The Capability Manifold and ML Scaling Laws
Quick summary
arXiv:2609.27588v1 Announce Type: cross Abstract: Existing machine learning (ML) scaling laws relate predictive loss to compute, model parameters, and data. However, as models are increasingly deployed through agentic harnesses, loss alone is insufficient to characterize downstream performance: models with similar loss can exhibit different capabilities in reasoning, retrieval, planning, and adaptation. Yet, no unified framework connects such capabilities to the coupled resources available across the ML lifecycle. We bridge this gap by introducing a capability manifold, a multidimensional fram
Key takeaways
- arXiv:2609.27588v1 Announce Type: cross Abstract: Existing machine learning (ML) scaling laws relate predictive loss to compute, model parameters, and data.
- However, as models are increasingly deployed through agentic harnesses, loss alone is insufficient to characterize downstream performance: models with similar loss can exhibit different capabilities in reasoning, retrieval, planning, and adaptation.
- Yet, no unified framework connects such capabilities to the coupled resources available across the ML lifecycle.
Why it matters
“The Capability Manifold and ML Scaling Laws” should be evaluated beyond branding and benchmark scores. Its practical importance will emerge in task accuracy, latency, unit cost, safety and integration with real workflows.
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