cs papers | Gist.Science

Navigating the Prompt Space: Improving LLM Classification of Social Science Texts Through Prompt Engineering

This paper demonstrates that while systematically varying prompt engineering elements like label descriptions, instructional nudges, and few-shot examples can significantly improve LLM classification accuracy for social science texts, performance gains are often marginal beyond minimal context increases, can sometimes decline with excessive context, and vary substantially across models and tasks, necessitating individual validation rather than reliance on general rules.

Erkan Gunes, Christoffer Florczak, Tevfik Murat Yildirim2026-07-10

💻 computer science

Detecting Cybersecurity Threats using Deep Learning: A Practical Approach

This paper proposes a practical, multi-layer deep learning framework utilizing CNNs and LSTMs to detect advanced cybersecurity threats by addressing critical engineering challenges like data imbalance and providing a guide for scalable, real-time implementation in Security Operations Centers.

Jamal Khouya, Abdelkrim El Mouatasim2026-07-10

💻 computer science

An Intelligent YOLO26-Based Early Warning Framework for Drone Detection in Critical Infrastructure Surveillance

This paper proposes an intelligent, real-time drone detection and early warning framework for critical infrastructure surveillance using the YOLO26 deep learning model, which is trained on a custom multi-scenario database and evaluated against baseline models to demonstrate superior accuracy and speed in identifying small, distant, and challenging drone targets.

Younis Arrabi2026-07-10✓ Author reviewed ⓘ

💻 computer science

Recursive Transport Topology Detects Structural Regime Transitions Associated with Behavioral Failure in World Models

This paper demonstrates that Recursive Indexing (RIX) analysis of latent transport topology can detect structural regime transitions in world models that precede behavioral failure, revealing that representations can remain pointwise stable while their underlying organizational structure collapses under stress.

Jeffery Scott Allbright2026-07-10

💻 computer science

Tamarin (HSQ): Reversible Hierarchical KV-Cache Compression for LLM Inference

Tamarin (HSQ) introduces a reversible, three-tier hierarchical KV-cache compression scheme that routes tokens into a focal tier, learned summary vectors, and a CPU-based archive to achieve 12–28× GPU memory reduction for long-context LLM inference while maintaining near-baseline perplexity and retrieval accuracy.

Alikhan Bazakov, Kirill Kiselev2026-07-10

💻 computer science

A Graded Autonomy Framework for Governing Agentic AI in Health Care

This paper proposes a graded-autonomy framework, adapted from the automotive J3016 standard, to govern agentic AI in healthcare by classifying systems across three domains and six levels to separate capability from authorization, thereby addressing safety gaps in current evaluation metrics through a worked example of autonomous sepsis management.

Sing Chee Tan, Vlada Rozova, Rebecca Jessup, Daniel Capurro2026-07-10

💻 computer science

Adaptive-Hazard Bayesian Online Change-Point Detection for Text Streams: A Dirichlet-Multinomial Formulation

This paper proposes an adaptive-hazard Bayesian online change-point detection method for text streams that dynamically adjusts reset probabilities based on lexical distribution drift via a Dirichlet-multinomial formulation, demonstrating improved detection performance and reduced delay particularly in scenarios involving gradual or weak lexical changes.

Muhammad Ali Gunawan, Amalia Fitri2026-07-10

💻 computer science

Large scale IoT Intrusion Detection using Isolation Forest and Mutual Information based Feature Selection

This paper demonstrates that an Isolation Forest algorithm combined with Mutual Information-based feature selection outperforms Autoencoder and Ensemble methods in large-scale IoT intrusion detection by achieving superior accuracy, F1-score, and AUC while maintaining lower computational complexity on the NF-ToN-IoT-v3 dataset.

siddharth Ghansela2026-07-10

💻 computer science

A Multi-Agent Framework for Explainable Prompt Injection Detection in Large Language Models

This paper introduces PromptShield, an interpretable multi-agent framework that combines transformer-based semantic analysis with XAI techniques like SHAP and LIME to effectively detect and explain prompt injection attacks in Large Language Models.

Atul2026-07-10✓ Author reviewed ⓘ

💻 computer science

Temporal Multi-Signal Fusion for Token-Level Hallucination Detection

This paper proposes a temporal multi-signal fusion approach that treats hallucination as a sequence-labeling problem using a BiGRU to combine text statistics, NLI entailment, and language model surprisal, achieving a significant AUC improvement of 0.840 on RAGTruth by leveraging temporal context rather than model capacity, while maintaining effectiveness on closed-source and unseen models.

Igor Itkin2026-07-10

← Previous Next →