large language models

Auditing Alignment Controllability in LLMs via Political Axes

A dispersion-first stress test of prompt-based controllability across 12 ideological personas plus an unsteered baseline, 70 Political Compass items, ten replicates, and seven leading LLMs: GPT-5, Claude, Grok, Gemini, DeepSeek, Kimi, and Qwen.

Financial news sentiment meets market data: A large language model-based approach to stock price prediction

Identifying central bank multi-objective preferences using large language models: Evidence from China

Synergistic Perception-Reasoning Governance: Grounding Medical MLLMs with Verifiable Anatomical Evidence

A holistic, training-free evidence-injection framework that systematically mitigates hallucinations through dual-side evidence injection and introduces a task-aware dynamic router to select modality-specific interventions based on task semantics, balancing perceptual grounding and linguistic fluency is proposed.

NeuroPrefetcher: Storage-Aware Sparse LLM Inference via Delta Prefetching

NeuroPrefetcher is presented, a storage-backed LLM inference system that exploits that MLP activity during autoregressive decoding has strong temporal locality, and achieves 7.9-12.0x speedup over llama.cpp across constrained memory budgets.

Adversarial Machine Learning: Security Risks and Defense Strategies in AI-Driven Applications

A detailed overview of the security risks associated with adversarial attacks is offered, including evasion attacks carried out at inference time, data poisoning that corrupts the training process, backdoor insertion that hides dormant triggers inside a model, and model inversion that leaks private information back out...

MARS: Multi-hop Adaptive Retrieval and SPARQL Generation for KGQA

This work proposes MARS, a scalable knowledge graph question answering (KGQA) approach that requires no model fine-tuning, and performs a structured retrieval procedure that links question entities to the KG and iteratively retrieves relevant next-hop information.

ProphDR: An Interpretable Deep Learning Model for Predicting Cancer Drug Response via Multi-Omics and Cross-Attention Mechanisms

ProphDR is an interpretable deep learning framework that integrates multiomics data and drug structural information using a hierarchical attention mechanism, and generates biologically interpretable attention maps that highlight key pharmacophores and resistance-related genes consistent with established mechanisms in N...

TACTIC-KG: Toward Small Agent Teams for Cyber Threat Intelligence Knowledge Graph Construction

TACTIC-KG is introduced, an agentic framework for CSKG construction that decomposes the task into modular, specialized LLM agents responsible for extraction, typing, verification, and curation that improves stability, recall, and graph consistency while reducing deployment cost.

Open-Vocabulary Semantic Segmentation with Uncertainty Alignment for Robotic Scene Understanding of Indoor Building Spaces

Society is steadily embracing the idea of coinhabitant service robots in the built environment for assistance with household tasks and chores. In particular, the growing number of older adults, individuals with disabilities, and people requiring daily assistance has further created an urgent need for intelligent coin...

CodeAssay: A Multi-Metric Benchmark with Audited Ground Truth for LLM Code Generation

These findings show that reliable evaluation of LLM-generated code requires validated ground truth, protected tests, and multiple explicitly interpreted measures, and that CodeAssay provides a reproducible basis for evidence-based model evaluation in AI-augmented software development.

Automated PLC Programming Using a Multi-Task LLM System

Programmable Logic Controllers (PLCs) are central to industrial automation, yet their programming remains largely manual and time-consuming. While Large Language Models (LLMs) excel at general-purpose code generation, their use in industrial control is limited by tight coupling with physical hardware, constrained I/O s...