---
title: "AI4AI at Test-Time: Strong-to-Weak Capability Transfer via Harnesses"
source: "arXiv AI/CL/LG（论文）"
source_tier: "t2"
url: "https://arxiv.org/abs/2608.12307v1"
permalink: "https://feed.astralor.com/items/itm_6d6bf8549af65828e5fb103a"
published: "2026-08-12T17:53:18.000Z"
score: 76
channel: "ai"
tags: ["arXiv","Strong-to-weak transfer","Test-time scaffolding","Capability distillation"]
---

# AI4AI at Test-Time: Strong-to-Weak Capability Transfer via Harnesses

## AI commentary

> What stands out here is turning 'scaffolding' from a systems-engineering term into a quantifiable transfer mechanism, with near-doubling gains that make you wonder how much training-time distillation cost could be saved. Engineers working on model deployment and inference optimization should watch this closely; for distillation researchers, it's a nudge that test-time design might be cheaper than parameter updates.

## At a glance

This paper proposes a test-time capability transfer method where a stronger builder model constructs inference-time harnesses that help a weaker target model solve tasks more reliably, without any parameter updates. On four representative Theory-of-Mind benchmarks, average target-model performance nearly doubles from 0.49 to 0.91. Analysis shows that gains come primarily from offloading unstable reasoning into deterministic code, benchmark-specific routing, and strict answer-format enforcement, rather than from encouraging deeper reasoning or broader sampling. The authors further find that builder-model reasoning effort improves harness quality monotonically, platform effects are modest relative to builder capability, and weaker targets benefit most. These results suggest inference-time harness design is an important complement to conventional training-time distillation.

## Key points

- Test-time capability transfer is achieved via strong model-built harnesses, without updating weak model parameters.
- Average performance on four Theory-of-Mind benchmarks nearly doubles from 0.49 to 0.91.
- Gains stem mainly from deterministic code offloading, task routing, and strict output format enforcement.
- Weaker target models benefit most, and builder reasoning effort monotonically improves harness quality.

## Full text

Recent work on distillation transfers the capabilities of large models to smaller ones often by updating the latter's parameters, through teacher forcing, on-policy distillation, and related training-time methods. In this paper, we ask whether such transfer can instead occur at test time. We study strong-to-weak scaffolding: whether a stronger builder model can construct inference-time harnesses that help a weaker target model solve tasks more reliably without any parameter updates. Using four representative Theory-of-Mind benchmarks, each builder model uses 5% of the data as a validation set to iteratively refine its harness over multiple rounds, after which the finalized harness is evaluated on the full test set. Empirically, this form of test-time capability transfer is highly effective, nearly doubling average target-model performance from 0.49 to 0.91. Our analysis shows that the gains come primarily from offloading unstable model reasoning into deterministic code, benchmark-specific routing, and strict answer-format enforcement, rather than from encouraging the target model to reason more extensively or sample more broadly. We further find that builder-model reasoning effort improves harness quality monotonically, platform effects are modest relative to the builder model's own capability, and weaker target models receive the largest gains. These results suggest that inference-time harness design is an important complement to conventional training-time distillation, enabling strong models to transfer cognitive structure to weaker models without retraining.

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