We present a novel approach for test-time adaptation via online self-training, consisting of two components. First, we introduce a statistical framework that detects distribution shifts in the classifier's entropy values obtained on a stream of unlabeled samples. Second, we devise an online adaptation mechanism that utilizes the evidence of distribution shifts captured by the detection tool to dynamically update the classifier's parameters. The resulting adaptation process drives the distribution of test entropy values obtained from the self-trained classifier to match those of the source domain, building invariance to distribution shifts. This approach departs from the conventional self-training method, which focuses on minimizing the classifier's entropy. Our approach combines concepts in betting martingales and online learning to form a detection tool capable of quickly reacting to distribution shifts. We then reveal a tight relation between our adaptation scheme and optimal transport, which forms the basis of our novel self-supervised loss. Experimental results demonstrate that our approach improves test-time accuracy under distribution shifts while maintaining accuracy and calibration in their absence, outperforming leading entropy minimization methods across various scenarios.

Sensitive information leakage in code repositories has emerged as a critical security challenge. Traditional detection methods that rely on regular expressions, fingerprint features, and high-entropy calculations often suffer from high false-positive rates. This not only reduces detection efficiency but also significantly increases the manual screening burden on developers. Recent advances in large language models (LLMs) and multi-agent collaborative architectures have demonstrated remarkable potential for tackling complex tasks, offering a novel technological perspective for sensitive information detection. In response to these challenges, we propose Argus, a multi-agent collaborative framework for detecting sensitive information. Argus employs a three-tier detection mechanism that integrates key content, file context, and project reference relationships to effectively reduce false positives and enhance overall detection accuracy. To comprehensively evaluate Argus in real-world repository environments, we developed two new benchmarks, one to assess genuine leak detection capabilities and another to evaluate false-positive filtering performance. Experimental results show that Argus achieves up to 94.86% accuracy in leak detection, with a precision of 96.36%, recall of 94.64%, and an F1 score of 0.955. Moreover, the analysis of 97 real repositories incurred a total cost of only 2.2$. All code implementations and related datasets are publicly available at https://github.com/TheBinKing/Argus-Guard for further research and application.

As the use of AI tools by students has become more prevalent, instructors have started using AI detection tools like GPTZero and QuillBot to detect AI written text. However, the reliability of these detectors remains uncertain. In our study, we focused mostly on the success rate of GPTZero, the most-used AI detector, in identifying AI-generated texts based on different lengths of randomly submitted essays: short (40-100 word count), medium (100-350 word count), and long (350-800 word count). We gathered a data set consisting of twenty-eight AI-generated papers and fifty human-written papers. With this randomized essay data, papers were individually plugged into GPTZero and measured for percentage of AI generation and confidence. A vast majority of the AI-generated papers were detected accurately (ranging from 91-100% AI believed generation), while the human generated essays fluctuated; there were a handful of false positives. These findings suggest that although GPTZero is effective at detecting purely AI-generated content, its reliability in distinguishing human-authored texts is limited. Educators should therefore exercise caution when relying solely on AI detection tools.

We present a novel approach for test-time adaptation via online self-training, consisting of two components. First, we introduce a statistical framework that detects distribution shifts in the classifier's entropy values obtained on a stream of unlabeled samples. Second, we devise an online adaptation mechanism that utilizes the evidence of distribution shifts captured by the detection tool to dynamically update the classifier's parameters. The resulting adaptation process drives the distribution of test entropy values obtained from the self-trained classifier to match those of the source domain, building invariance to distribution shifts. This approach departs from the conventional self-training method, which focuses on minimizing the classifier's entropy.

The proliferation of generative AI and deceptive synthetic media threatens the global information ecosystem, especially across the Global Majority. This report from WITNESS highlights the limitations of current AI detection tools, which often underperform in real-world scenarios due to challenges related to explainability, fairness, accessibility, and contextual relevance. In response, WITNESS introduces the Truly Innovative and Effective AI Detection (TRIED) Benchmark, a new framework for evaluating detection tools based on their real-world impact and capacity for innovation. Drawing on frontline experiences, deceptive AI cases, and global consultations, the report outlines how detection tools must evolve to become truly innovative and relevant by meeting diverse linguistic, cultural, and technological contexts. It offers practical guidance for developers, policy actors, and standards bodies to design accountable, transparent, and user-centered detection solutions, and incorporate sociotechnical considerations into future AI standards, procedures and evaluation frameworks. By adopting the TRIED Benchmark, stakeholders can drive innovation, safeguard public trust, strengthen AI literacy, and contribute to a more resilient global information credibility.

ARTICLE TEMPLATE Beyond Detection: Designing AI-Resilient Assessments with Automated Feedback Tool to Foster Critical Thinking and Originality Muhammad Sajjad Akbar a a University of Sydney, Australia; ARTICLE HISTORY Compiled April 1, 2025 ABSTRACT The growing prevalence of generative AI tools such as ChatGPT has raised urgent concerns about their impact on student learning, particularly their potential to erode critical thinking and creativity in academic contexts. As students increasingly use these tools to complete assessments, foundational cognitive skills are at risk of being bypassed, challenging the integrity of higher education and the authenticity of student work. Current AI-generated text detection tools are fundamentally inadequate in addressing this challenge. They produce unreliable, unverifiable outputs and are highly susceptible to false positives and false negatives, especially when students apply obfuscation techniques such as paraphrasing, translation, or structural rewording. These tools rely on shallow statistical features rather than contextual or semantic understanding, making them unsuitable as definitive indicators of AI misuse. In response, this research proposes an AI-resilient, assessment-based solution that shifts focus from reactive detection to proactive assessment design. The solution is delivered through a web-based Python tool that integrates Bloom's Taxonomy with advanced natural language processing techniques including GPT-3.5 Turbo, BERT-based semantic similarity, and TF-IDF metrics to evaluate the AI-solvability of assignment tasks. By analyzing both surface-level and semantic features, the tool helps educators assess whether a task targets lower-order thinking (e.g., recall, summarization), which is more easily completed by AI, or higher-order skills (e.g., analysis, evaluation, creation), which are more resistant to AI automation. This framework empowers educators to intentionally design cognitively demanding AI-resistant assessments that promote originality, critical thinking, and fairness. By addressing the design of root issue assessment rather than relying on flawed detection tools, this research contributes a sustainable and pedagogically sound strategy to uphold academic standards and foster authentic learning in the era of AI. KEYWORDS Generative AI; ChatGPT; AI-resilient; Bloom's Taxonomy; Automated Assessments; AI-solvability;Automated Feedback; appendices 1. Introduction Integrating AI-technology with innovative thinking skills in higher education (HE) environment has grown more challenging due to rapid digital innovation and ubiquitous data availability. In applied education, innovative thinking is essential. It is charac-CONTACT Muhammad Sajjad Akbar. It entails thinking creatively to come up with original solutions to issues, enhance workflows, or open up new possibilities.

Multimodal large language models (MLLMs) show promise in tasks like visual question answering (VQA) but still face challenges in multimodal reasoning. Recent works adapt agentic frameworks or chain-of-thought (CoT) reasoning to improve performance. However, CoT-based multimodal reasoning often demands costly data annotation and fine-tuning, while agentic approaches relying on external tools risk introducing unreliable output from these tools. In this paper, we propose Seeing and Reasoning with Confidence (SRICE), a training-free multimodal reasoning framework that integrates external vision models with uncertainty quantification (UQ) into an MLLM to address these challenges. Specifically, SRICE guides the inference process by allowing MLLM to autonomously select regions of interest through multi-stage interactions with the help of external tools. We propose to use a conformal prediction-based approach to calibrate the output of external tools and select the optimal tool by estimating the uncertainty of an MLLM's output. Our experiment shows that the average improvement of SRICE over the base MLLM is 4.6% on five datasets and the performance on some datasets even outperforms fine-tuning-based methods, revealing the significance of ensuring reliable tool use in an MLLM agent.

The email arrived out of the blue: it was the university code of conduct team. Albert, a 19-year-old undergraduate English student, scanned the content, stunned. He had been accused of using artificial intelligence to complete a piece of assessed work. If he did not attend a hearing to address the claims made by his professor, or respond to the email, he would receive an automatic fail on the module. The problem was, he hadn't cheated. The Guardian's journalism is independent.

The rapid advancement of large language models (LLMs) has enabled the ability to effectively analyze and generate code nearly instantaneously, resulting in their widespread adoption in software development. Following this advancement, researchers and companies have begun integrating LLMs across the hardware design and verification process. However, these highly potent LLMs can also induce new attack scenarios upon security vulnerabilities across the hardware development process. One such attack vector that has not been explored is intellectual property (IP) piracy. Given that this attack can manifest as rewriting hardware designs to evade piracy detection, it is essential to thoroughly evaluate LLM capabilities in performing this task and assess the mitigation abilities of current IP piracy detection tools. Therefore, in this work, we propose LLMPirate, the first LLM-based technique able to generate pirated variations of circuit designs that successfully evade detection across multiple state-of-the-art piracy detection tools. We devise three solutions to overcome challenges related to integration of LLMs for hardware circuit designs, scalability to large circuits, and effectiveness, resulting in an end-to-end automated, efficient, and practical formulation. We perform an extensive experimental evaluation of LLMPirate using eight LLMs of varying sizes and capabilities and assess their performance in pirating various circuit designs against four state-of-the-art, widely-used piracy detection tools. Our experiments demonstrate that LLMPirate is able to consistently evade detection on 100% of tested circuits across every detection tool. Additionally, we showcase the ramifications of LLMPirate using case studies on IBEX and MOR1KX processors and a GPS module, that we successfully pirate. We envision that our work motivates and fosters the development of better IP piracy detection tools.

Nowadays, it's easy to get fooled by AI content on the web. Whether it's a picture of the Pope sporting a puffy Balenciaga jacket or Trump getting tackled and arrested, these AI-generated images appear super realistic (as long as you're not looking too close), so it can be hard to separate fact from fiction. This is because AI doesn't really understand context in the cultural or historical sense. While some AI-generated images are harmless and do not spread misinformation, others, especially ones involving celebrities or politicians, can cause a great deal of damage and brain rot. Heck, I consider myself to be a relatively tech-savvy person and even I've been fooled once or twice.