
Ancil Crayton, Nathan Batty, Neil Pendyala, Basil Hasan Khwaja, Jess Bergs
SycEval-EA studies how sycophantic responses can turn into persistent misinformation in LLM memory. Across 352,800 trials involving seven models and 200 algebra and medical questions, the study distinguishes between caving to false corrections, valid revision, and whether admitted errors are later stored and reproduced. The results show that framing can influence caving, with reality-testing generally reducing it and contingent approval increasing it, while structured memory records reduce false admissions and contamination compared with naive summaries.
Basil Hasan Khwaja, Yuan-Yao Lou, Jonathan Spencer, Kwang Taik Kim
Real-time autonomous vehicle (AV) local planning demands low-latency, safe, and compute-efficient trajectory generation. While offloading Model Predictive Control (MPC) rollouts to edge servers expands the planning search space, intermittent wireless downlinks and transmission errors create critical latency bottlenecks. Different rules that govern edge servers can not all prove to benefit the agent at all times. Existing frameworks remain agnostic to historical execution patterns and spatial context because of large overhead computation that exists with brute-force recommendation systems...
Basil Hasan Khwaja, Dylan Chen, Guntas Toor, Anastasiya Kuznetsova
We investigate how drug-group semantics are represented and retrieved within Llama-based biomedical language models using causal and probing-based interpretability methods. Our results demonstrate that early layers play a key role in encoding drug-group knowledge, with pharmacological semantics distributed across tokens rather than localized to single positions.





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