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Aashiq Muhamed

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#machine learning Preprint Sep 2026

Decoy Direction Optimization: A Post-Hoc Defense Against LLM Abliteration

Decoy Direction Optimization (DDO), a fast, post-hoc weight-editing defense that requires no base-model finetuning, is introduced, based on a simple mechanistic insight: ablation attacks rely on contrastive estimators to find the refusal direction.

Aashiq Muhamed, Mona T. Diab, Virginia Smith · 2 citations · ⚡1
#machine learning Review Sep 2026

MOLE: Detecting Insider Threats in AI Agents

Model misalignment, prompt injection, or operator misuse could lead AI agents operating frontier-lab accounts to exfiltrate model weights, poison training data, or weaken release gates. Existing benchmarks do not test whether defenders can detect this activity among routine work under a limited review budget. We introd...

Aashiq Muhamed, Virginia Smith · 0 citations

DSPA: Dynamic SAE Steering for Data-Efficient Preference Alignment

This work proposes Dynamic SAE Steering for Preference Alignment (DSPA), an inference-time method that makes sparse autoencoder (SAE) steering prompt-conditional, and audits the SAE features DSPA modifies, finding that preference directions are dominated by discourse and stylistic signals.

J. Wedgwood, Aashiq Muhamed, Mona T. Diab et al. · 1 citation
Conference Open access 2026

Mechanistic Interpretability Should Prioritize Feature Consistency in Sparse Autoencoders

This work proposes the Pairwise Dictionary Mean Correlation Coefficient (PW-MCC) as an assignment-based metric to quantify consistency and demonstrates that high levels are achievable with appropriate architectural choices for TopK SAEs on LLM activations with appropriate architectural choices.

Xiangchen Song, Aashiq Muhamed, Yujian Zheng et al. · 0 citations

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