[2603.04045] Inference-Time Toxicity Mitigation in Protein Language Models

Computer Science > Machine Learning arXiv:2603.04045 (cs) [Submitted on 4 Mar 2026] Title:Inference-Time Toxicity Mitigation in Protein Language Models Authors:Manuel Fernández Burda, Santiago Aranguri, Iván Arcuschin Moreno, Enzo Ferrante View a PDF of the paper titled Inference-Time Toxicity Mitigation in Protein Language Models, by Manuel Fern\'andez Burda and 3 other authors View PDF HTML (experimental) Abstract:Protein language models (PLMs) are becoming practical tools for de novo protein design, yet their dual-use potential raises safety concerns. We show that domain adaptation to specific taxonomic groups can elicit toxic protein generation, even when toxicity is not the training objective. To address this, we adapt Logit Diff Amplification (LDA) as an inference-time control mechanism for PLMs. LDA modifies token probabilities by amplifying the logit difference between a baseline model and a toxicity-finetuned model, requiring no retraining. Across four taxonomic groups, LDA consistently reduces predicted toxicity rate (measured via ToxDL2) below the taxon-finetuned baseline while preserving biological plausibility. We evaluate quality using Fréchet ESM Distance and predicted foldability (pLDDT), finding that LDA maintains distributional similarity to natural proteins and structural viability (unlike activation-based steering methods that tend to degrade sequence properties). Our results demonstrate that LDA provides a practical safety knob for protein generators t...