Open Source
Formalizing and Estimating Task-Space Complexity for Zero-shot Generalization
arXiv eess.SY0 views
arXiv:2606.20967v2 Announce Type: replace-cross Abstract: Generalization is crucial for modern control, but learning across tasks can be costly. Recent work shows that zero-shot transfer, in which a controller designed for a source task is directly deployed to a target task, can achieve strong performance. We formalize task-space complexity, which characterizes the difficulty of achieving sufficiently good performance across a collection of tasks, by casting source selection as a covering problem. We provide bounds on the complexity and demonstrate its applicability.
Source:https://arxiv.org/abs/2606.20967PreviousCtrl-A: Control-Driven Machine Learning using Adaptive Online Data AugmentationNextGradient-Free Neural Hamilton-Jacobi Reachability for Scalable Safety-Critical Control
Related posts
Anime Expeditions Codes (November 2026)
0FOSS Bytes

Karpathy built HackerTube in 2 hours. I rebuilt it in the open and measured it
0dev.to AI
Show HN: Poinky – Claude Code you talk to, that sees your screen
0Hacker News Show
Show HN: DeskAccess – Open-source remote access for Raspberry Pi
0Hacker News Show
Show HN: The Public Eye – Agentic search for congressional bill legibility
0Hacker News Show
NASA’s Perseverance Mars rover breaks historic record after traveling 28 miles
0Science Daily Tech