Sycophancy and hallucination are persistent failure modes of Large Language Models (LLMs) across domains. However, it becomes particularly consequential in clinical question answering, where responses must remain grounded in the provided context and robust to user pressure. Hallucination can introduce information that...
Himanshu Tripathi, Subash Neupane, Shaswata Mitra et al.· 0 citations
Longitudinal clinical reasoning requires large language models (LLMs) to identify and integrate relevant evidence distributed across extended patient histories. Although long-context models can process increasingly large amounts of information, providing more history does not necessarily make relevant evidence more acc...
Taye Akinrele, Noorbakhsh Amiri Golilarz, Subash Neupane et al.· 0 citations
This work proposes off-attractor supervision (OAS) to supervise neural surrogates where they leave the attractor and integrates physical models into neural simulation by extending supervision from fixed reference trajectories to states where the surrogate is likely to fail.
Yi-Long Dai, Shaswata Mitra, Raj Patel et al.· 0 citations
The findings support the claim that BF is a more structured and automation-friendly framework than CWE, and exploration reveals specific gaps in BF, including under-specified guidance on attributes.
Mohammad Nazmul Hoque, Shaswata Mitra, Subash Neupane et al.· 0 citations
Overall, the results show that SNN design choice, evaluation protocol, spike-timing representation, and computational activity should be examined separately when applying SNNs to static network-flow data.
Raj Patel, David Amebley, Taye Akinrele et al.· 0 citations
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