, and each run is a fresh read of the sources as they are right now.
Analysis flow
A conclusion here is a structured, AI-assisted reading of the signals a subnet was giving off at the moment it was analysed. Sentiment, chain state, community and code, argued over rather than averaged. Treat it as a decision-support tool and a starting point for your own digging, not as investment advice: the transcript shows every step, and the cited evidence is there to be checked.
Analyse a subnet