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Life sciences · Preprint
arXiv · August 12, 2026
Raises a question worth testing. It does not answer one.
This preprint proposes LEMUR, a training-free inference-time method to suppress privacy leakage of sensitive facts from reasoning traces in reinforcement-learning–trained multimodal models by detecting and redirecting entropy signatures. The work identifies a novel vulnerability but is unreviewed, lacks quantitative comparative evaluation, and does not report effect sizes or statistical significance.
Preprint. Multimodal large reasoning models trained with reinforcement learning; no details on specific model instances, versions, or training protocols.. Intervention: LEMUR: entropy-aware inference-time unlearning framework using latent injection and visual-anchor redirection to suppress sensitive content in reasoning traces.. Compared with: Existing unlearning methods (not named or described in abstract)..
RL-induced exploration leaves sensitive content with distinctive token-level entropy signatures absent from base models LEMUR 'consistently outperforms existing unlearning methods in suppressing both reasoning-trace and answer leakage' across diverse MLRMs (no numeric comparison provided) LEMUR better preserves non-sensitive utility and output fluency compared to existing methods (no metrics reported)
Safety was not reported in the material analysed. Check the source before drawing any conclusion about harm.
The source did not state who this applies to in practice.
This is a preprint describing a novel algorithmic approach to a newly identified privacy vulnerability in multimodal models; it presents proof-of-concept results without peer review, clinical validation, or comparison to established baselines in a controlled trial.
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Reinforcement-learning (RL) post-training equips multimodal large reasoning models (MLRMs) with exploratory chains of thought (CoT), substantially improving visual reasoning. However, we find that this capability introduces a distinct privacy vulnerability: even when a sensitive fact is successfully unlearned from the final answer, the model may still reproduce it in its reasoning trace. This leakage is substantially more pronounced in natively RL-trained MLRMs than in their non -reasoning base models, revealing a privacy risk that existing unlearning methods are not designed to address. We show that RL-induced exploration leaves sensitive content with a distinctive token-level entropy signature that is largely absent from base models. Based on this observation, we propose LEMUR, a fully training-free, inference-time unlearning framework for natively RL-trained multimodal models. LEMUR uses entropy dynamics as a control signal to identify when sensitive reasoning begins and when sanitization should stop. During this interval, it redirects the reasoning trajectory through entropy-modulated visual-anchor latent injection, replacing committed tokens with sanitized, probability-weighted embeddings re-grounded in the input image. Across diverse MLRMs, LEMUR consistently outperforms existing unlearning met hods in suppressing both reasoning-trace and answer leakage, while better preserving non-sensitive utility and output fluency. These results demonstrate that RL-induced entropy dynamics provide a distinctive signal for privacy leakage and that exploiting this signal enables effective training-free unlearning for reasoning-capable multimodal models.
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