Papers with continuous
LibVulnWatch: A Deep Assessment Agent System and Leaderboard for Uncovering Hidden Vulnerabilities in Open-Source AI Libraries (2025.acl-srw)
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Zekun Wu, Seonglae Cho, Umar Mohammed, Cristian Enrique Munoz Villalobos, Kleyton Da Costa, Xin Guan, Theo King, Ze Wang, Emre Kazim, Adriano Koshiyama
| Challenge: | Open-source AI libraries present significant, underexamined risks spanning security, licensing, maintenance, supply chain integrity, and regulatory compliance. |
| Approach: | They propose a system that leverages large language models and agentic workflows to perform deep, evidence-based evaluations of open-source AI libraries. |
| Outcome: | The proposed system covers up to 88% of OpenSSF Scorecard checks and uncovers 19 additional risks per library. |
AiraXiv: An AI-Driven Open-Access Platform for Human and AI Scientists (2026.acl-demo)
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| Challenge: | Recent advances in artificial intelligence (AI) have accelerated the growth of both human-authored and AI-generated research outputs. |
| Approach: | They propose an AI-driven open-access platform built on open preprints, AI-augmented analysis and review, and reader feedback. |
| Outcome: | The proposed platform supports human scientists through an interactive UI and AI scientists through Model Context Protocol (MCP)-based interactions. |
Unsupervised Discontinuous Constituency Parsing with Mildly Context-Sensitive Grammars (2023.acl-long)
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| Challenge: | a recent study shows that context-free grammars are not natural for modeling discontinuous language phenomena such as extrapositions and cross-serial dependencies. |
| Approach: | They propose a grammar induction approach with mildly context-sensitive grammars for unsupervised discontinuous parsing. |
| Outcome: | Experiments on German and Dutch show that the proposed grammar induction method is beneficial for unsupervised parsing. |
Aligning Black-box Language Models with Human Judgments (2025.findings-naacl)
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| Challenge: | Large language models (LLMs) are increasingly used as automated judges to evaluate recommendation systems, search engines, and other subjective tasks. |
| Approach: | They propose a framework to align LLM judgments with individual human evaluators or their aggregated judgments without retraining or fine-tuning the LLM. |
| Outcome: | The proposed framework achieves 142% improvement in agreement across 29 tasks and exceeds inter-human agreement on four out of six tasks. |
CIKT: A Collaborative and Iterative Knowledge Tracing Framework with Large Language Models (2025.emnlp-main)
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| Challenge: | Knowledge Tracing (KT) aims to model a student’s learning state over time and predict their future performance. |
| Approach: | They propose a framework that harnesses Large Language Models to enhance both prediction accuracy and explainability by a synergistic optimization loop. |
| Outcome: | The proposed framework improves both prediction accuracy and explainability by using a synergistic optimization loop. |