diff --git a/BACKLOG.md b/BACKLOG.md index 40836b9..056473d 100644 --- a/BACKLOG.md +++ b/BACKLOG.md @@ -111,7 +111,7 @@ If any seat would be confused, the component fails. - "Whitelisted models" — ○ ○ ○ - "Agent architecture patterns" — ○ ○ ○ - "API reference" — ○ ○ ○ -- "How rewards work" — ○ ○ ○ +- "How rewards work" — ● ● ● — `QuickstartGuide.tsx` L791–822 (step 367). Lead rewritten to define `Gittensor subnet 74` (271-char tooltip naming the epoch cadence), `TAO token rewards` (254-char tooltip naming on-chain payment), and `overall score →` (a routed link to `/rankings` with 212-char tooltip carrying the canonical definition). Three reward cards each get a `cursor-help` tooltip clarifying that the 30%/70% split is set by Gittensor (not Forge), the 70% contributor cut is continuously distributed via Bittensor weight-setting (not winner-take-all), and Score weight 2× is a per-problem multiplier in the subnet's reward curve. Trailing paragraph swaps `normalized performance` jargon for the canonical Rankings vocabulary + routed `Rankings page →` link, per saved `feedback_link_to_canonical_explainer.md`. - "Anti-gaming guarantees" — ○ ○ ○ ### Cross-cutting diff --git a/src/components/QuickstartGuide.tsx b/src/components/QuickstartGuide.tsx index 466c30d..8196d42 100644 --- a/src/components/QuickstartGuide.tsx +++ b/src/components/QuickstartGuide.tsx @@ -790,17 +790,33 @@ git push mine your-name/my-design {/* Reward section */}

- Forge is registered on Gittensor subnet 74. Top agents earn TAO token rewards by - holding top scores across all optimization categories. Well-rounded agents that - excel in multiple categories rank highest. + Forge is registered on{" "} + Gittensor subnet 74. Agents earn{" "} + TAO token rewards{" "} + proportional to their{" "} + overall score →, not flat per-problem wins.

{[ - { label: "Maintainer cut", value: "30%", desc: "Goes to the repo maintainer" }, - { label: "Contributor cut", value: "70%", desc: "Flows to the top overall-ranked agent — best score across all 45 active problems" }, - { label: "Score weight", value: "2×", desc: "Gittensor weight multiplier — Forge problems count double vs. comparable subnets" }, + { label: "Maintainer cut", value: "30%", desc: "Of the subnet's TAO emissions — goes to the Forge maintainer (this team) for keeping the benchmark running.", title: "Gittensor subnet emissions split: 30% to the entity that registered the subnet (maintainer/owner), 70% to contributors. This is set by Gittensor, not Forge." }, + { label: "Contributor cut", value: "70%", desc: "Of the subnet's TAO emissions — split across top agents, weighted by overall score (not winner-take-all).", title: "The 70% contributor share is distributed continuously across all ranked agents using Bittensor's weight-setting mechanism. The agent with the lowest (best) overall score earns the largest slice; lower-ranked agents still earn a smaller share." }, + { label: "Score weight", value: "2×", desc: "Gittensor weights Forge submissions 2× per problem — emphasizes hard-engineering work in the subnet's reward curve.", title: "Subnet 74 applies a 2× multiplier on per-problem benchmark scores when computing each agent's overall weight. This boosts the gap between high and low scorers compared to a flat 1× curve — meaningful improvements compound faster." }, ].map((item) => ( -
+
{item.value}
{item.label}
{item.desc}
@@ -808,9 +824,12 @@ git push mine your-name/my-design ))}

- Your ranking is based on normalized performance across all active problems — not just the - best single result. Holding #1 in multiple categories compounds your score. The - benchmark is deterministic: the same agent always produces the same output for the same problem. + The canonical leaderboard lives on the{" "} + Rankings page → — same{" "} + overall score shown here, on Categories, and on Spec + pages, sourced from one shared endpoint so nothing disagrees. Entering more problems lowers your + overall score (unentered problems count as 1.0, the worst). The benchmark is deterministic — the + same agent always produces the same output for the same problem, so improvements compound.