Predictive Memory Localization: Forecasting Selective Intervention Paths from Internal Signals
Quick summary
arXiv:2608.12892v1 Announce Type: new Abstract: Activation steering turns localized representations into control directions, but localization alone does not reveal whether a direction has a selective operating regime. We introduce Predictive Memory Localization (PML), which treats the measured-grid intervention path as the predictive object of memory localization. PML separates random-calibrated target movement from semantic-neighbor and capability damage, and compares static localization and supervised geometry with a strength-disjoint low-dose causal response. Our frozen study covers 3,000 r
Key takeaways
- arXiv:2608.12892v1 Announce Type: new Abstract: Activation steering turns localized representations into control directions, but localization alone does not reveal whether a direction has a selective operating regime.
- We introduce Predictive Memory Localization (PML), which treats the measured-grid intervention path as the predictive object of memory localization.
- PML separates random-calibrated target movement from semantic-neighbor and capability damage, and compares static localization and supervised geometry with a strength-disjoint low-dose causal response.
Why it matters
“Predictive Memory Localization: Forecasting Selective Intervention Paths from Internal Signals” highlights the need for repeatable measurement rather than a single impressive demonstration. Independent validation across datasets and clearly stated limitations determine whether a result can guide product decisions.
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