Today’s AI paper leaderboard #1 puts recursive self-improvement into a scientific agent. The core idea is to let the agent break down experiments, write code to run them, and then use the results to iteratively improve its own strategy—forming a self-reinforcing loop of “experiment—feedback—upgrade.” With 17 votes, it leaves other research in the dust.
This line of work naturally fits the encrypted-circles agent protocol: the agent can reliably self-evolve, so an on-chain research market and an experiment-simulation protocol can be plugged in directly. The agent can also manage funds to run hypotheses, validate conclusions, and post the results on-chain. $FET is a long-established coordination protocol for agents, and $TAO is the market for the machine-learning subnet. These two directions line up with the agent self-evolution logic described in the paper.
Going further, a 200K long-context model can run on a regular laptop, meaning individual nodes can also run large-model inference, and the hardware barrier for decentralized inference is lowered by another step. Another paper currently in progress on long-horizon multi-agent systems is stress-testing against the same kind of issue as today’s recursive self-improvement: whether agents can withstand the cumulative failures that come with long-running tasks.
#AI代理 #科学智能体 #AI
This line of work naturally fits the encrypted-circles agent protocol: the agent can reliably self-evolve, so an on-chain research market and an experiment-simulation protocol can be plugged in directly. The agent can also manage funds to run hypotheses, validate conclusions, and post the results on-chain. $FET is a long-established coordination protocol for agents, and $TAO is the market for the machine-learning subnet. These two directions line up with the agent self-evolution logic described in the paper.
Going further, a 200K long-context model can run on a regular laptop, meaning individual nodes can also run large-model inference, and the hardware barrier for decentralized inference is lowered by another step. Another paper currently in progress on long-horizon multi-agent systems is stress-testing against the same kind of issue as today’s recursive self-improvement: whether agents can withstand the cumulative failures that come with long-running tasks.
#AI代理 #科学智能体 #AI
