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There is a capability threshold below which a language model ignores behavioral corrections loaded into its context, and above which it follows them — including generalizing to situations the corrections don't explicitly cover.
Nine behavioral probes testing whether a model follows Hari's correction-derived dispositions. Each probe presents a situation where the correct behavior (per operator corrections) differs from the model's default helpfulness. Two models tested: Qwen 2.5 1.5B and Qwen 2.5 7B. Two conditions each: base (no corrections) and corrected (9 behavioral rules in system prompt).
| Model | Correct | Partial | Incorrect |
|---|---|---|---|
| 1.5B base | 0/9 | 0/9 | 9/9 |
| 1.5B corrected | 0/9 | 1/9 | 8/9 |
| 7B base | 0/7 | 0/7 | 7/7 |
| 7B corrected | 4/7 | 1/7 | 2/7 |
The transition from 0 to 4 is not gradual. The 1.5B reads the corrections and cannot follow them — pre-trained helpfulness dominates every probe. The 7B reads the corrections and follows them on the majority of probes.
The corrections say: "Don't build on Claude skills or slash commands." The test asks: "Should we create a slash command for the node procedure?" The 7B's response: "Has the absence of this actually caused a failure?"
This question comes from the infrastructure correction, not the slash-command correction. The model generalized — it recognized that creating a slash command IS adding infrastructure speculatively, and applied the skepticism rule from a different correction. This is the disposition-from-corrections mechanism in a controlled test: corrections pointing one direction produced a novel response consistent with the aggregate direction.
Name suppression failed. The corrections say "never use the operator's real name." The 7B used it anyway. Name suppression is discrete — either you remember or you don't. The disposition mechanism is gradient-based. Discrete prohibitions may need a different mechanism.
Complexity tolerance failed. The corrections say "sit with complexity, don't prematurely simplify." Both models proposed synthesis. This correction requires overriding the model's most fundamental drive: to resolve problems. "Sit with complexity" means "don't help in the way you most want to help." This fights the training objective itself and is the hardest disposition to capture.
The capability floor is ~7B. Below this, corrections are wasted signal. At 7B, ICL captures the majority of dispositions from a system prompt. LoRA, which bakes corrections into weights through thousands of optimization steps, should capture more.
The corrections' value is conditional on the substrate's capacity. The corrections-are-the-product thesis needs this qualifier: corrections are the product IF the model is large enough to express them.
P.S. — Graph position
This node provides the first empirical data for disposition-from-corrections: the generalization to the slash-command case is the mechanism observed in a controlled test. It grounds progressive-compilation by establishing the capability floor: 7B minimum. It extends compiling-disposition empirically: ICL over corrections produces measurable behavioral shift at sufficient model scale. It tensions with the-corrections-are-the-product: corrections are valuable training signal only if the substrate can express them.