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20222026
most citedImpacts and Risk of Generative AI Technology on Cyber Defense

14 citations · 60 across the 41 of their papers we have counts for

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7 papers · 1 filter

cs.CL2026

Can LLMs Reason Over Long Horizons? An Empirical Evaluation of Context Strategies for Longitudinal Clinical Reasoning

Taye Akinrele, Noorbakhsh Amiri Golilarz, Subash Neupane +2

Longitudinal clinical reasoning requires large language models (LLMs) to identify and integrate relevant evidence distributed across extended patient histories. Although long-conte…

cs.LG2026

Staying on the Attractor: Supervising Neural Surrogates of 3D Turbulence Where They Leave It

Yilong Dai, Shaswata Mitra, Raj Patel +7

Neural surrogates are trained to predict 3D turbulent flows in place of direct numerical simulation (DNS). For chaotic flows, the goal is short-term pointwise accuracy followed by…

cs.CR2026

Evaluating the NIST Bugs Framework Against CWE as a Successor for Automated Vulnerability Classification

Md Nazmul Hoque, Shaswata Mitra, Subash Neupane +2

Vulnerability classification based on root cause weaknesses is essential for numerous cybersecurity activities, where the Common Weakness Enumeration (CWE) serves as a public repos…

cs.AI2026

Gated Activation Steering for Reducing Sycophancy & Hallucination in Medical Question Answering

Himanshu Tripathi, Subash Neupane, Shaswata Mitra +3

Sycophancy and hallucination are persistent failure modes of Large Language Models (LLMs) across domains. However, it becomes particularly consequential in clinical question answer…

cs.AI2026

How Compliant is Sepsis Treatment? An Expert-Guided Neuro-symbolic Pipeline for Generating Clinical Compliance Insights

Himanshu Tripathi, Kaushik Roy, Subash Neupane +1

Verifying whether clinical care follows evidence-based protocols is a natural neuro-symbolic problem, yet the safety-critical setting defeats either paradigm alone. We present an e…

cs.CL2026

Find Before You Fine-Tune: A Diagnostic Study of Small LLMs for Cybersecurity QA

Shaswata Mitra, Subash Neupane, Trisha Chakraborty +4

Large Language Models (LLMs) are increasingly fine-tuned for critical-domain Question-Answering (QA), yet choosing which small model to adapt, before paying the cost of adaptation,…

cs.CR2026

The Value of Spike Timing: A Leakage-Resistant Benchmark of SNN Design Choices for Network Intrusion Detection

Raj Patel, Shaswata Mitra, David Amebley +4

Spiking neural networks (SNNs) are increasingly studied for network intrusion detection, but comparative evidence on how neuron models and spike encodings affect performance remain…