elvis(@omarsar0):Neat work on long-horizon agents. Splitting a hard task across agents is typically how standard multi-agent work. The usual design lets them exchange findings only at phase boundaries, through staged handoffs or synchronized rounds, so communication and work stay mutually exclusive. AgentRadio breaks that constraint with an asynchronous message-passing layer. Three primitives, threads, messages, and waiting for mentions. The last one runs as a background task, surfacing a teammate's discovery without interrupting foreground work. On SWE-Atlas QnA, a single Claude Code agent on Opus 4.6 resolves 32.3% of tasks. Four agents wired through AgentRadio resolve 62.1%. That also beats a single Claude Code agent running the newer Opus 4.8 at 57.2%. Rubric-level analysis shows the gain growing with task difficulty, which points at mid-course correction as the mechanism rather than raw parallelism. Paper: https://t.co/LQ0It2dnZ2 Track more trending AI papers in our academy:

2026.07.31 20:45

Neat work on long-horizon agents. Splitting a hard task across agents is typically how standard multi-agent work. The usual design lets them exchange findings only at phase boundaries, through staged handoffs or synchronized rounds, so communication and work stay mutually exclusive. AgentRadio breaks that constraint with an asynchronous message-passing layer. Three primitives, threads, messages, and waiting for mentions. The last one runs as a background task, surfacing a teammate's discovery without interrupting foreground work. On SWE-Atlas QnA, a single Claude Code agent on Opus 4.6 resolves 32.3% of tasks. Four agents wired through AgentRadio resolve 62.1%. That also beats a single Claude Code agent running the newer Opus 4.8 at 57.2%. Rubric-level analysis shows the gain growing with task difficulty, which points at mid-course correction as the mechanism rather than raw parallelism. Paper: Track more trending AI papers in our academy: