Three Empirical Tests of Harness Scaling
Harness scaling investigates whether external execution controls can convert a model's latent capabilities into reliable work through three progressively stronger empirical evaluations:
- Fixed-Model Improvement (Within-Model Lift): Assesses whether a refined execution harness can improve the end-to-end task completion rate of a fixed model without altering its underlying weights.
- Cross-Generation Transfer (Frozen Transfer): Assesses whether an execution control profile developed on an earlier model generation can transfer directly to a newer, more capable model without retuning or modifying control logic.
- Beyond-Benchmark Generalization (Held-Out Task Generalization): Assesses whether the resulting control principles and runtime mechanisms can improve performance on completely different, held-out task distributions beyond the benchmark on which they were originally developed.
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Long-Horizon Agent Reliability: Stateful Scaffolding and Runtime Verification @ University of Michigan - Ann Arbor
Ch.1 Foundations and Operational Challenges - Long-Horizon Agent Reliability: Stateful Scaffolding and Runtime Verification @ University of Michigan - Ann Arbor
Harness Scaling Foundations and Control Design Space - Long-Horizon Agent Reliability: Stateful Scaffolding and Runtime Verification @ University of Michigan - Ann Arbor
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Three Empirical Tests of Harness Scaling
Control-Layer Design Space for Long-Horizon Agents
Harness Scaling
Control-Signal Dilution
Mutable-State Ambiguity
Procedural-Memory Gap in Autonomous Agents
Harness scaling is designed to serve as a direct substitute for model scaling.
Identify the four operational mechanisms implemented within harness scaling to manage agent execution.
An autonomous agent deployment suffers from execution breakdowns when encountering tool errors and taking unpermitted actions. A team member suggests retraining the model's weights to fix these issues. Evaluate this scenario by contrasting harness scaling with model modification, explaining how harness scaling mechanisms resolve these operational problems.
Match each operational mechanism of harness scaling to its functional role in autonomous agent execution.
Evaluate the technical lead's assertion regarding model scaling, and explain to the project manager what harness scaling specifically investigates and achieves without modifying model weights.
When implementing harness scaling in an autonomous agent architecture, which component undergoes systematic enhancement?
According to runtime architecture principles, what specific attribute of an autonomous agent does harness scaling aim to convert into finished, reliable work?
Empirical Tests of Harness Scaling
Control-Signal Dilution
Mutable-State Ambiguity
Three Empirical Tests of Harness Scaling
Which system component is directly enhanced through harness scaling?
Learn After
An engineering team develops an execution harness that significantly improves task performance on a specific model. Next, without modifying or retuning any control logic, they apply that exact harness to the provider's newly released successor model to evaluate whether the performance benefits persist. Which empirical test of harness scaling is being conducted?
Demonstrating Fixed-Model Improvement (Within-Model Lift) requires modifying or fine-tuning the model's underlying weights to support the new execution harness.
What does the Beyond-Benchmark Generalization (Held-Out Task Generalization) test assess in harness scaling evaluations?