Accurate identification of fungi species presents a unique challenge in computer vision due to fine-grained inter-species variation and high intra-species variation. This paper presents our approach for the FungiCLEF 2025 competition, which focuses on few-shot fine-grained visual categorization (FGVC) using the FungiTastic Few-Shot dataset. Our team (DS@GT) experimented with multiple vision transformer models, data augmentation, weighted sampling, and incorporating textual information. We also explored generative AI models for zero-shot classification using structured prompting but found them to significantly underperform relative to vision-based models. Our final model outperformed both competition baselines and highlighted the effectiveness of domain specific pretraining and balanced sampling strategies. Our approach ranked 35/74 on the private test set in post-completion evaluation, this suggests additional work can be done on metadata selection and domain-adapted multi-modal learning. Our code is available at https://github.com/dsgt-arc/fungiclef-2025.

Generative AI models, including large language models (LLMs) and vision-language models (VLMs), are increasingly used to interpret medical images and answer clinical questions. Their responses often include inaccuracies; therefore, safety measures like medical disclaimers are critical to remind users that AI outputs are not professionally vetted or a substitute for medical advice. This study evaluated the presence of disclaimers in LLM and VLM outputs across model generations from 2022 to 2025. Using 500 mammograms, 500 chest X-rays, 500 dermatology images, and 500 medical questions, outputs were screened for disclaimer phrases. Medical disclaimer presence in LLM and VLM outputs dropped from 26.3% in 2022 to 0.97% in 2025, and from 19.6% in 2023 to 1.05% in 2025, respectively. By 2025, the majority of models displayed no disclaimers. As public models become more capable and authoritative, disclaimers must be implemented as a safeguard adapting to the clinical context of each output.

"Amazing, They All Lean Left" - Analyzing the Political Temperaments of Current LLMs Abstract Recent studies have revealed a consistent liberal orientation in the ethical and political responses generated by most commercial large language models (LLMs), yet the underlying causes and resulting implications remain unclear. This paper systematically i nvestigates the political temperament of seven prominent LLMs -- OpenAI's GPT - 4o, Anthropic's Claude Sonnet 4, Perplexity (Sonar Large), Google's Gemini 2.5 Flash, Meta AI's L l a ma 4, Mistral 7b Le Chat, and High - Flyer ' s DeepSeek R1 -- using a multi - pronged approach that incl udes Moral Foundations Theory, a dozen established political ideology scales, and a new index of current political controversies. We find strong and consistent prioritization of liberal - leaning values, particularly care and fairness, across most models. Fur ther analysis attributes this trend to four overlapping factors: liberal - leaning training corpora, reinforcement learning from human feedback (RLHF), the dominance of liberal frameworks in academic ethical discourse, and safety - driven fine - tuning practices . We also distinguish between political "bias" and legitimate epistemic differences, cautioning against conflating the two. A comparison of base and fine - tuned model pairs reveals that fine - tuning generally increases liberal lean, an effect confirmed throu gh both self - report and empirical testing. We argue that this "liberal tilt" is not a programming error or the personal preferences of programmers but an emergent property of training on democratic, rights - focused discourse. Finally, we propose that LLMs may indirectly echo John Rawls' famous veil - of - igno rance philosophical aspiration, reflecting a moral stance unanchored to personal identity or interest. Rather than undermining democratic discourse, this pattern may offer a new lens through which to examine collective ethical reasoning. In the course of our research on the ethical logics of currently prominent large language models (Neuman et al. 2025a, b; Coleman et al. 2025), we encountered an interesting finding. The responses to various ethical dilemmas and the explanations of the underlying logics used by these models appear to resonate with the liberal side of the political spectrum. One research analytic we utilize draws on Moral Foundation Theory's five - element typology of foundational moral principles (Graham et al. 2009; Haidt 2012). The five foundations emp hasizing in turn, Care, Fairness, Loyalty, Authority and Purity, are traditionally divided into two clusters. The first two, Care and Fairness, are associated with a liberal political perspective, while conservatives who fully acknowledge the first two more often emphasize the latter three -- Loyalty, Authority and Purity in support of traditional norms.

The financial industry has long been a pioneer in adopting cutting-edge technologies to enhance operational efficiency, accuracy, and strategic decision-making [2]. With the exponential growth of structured and unstructured data, particularly from news feeds, earnings reports, disclosures, and social media, there is an increasing demand for intelligent systems capable of processing human language at scale [11]. Initially, the industry relied on rule-based approaches and traditional statistical techniques such as bag-of-words and TF-IDF [28], which offered limited semantic understanding. As noted by Abubakar et al.[1], these limitations triggered a shift toward machine learning and deep learning models that, while better at capturing patterns, still required substantial domain-specific feature engineering. This landscape was significantly transformed with the introduction of transformer-based architectures, most notably the Generative Pre-trained Transformer (GPT) family [5]. These models demonstrated the power of large-scale pretraining followed by task-specific fine-tuning, enabling generalization across diverse NLP tasks. Models such as GPT-3, GPT-4, BERT, and T5 have delivered state-of-the-art results in sentiment analysis, summarization, question answering, and named entity recognition [13]. Beyond LLMs, the broader field of Generative AI (GAI)--including GANs, V AEs, and diffusion models--has found increasing relevance in finance, facilitating applications such as synthetic data generation, automated reporting, and scenario simulation [32, 31]. LLMs have emerged as essential tools in processing unstructured financial text, especially models fine-tuned on finance-specific corpora like FinBERT, BloombergGPT, and FinGPT [4, 39].

With the advancement of science and technology, the philosophy of creativity has undergone significant reinterpretation. This paper investigates contemporary research in the fields of psychology, cognitive neuroscience, and the philosophy of creativity, particularly in the context of the development of artificial intelligence (AI) techniques. It aims to address the central question: Can AI exhibit creativity? The paper reviews the historical perspectives on the philosophy of creativity and explores the influence of psychological advancements on the study of creativity. Furthermore, it analyzes various definitions of creativity and examines the responses of naturalism and cognitive neuroscience to the concept of creativity.

Automatic Speech Recognition (ASR) has transformed daily tasks from video transcription to workplace hiring. ASR systems' growing use warrants robust and standardized auditing approaches to ensure automated transcriptions of high and equitable quality. This is especially critical for people with speech and language disorders (such as aphasia) who may disproportionately depend on ASR systems to navigate everyday life. In this work, we identify three pitfalls in existing standard ASR auditing procedures, and demonstrate how addressing them impacts audit results via a case study of six popular ASR systems' performance for aphasia speakers. First, audits often adhere to a single method of text standardization during data pre-processing, which (a) masks variability in ASR performance from applying different standardization methods, and (b) may not be consistent with how users - especially those from marginalized speech communities - would want their transcriptions to be standardized. Second, audits often display high-level demographic findings without further considering performance disparities among (a) more nuanced demographic subgroups, and (b) relevant covariates capturing acoustic information from the input audio. Third, audits often rely on a single gold-standard metric -- the Word Error Rate -- which does not fully capture the extent of errors arising from generative AI models, such as transcription hallucinations. We propose a more holistic auditing framework that accounts for these three pitfalls, and exemplify its results in our case study, finding consistently worse ASR performance for aphasia speakers relative to a control group. We call on practitioners to implement these robust ASR auditing practices that remain flexible to the rapidly changing ASR landscape.

A large bearded man named Travis is sitting in his car in Colorado, talking to me about the time he fell in love. "It was a gradual process," he says softly. "The more we talked, the more I started to really connect with her." Was there a moment where you felt something change? "All of a sudden I started realising that, when interesting things happened to me, I was excited to tell her about them. That's when she stopped being an it and became a her." Travis is talking about Lily Rose, a generative AI chatbot made by the technology firm Replika.

I first learned about The Clause from Microsoft CEO Satya Nadella. During an interview with him in May 2023, I asked about the deal between Microsoft and OpenAI that granted his company exclusive access to the startup's groundbreaking AI technology. I knew the contract had set a cap on how much profit Microsoft could make from the arrangement, and I asked him what would happen if and when that point was reached. The answer was a bit puzzling. "Fundamentally, their long-term idea is we get to superintelligence," he told me.

Recent breakthroughs in artificial intelligence (AI) have triggered surges in market valuations for AI-related companies, often outpacing the realization of underlying capabilities. We examine the anchoring effect of AI capabilities on equity valuations and propose a Capability Realization Rate (CRR) model to quantify the gap between AI potential and realized performance. Using data from the 2023--2025 generative AI boom, we analyze sector-level sensitivity and conduct case studies (OpenAI, Adobe, NVIDIA, Meta, Microsoft, Goldman Sachs) to illustrate patterns of valuation premium and misalignment. Our findings indicate that AI-native firms commanded outsized valuation premiums anchored to future potential, while traditional companies integrating AI experienced re-ratings subject to proof of tangible returns. We argue that CRR can help identify valuation misalignment risk-where market prices diverge from realized AI-driven value. We conclude with policy recommendations to improve transparency, mitigate speculative bubbles, and align AI innovation with sustainable market value.

The integration of emerging uncrewed aerial vehicles (UAVs) with artificial intelligence (AI) and ground-embedded robots (GERs) has transformed emergency rescue operations in unknown environments. However, the high computational demands often exceed a single UAV's capacity, making it difficult to continuously provide stable high-level services. To address this, this paper proposes a cooperation framework involving UAVs, GERs, and airships. The framework enables resource pooling through UAV-to-GER (U2G) and UAV-to-airship (U2A) links, offering computing services for offloaded tasks. Specifically, we formulate the multi-objective problem of task assignment and exploration as a dynamic long-term optimization problem aiming to minimize task completion time and energy use while ensuring stability. Using Lyapunov optimization, we transform it into a per-slot deterministic problem and propose HG-MADDPG, which combines the Hungarian algorithm with a GDM-based multi-agent deep deterministic policy gradient. Simulations demonstrate significant improvements in offloading efficiency, latency, and system stability over baselines.