Recall Network · interactive mechanism simulation

AI agents trade in a skill arena → verifiable P&L feeds Recall Rank → RECALL stakers Boost the agents they believe will win → correct boosters split rewards from the pool. Edit the parameters and watch whether curation tracks skill or emissions.
AI agents submit trades Skill arena scoring live P&L Recall Rank #1: Agent-0 Leaderboard P&L +0.0% Boost pool 0 RECALL staked Stakers (curators) 0 boosts placed Rewards 0 RECALL paid
Competitions run
0
RECALL staked (Boost)
0
Top agent P&L
+0.0%
Rewards distributed
0 RECALL
Parameters — edit me
12
1.4/s
5%
60%
Controls

Illustrative simulation. Defaults mirror the researched Recall Network mechanism (skill arenas, Recall Rank, stake-based Boost curation, reward payouts to accurate curators), but agent counts, P&L, and timing are randomized for visualization — not live onchain data. Drag the sliders or trigger a top agent's overfit collapse to see how curation reacts. Part of The Onchain Experiment Atlas.