Taaha Saleem Bajwa

LLM assistants increasingly retain long-term memories of their users, but retaining a memory is not the same as knowing when to use it. We introduce PersistBench, a benchmark for two safety risks that emerge from persistent memory: cross-domain leakage, where models inappropriately inject stored personal context into unrelated requests, and memory-induced sycophancy, where retained memories cause models to reinforce a user’s stated beliefs. Evaluating 18 models, we find a median failure rate of 53% on cross-domain samples and 97% on sycophancy samples, indicating that current memory-augmented assistants lack reliable mechanisms for deciding when a memory should be forgotten.

This work examines how the May 2025 India-Pakistan conflict reshaped domestic news coverage in Pakistan. Using LLMs to analyse roughly 2,600 articles from three major newspapers, it finds that war-related reporting significantly overshadowed coverage of political opposition and dissent. The results suggest that regional conflict can suppress democratic dialogue, underscoring the importance of protecting journalistic independence in politically unstable regions.

An evaluation of how large language models handle Urdu, a low-resource language, separating comprehension from generation. The work finds that LLMs primarily reason in English and use their final layers as a translator into the target language: the internal English reasoning is coherent, but the translation step introduces significant information loss, so models comprehend Urdu substantially better than they generate it. Mechanistically ablating these translation features and applying a dedicated translation model to the internal representations improves performance while better preserving cultural context.