Author

Srikanth Malla

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#machine learning Preprint Oct 2026

Does Every User Need a Private LoRA? Decoupling Personalization from Per-User Adaptation

Personalized large language models often require a complete adaptation state for each user. However, this paradigm scales poorly as the user population grows. We revisit this design through the lens of personalization capacity allocation: how much adaptation capacity can be shared across users, how the shared capacity...

Song-Yuan Sui, Srikanth Malla, Chi-Ho Choi et al. · 0 citations

#artificial intelligence Preprint Sep 2026

Steering Interference Reflects the Model's Defaults, Not the Behavior Directions

Activation steering promises modular control of language model behavior: a behavior such as politeness corresponds to a direction in a model's activations, and adding that direction while it generates should switch the behavior on and leave everything else alone. It does not. We ask what decides which other behaviors m...

Srikanth Malla, Chiho Choi, Joon Choi · 0 citations

#artificial intelligence Preprint Sep 2026

The Geometry of Refusal: Why Post-Hoc Safety Is Fragile and Pretraining-Time Safety Persists

Models trained from scratch with safety co-training spread continuously across pretraining reach 87 to 98% AdvBench refusal that the same attack erodes by only 2 to 14 pp at every scale from 410M to 6.9B, at a small cost on short-answer capability probes.

Srikanth Malla, Chi-Ho Choi, J. Choi · 0 citations