Neural networks hide entangled features in multiple ways, not one
Researchers have identified a fundamental limitation in how neural networks suppress unwanted features. When two concepts are densely entangled in a shared subspace, linear erasure methods fail catastrophically, destroying both features instead of the target alone. Networks trained via gradient descent sidestep this by converging to one of two distinct non-linear solutions, determined by initialization rather than the optimization process itself. This bifurcation reflects stable attractor dynamics, not experimental noise. The finding matters for mechanistic interpretability and safety: it reveals that feature suppression is far messier than assumed, and that identical training procedures can yield fundamentally different internal circuits depending on random seed.
Modelwire context
ExplainerThe paper's core contribution is not just that linear erasure fails on entangled features, but that networks avoid this failure by converging to one of two discrete non-linear solutions based purely on random initialization. This means identical training runs produce fundamentally different internal structures, not just different weights.
This connects directly to the mechanistic interpretability gap exposed in recent work on LLM robustness. The radiology report evaluation paper from late September showed that even specialized evaluation systems struggle with factual consistency, partly because we lack reliable methods to verify what internal representations models actually learn. This new finding suggests the problem runs deeper: even when we try to suppress unwanted features, the network's solution space is discrete and path-dependent, making it harder to predict or control what emerges. The implication for safety is that feature suppression cannot be treated as a deterministic, linear problem.
If researchers can identify which initialization conditions lead to each attractor solution before training completes, that would confirm the bifurcation is truly deterministic and controllable. Watch for follow-up work within the next 6 months showing whether this pattern holds across different architectures and whether the two solutions differ in robustness to adversarial inputs.
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Modelwire summarizes, we don’t republish. arXiv cs.LG originally reported this story as “Hidden not Deleted: How Networks Suppress Entangled Features”. The full content lives on arxiv.org. If you’re a publisher and want a different summarization policy for your work, see our takedown page.