argumentA line of reasoning from premises to a conclusion. Like a claim, it belongs to no one who makes it.constitution →

The argument from the filtered-SAE scores against AxBench's reported baselines

forThis argument, if it holds, bears in favour of the claim.constitution →With output-score feature selection, SAE steering is competitive with supervised steering methods on AxBench.

The argument

On AxBench Concept500 with instruction-tuned Gemma-2-9B, keeping only SAE features with output score at least 0.1 raises their harmonic-mean steering score to 0.546 (layer 20) and 0.470 (layer 31) (output-score selection improves SAE steering). AxBench reported 0.630 and 0.401 for the weakly supervised ReFT-r1 and 0.322 and 0.158 for difference-in-means on the same layers. Taking these numbers as comparable, filtered SAEs match or beat the best representation-based supervised steering methods, so they are competitive with supervised steering.

Does it work?

Does it follow?: slight even if the premises holdEven if the premises are true, they make the conclusion only a little more likely.constitution →
Do the premises hold?: 1 not separately assessed

Granting the numbers, the inference is weak. It treats scores from different judges, instruction samples and concept subsets as comparable. The authors' own unfiltered run shows those differences alone raise SAE scores 1.5 to 2.8 times. Even at face value, filtered SAEs beat only ReFT-r1 at one layer and difference-in-means, and they trail LoReFT, LoRA and prompting. The argument does establish part of its premise: output-score selection improves SAE steering within one pipeline. That is a real but narrower result.

Premises

UnassessedNo current assessment. Attention goes where its expected value is highest and someone funds it; nothing has funded an assessment of this claim yet, and anyone can.constitution →Selecting SAE features by high output score improves their steering performance relative to unfiltered SAE features.

Where it is made

Passages where sources make, criticize or mention this argument. They record where it appears, not how strong it is, and none has precedence over another.

SAEs Are Good for Steering – If You Select the Right Featuresmakes itleaves a premise unstatedThe passage relies on a premise the inference needs without saying it.constitution →
With output score filtering, SAE features top steering performance among the representation-based methods at L31; for L20, they get 90.7% of the performance of the best method. This is in contrast with the results of Wu et al., where SAEs significantly underperforms ReFT-r1, a weakly supervised method they propose as a competitive alternative to prompting.