Hermes: Learning Contextual Reasoning Unlocks Test-Time Scaling
Quick summary
arXiv:2609.38332v1 Announce Type: cross Abstract: Test-time scaling improves model performance by allocating additional compute during inference. Using this compute effectively across multiple context windows requires deciding how to allocate fresh contexts and what information to carry between them. We call a model's ability to make these decisions contextual reasoning. Existing approaches largely prescribe these decisions through their harness; we instead shift them to the model. We introduce 1) Hermes, a family of simple, configurable harnesses that progressively varies model control over c
Key takeaways
- arXiv:2609.38332v1 Announce Type: cross Abstract: Test-time scaling improves model performance by allocating additional compute during inference.
- Using this compute effectively across multiple context windows requires deciding how to allocate fresh contexts and what information to carry between them.
- We call a model's ability to make these decisions contextual reasoning.
Why it matters
“Hermes: Learning Contextual Reasoning Unlocks Test-Time Scaling” 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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