Summary
According to a post on X by @SciFi, a new paper by Junlin Wang, Haozhe Chen, Ang Li, Rahul Chalamala and Zhuang Liu, from Duke, Modal Labs, Princeton and NYU, studies how to allocate test-time compute across agents. The approach samples many small teams that share a forum and keeps the best team's result. Using Qwen3.8-27B with a budget of 100 agents, ten teams of ten beat 100 independent agents on 10 of 12 long-horizon tasks, and beat one team of 100 on all four tasks where that was run. A manager-led variant is also mentioned, but the snippet cuts off before giving its results.
AI-assisted summary based on the listed source.
What happened
A new paper by Junlin Wang, Haozhe Chen, Ang Li, Rahul Chalamala and Zhuang Liu (Duke, Modal Labs, Princeton, NYU) samples many small teams of agents that share a forum and keeps the best team's result. With Qwen3.8-27B and a budget of 100 agents, ten teams of ten beat 100 independent agents on 10 of 12...
Why it matters
Teams building agent systems may need to decide whether to split a fixed compute budget across many small teams rather than run lone agents or one large team. These are the paper's reported findings on a specific set of long-horizon tasks, so how well they carry over to other work is still open. Watch for the full paper and the manager-led variant results, which the snippet does not show.
Signal Intelligence
Signal Strength 90%
Technical label WATCH
Public Interest 47
Category RESEARCH
Reader Depth TECHNICAL
Signal Strength reflects source quality, relevance, freshness and evidence. Public Interest helps organize discovery; it is not proof of truth.
Public Interest components
Recognizable Entity Score 60
Practical Impact Score 8
Novelty Interest Score 94
Consequence Score 30
Curiosity Score 16
Shareability Score 59
Why this is here
VQV surfaced this signal because it is recent, relevant to Developer Tools, connected to Scobleizer News Pulse (X).
