Research·arXiv cs.LG·12h ago

LLM outputs may not reveal how models actually think, researchers warn

Researchers formalize a critical interpretability gap: the disconnect between what LLMs output linguistically and how their internal computations actually operate. The paper argues that because models compute over activation spaces rather than language directly, any translation to natural language introduces irreducible information loss. This 'linguistic illegibility' problem undermines both mechanistic interpretability efforts and security audits that rely on probing or analyzing model outputs as reliable windows into reasoning. The finding reshapes how the field should approach model transparency and threat detection, suggesting current interpretability methods may miss or mischaracterize actual model behavior.

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Explainer

The paper's core claim is not just that interpretability is hard, but that it's fundamentally constrained by information loss in translation itself. This suggests the problem isn't fixable by better probes or finer-grained analysis, but rather structural to how we map computation onto language.

This connects directly to two recent findings on interpretability gaps. The Lagged Coupling paper (Sept 1) showed that internal readability doesn't guarantee causal influence, implying mechanistic tools overstate understanding. This new work goes further: even if we read representations perfectly, converting them to language loses irreducible information. Together, these papers suggest current security audits that rely on output analysis or internal probing may be systematically blind to actual model behavior. The Visual Insensitivity Gap (Sept 1) reinforces the pattern: models can appear to reason about inputs they're actually ignoring, a failure mode that linguistic illegibility would help explain.

If security teams begin requiring non-linguistic verification methods (e.g., intervention-based testing on activations rather than output analysis) within the next 12 months, that signals the field is internalizing this constraint. Conversely, if interpretability benchmarks continue to treat output-level probing as a valid proxy for understanding, the paper's implications haven't shifted practice.

Coverage we drew on

Lagged Coupling: Internal Representations Become Readable Before They Become Causal · arXiv cs.CL

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MentionsLLM · arXiv

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Modelwire summarizes, we don’t republish. arXiv cs.LG originally reported this story as “The Implications of Linguistic Illegibility for LLM Security”. 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.