Author

Alizishaan Khatri

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Preprint Aug 2026

Do All LLMs Know When They're Being Harmful? A Reproducibility Study of Latent-Space Safety Probes Across Model Families

Khatri et al. (2026) [DOI: 10.1109/DSN-W70714.2026.00027] show that lightweight MLP probes on final-layer activations of a single 8B model (LLaMA-3.1-8B) detect harmful prompts at F1 competitive with guard models 1000x larger, using one probe per benchmark. We reproduce this pipeline end-to-end and extend it along two...

Alizishaan Khatri, Dun-Li Chan · 2 citations

#machine learning Preprint Sep 2026

Learned Compression of SAR Phase-History Data: A Rate-Honest Feasibility Study on GOTCHA

On-board compression of synthetic aperture radar (SAR) phase history is bandwidth-critical, and block-adaptive quantization (BAQ) remains the operational standard. We test whether a small convolutional autoencoder, with its encoder on the sensor, can compete with BAQ on complex phase-history patches from the AFRL GOTCH...

Alizishaan Khatri · 0 citations

#artificial intelligence Preprint Sep 2026

Same Probe, Different Numbers: Are Activation Probes Robust to Inference-Time Numerical Non-Determinism?

Robustness evaluations of activation monitors should report per-example agreement rather than aggregate accuracy, separate representational noise from input change, and state the serving configuration.

Alizishaan Khatri · 0 citations

#artificial intelligence Preprint Aug 2026

Probing the Prefill: Detecting Code Vulnerabilities via Latent Activations

LLM-based code generation is now embedded in mission-critical pipelines, but defenses against vulnerable output remain post-hoc -- static analyzers, fine-tuned classifiers, or an LLM judge that screen completed code, ignoring the generating model's own internal state. We test a narrower, directly measurable question: w...

Alizishaan Khatri · 0 citations