Temporal preference in Qwen3-4B-Instruct-2507 localizes to layers 17–35 (especially L24 attention), has curved residual-stream geometry, is behaviorally unstable, and can be bidirectionally steered.
arXiv preprint arXiv:2601.20834 , year=
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Sparse interventions on 8–64 neurons distributed across layers can activate task behavior in instruction-tuned LLMs, outperforming first-order linear steering approaches by modeling nonlinear neuron interactions.
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PROPEL amortizes solver evaluation with a trained activation probe to optimize task generators toward a target solve rate, raising the share of learnable tasks from ~10% to ~20% in coding and SWE experiments.
Linear probes on residual-stream activations identify a shared preference vector in LLMs that tracks choices across prompts and causally steers decisions even for anti-correlated personas.
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