11 papers
Evidence-Grounded SLM Behavioral Prediction Architecture for Future Assistance Use
Adeyemi Adeseye, Aisvarya Adeseye, Hannu Tenhunen +1
Users often exhibit changes in interaction behavior before explicitly seeking assistance, enabling systems to anticipate future assistance use. This is especially important in high…
CARE-SD: Explainable Reasoning Architecture for Local SLM Self-Disclosure Analysis in Textual Data
Aisvarya Adeseye, Adeyemi Adeseye, Mohammad Tahir +1
Cybersecurity interviews uncover critical security experiences, behaviors, and privacy concerns often not fully captured by conventional technical assessments. Consequently, identi…
Reasoning Topology Matters: A Controlled Study of LLM-Based Cybersecurity Analysis
Jiling Zhou, Aisvarya Adeseye, Antti Hakkala +2
Large Language Models (LLMs) are increasingly used in cybersecurity, where accurate analysis often requires multi-step and context-dependent reasoning over complex and heterogeneou…
Dynamic Agentic AI Expert Profiler System Architecture for Multidomain Intelligence Modeling
Aisvarya Adeseye, Jouni Isoaho, Seppo Virtanen +1
In today's artificial intelligence driven world, modern systems communicate with people from diverse backgrounds and skill levels. For human-machine interaction to be meaningful, s…
Strengthening Human-Centric Chain-of-Thought Reasoning Integrity in LLMs via a Structured Prompt Framework
Jiling Zhou, Aisvarya Adeseye, Seppo Virtanen +2
Chain-of-Thought (CoT) prompting has been used to enhance the reasoning capability of LLMs. However, its reliability in security-sensitive analytical tasks remains insufficiently e…
Parallel LLM Reasoning for Bias-Resilient, Robust Conceptual Abstraction
Aisvarya Adeseye, Jouni Isoaho, Adeyemi Adeseye
Large language models (LLMs) have been increasingly used to analyze text. However, they are often plagued with contextual reasoning limitations when analyzing long documents. When…
Improving Quantized Model Performance in Qualitative Analysis with Multi-Pass Prompt Verification
Aisvarya Adeseye, Jouni Isoaho, Adeyemi Adeseye
Quantized Large Language Models (LLMs) are used more often in qualitative analysis because they run fast and need fewer computing resources. This study examines how different lower…
Entropy and Attention Dynamics in Small Language Models: A Trace-Level Structural Analysis on the TruthfulQA Benchmark
Adeyemi Adeseye, Aisvarya Adeseye, Hannu Tenhunen +1
Small language models (SLMs) have been increasingly deployed in edge devices and other resource-constrained settings. However, these models make confident mispredictions and produc…
A Prompt-Based Framework for Loop Vulnerability Detection Using Local LLMs
Adeyemi Adeseye, Aisvarya Adeseye
Loop vulnerabilities are one major risky construct in software development. They can easily lead to infinite loops or executions, exhaust resources, or introduce logical errors tha…
Local Language Models for Context-Aware Adaptive Anonymization of Sensitive Text
Aisvarya Adeseye, Jouni Isoaho, Seppo Virtanen +1
Qualitative research often contains personal, contextual, and organizational details that pose privacy risks if not handled appropriately. Manual anonymization is time-consuming, i…
Modular AI-Powered Interviewer with Dynamic Question Generation and Expertise Profiling
Aisvarya Adeseye, Jouni Isoaho, Seppo Virtanen +1
Automated interviewers and chatbots are common in research, recruitment, customer service, and education. Many existing systems use fixed question lists, strict rules, and limited…