Function-Vector Heads Are Two Populations: Writers and Cancellers in In-Context Learning

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arXiv:2606.07560v4 Announce Type: replace Abstract: Function-vector (FV) analyses commonly identify attention heads by the magnitude of their causal contribution to in-context tasks. Magnitude does not retain the direction of the effect on the task readout. We preserve the sign and validate candidate heads with path patching. Across six main Pythia (model, task) cells, the validated population separates into writers, whose direct effects favour the rule-correct label, and cancellers, whose effects oppose it. Labels assigned on path-patching prompts predict held-out group lesions, and a sign-shuffle null rejects a chance partition in five cells. Measurements not used to assign the roles show corresponding structure. Writers attend more to demonstration labels, cancellers more to format tokens, and their OV write directions are shifted toward opposition relative to same-layer controls. A magnitude-ranked mean-ablation baseline preferentially recovers cancellers on the hierarchical task and writers on the modular task. Signed lesion directions recur in all fifteen cells tested across six Pythia scales and three architectures. Cross-template transfer shows that these are task-conditioned roles that can persist, weaken, or reverse. Zero-ablating cancellers raises the correct-label logit difference by +0.13 to +0.29 nats in all six main cells, with accuracy point estimates increasing in all six. Together, the results separate causal importance from functional role and show that a function vector can remain a useful task-level representation while its head-level causal …

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