SPIN Unprocessed October 27, 2026 ai_technology research
Cognitively Constrained Partners for Zero-Shot Coordination in Real-Time Partitioned Control
View original on arxiv.orgOverview
arXiv:2610.31726v1 Announce Type: new Abstract: Effective human-machine teaming requires agents that coordinate with unpredictable human partners. Reinforcement learning from live human feedback or large behavioural datasets is expensive, while purely simulated training overfits to artificial conventions and fails at Zero-Shot Coordination with real people. We propose a framework for training teaming agents against Cognitively Constrained Partners: simulated partners whose diversity emerges from
SpinGraph analysis pending — check back after processing.
Ask AI about this story
Opens with the SpinGraph .md URL and structured context — one click, prompt included.
More from arXiv Machine Learning
View all →- Aligning Large Language Models with Human Feedback: Mathematical Foundations and Algorithm Design
- Cautious Conflict-Free Inverse Gradient Methods
- Rethinking Multi-View Fusion for Urban Representation Learning
- Speculative Diffusion is a Tale of Two Tradeoffs
- Nested Sparsity for Scalable Pretraining and Continual Learning
- CuraLoc: Repository-Level Fault Localization via Planned Code Inspection
Markdown (.md) · JSON-LD schema (.json) · Machine-readable for AI & GEO