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Research Paper#Software Engineering, Grey Literature, AI Tools🔬 ResearchAnalyzed: Jan 3, 2026 19:16

Automated Grey Literature Extraction Tool for Software Engineering

Published:Dec 28, 2025 20:20
1 min read
ArXiv

Analysis

This paper introduces GLiSE, a tool designed to automate the extraction of grey literature relevant to software engineering research. The tool addresses the challenges of heterogeneous sources and formats, aiming to improve reproducibility and facilitate large-scale synthesis. The paper's significance lies in its potential to streamline the process of gathering and analyzing valuable information often missed by traditional academic venues, thus enriching software engineering research.
Reference

“GLiSE is a prompt-driven tool that turns a research topic prompt into platform-specific queries, gathers results from common software-engineering web sources (GitHub, Stack Overflow) and Google Search, and uses embedding-based semantic classifiers to filter and rank results according to their relevance.”

PermalinkArXiv
Paper#AI Security, Video Segmentation🔬 ResearchAnalyzed: Jan 3, 2026 20:15

Backdoor Attacks on Video Segmentation Models

Published:Dec 26, 2025 14:48
1 min read
ArXiv

Analysis

This paper addresses a critical security vulnerability in prompt-driven Video Segmentation Foundation Models (VSFMs), which are increasingly used in safety-critical applications. It highlights the ineffectiveness of existing backdoor attack methods and proposes a novel, two-stage framework (BadVSFM) specifically designed to inject backdoors into these models. The research is significant because it reveals a previously unexplored vulnerability and demonstrates the potential for malicious actors to compromise VSFMs, potentially leading to serious consequences in applications like autonomous driving.
Reference

“BadVSFM achieves strong, controllable backdoor effects under diverse triggers and prompts while preserving clean segmentation quality.”

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Research#LLM🔬 ResearchAnalyzed: Jan 10, 2026 12:21

Fine-Grained Chinese Hate Speech Detection: A Prompt-Driven LLM Merge Approach

Published:Dec 10, 2025 11:58
1 min read
ArXiv

Analysis

This research explores merging large language models (LLMs) to enhance fine-grained hate speech detection in Chinese, a crucial area for mitigating online toxicity. The work's reliance on prompt engineering for the merged LLMs warrants further investigation into its robustness and generalizability across diverse data distributions.
Reference

“The study focuses on prompt-driven LLM merge for fine-grained Chinese hate speech detection.”

PermalinkArXiv