❌

Normal view

Received — 30 December 2025 ⏭ Favourites

DarkPatterns-LLM: A Multi-Layer Benchmark for Detecting Manipulative and Harmful AI Behavior

✇cs.AI, q-bio.NC updates on arXiv.org
By: Sadia Asif · Israel Antonio Rosales Laguan · Haris Khan · Shumaila Asif · Muneeb Asif
30 December 2025 at 13:00
arXiv:2512.22470v1 Announce Type: new Abstract: The proliferation of Large Language Models (LLMs) has intensified concerns about manipulative or deceptive behaviors that can undermine user autonomy, trust, and well-being. Existing safety benchmarks predominantly rely on coarse binary labels and fail to capture the nuanced psychological and social mechanisms constituting manipulation. We introduce \textbf{DarkPatterns-LLM}, a comprehensive benchmark dataset and diagnostic framework for fine-grained assessment of manipulative content in LLM outputs across seven harm categories: Legal/Power, Psychological, Emotional, Physical, Autonomy, Economic, and Societal Harm. Our framework implements a four-layer analytical pipeline comprising Multi-Granular Detection (MGD), Multi-Scale Intent Analysis (MSIAN), Threat Harmonization Protocol (THP), and Deep Contextual Risk Alignment (DCRA). The dataset contains 401 meticulously curated examples with instruction-response pairs and expert annotations. Through evaluation of state-of-the-art models including GPT-4, Claude 3.5, and LLaMA-3-70B, we observe significant performance disparities (65.2\%--89.7\%) and consistent weaknesses in detecting autonomy-undermining patterns. DarkPatterns-LLM establishes the first standardized, multi-dimensional benchmark for manipulation detection in LLMs, offering actionable diagnostics toward more trustworthy AI systems.

Why AI Safety Requires Uncertainty, Incomplete Preferences, and Non-Archimedean Utilities

✇cs.AI, q-bio.NC updates on arXiv.org
By: Alessio Benavoli · Alessandro Facchini · Marco Zaffalon
30 December 2025 at 13:00
arXiv:2512.23508v1 Announce Type: new Abstract: How can we ensure that AI systems are aligned with human values and remain safe? We can study this problem through the frameworks of the AI assistance and the AI shutdown games. The AI assistance problem concerns designing an AI agent that helps a human to maximise their utility function(s). However, only the human knows these function(s); the AI assistant must learn them. The shutdown problem instead concerns designing AI agents that: shut down when a shutdown button is pressed; neither try to prevent nor cause the pressing of the shutdown button; and otherwise accomplish their task competently. In this paper, we show that addressing these challenges requires AI agents that can reason under uncertainty and handle both incomplete and non-Archimedean preferences.

Interpretable Link Prediction in AI-Driven Cancer Research: Uncovering Co-Authorship Patterns

✇cs.AI, q-bio.NC updates on arXiv.org
By: Shahab Mosallaie · Andrea Schiffauerova · Ashkan Ebadi
30 December 2025 at 13:00
arXiv:2512.22181v1 Announce Type: cross Abstract: Artificial intelligence (AI) is transforming cancer diagnosis and treatment. The intricate nature of this disease necessitates the collaboration of diverse stakeholders with varied expertise to ensure the effectiveness of cancer research. Despite its importance, forming effective interdisciplinary research teams remains challenging. Understanding and predicting collaboration patterns can help researchers, organizations, and policymakers optimize resources and foster impactful research. We examined co-authorship networks as a proxy for collaboration within AI-driven cancer research. Using 7,738 publications (2000-2017) from Scopus, we constructed 36 overlapping co-authorship networks representing new, persistent, and discontinued collaborations. We engineered both attribute-based and structure-based features and built four machine learning classifiers. Model interpretability was performed using Shapley Additive Explanations (SHAP). Random forest achieved the highest recall for all three types of examined collaborations. The discipline similarity score emerged as a crucial factor, positively affecting new and persistent patterns while negatively impacting discontinued collaborations. Additionally, high productivity and seniority were positively associated with discontinued links. Our findings can guide the formation of effective research teams, enhance interdisciplinary cooperation, and inform strategic policy decisions.

LLM-Guided Exemplar Selection for Few-Shot Wearable-Sensor Human Activity Recognition

✇cs.AI, q-bio.NC updates on arXiv.org
By: Elsen Ronando · Sozo Inoue
30 December 2025 at 13:00
arXiv:2512.22385v1 Announce Type: cross Abstract: In this paper, we propose an LLM-Guided Exemplar Selection framework to address a key limitation in state-of-the-art Human Activity Recognition (HAR) methods: their reliance on large labeled datasets and purely geometric exemplar selection, which often fail to distinguish similar weara-ble sensor activities such as walking, walking upstairs, and walking downstairs. Our method incorporates semantic reasoning via an LLM-generated knowledge prior that captures feature importance, inter-class confusability, and exemplar budget multipliers, and uses it to guide exemplar scoring and selection. These priors are combined with margin-based validation cues, PageRank centrality, hubness penalization, and facility-location optimization to obtain a compact and informative set of exemplars. Evaluated on the UCI-HAR dataset under strict few-shot conditions, the framework achieves a macro F1-score of 88.78%, outperforming classical approaches such as random sampling, herding, and $k$-center. The results show that LLM-derived semantic priors, when integrated with structural and geometric cues, provide a stronger foundation for selecting representative sensor exemplars in few-shot wearable-sensor HAR.

MedGemma vs GPT-4: Open-Source and Proprietary Zero-shot Medical Disease Classification from Images

✇cs.AI, q-bio.NC updates on arXiv.org
By: Md. Sazzadul Islam Prottasha · Nabil Walid Rafi
30 December 2025 at 13:00
arXiv:2512.23304v1 Announce Type: cross Abstract: Multimodal Large Language Models (LLMs) introduce an emerging paradigm for medical imaging by interpreting scans through the lens of extensive clinical knowledge, offering a transformative approach to disease classification. This study presents a critical comparison between two fundamentally different AI architectures: the specialized open-source agent MedGemma and the proprietary large multimodal model GPT-4 for diagnosing six different diseases. The MedGemma-4b-it model, fine-tuned using Low-Rank Adaptation (LoRA), demonstrated superior diagnostic capability by achieving a mean test accuracy of 80.37% compared to 69.58% for the untuned GPT-4. Furthermore, MedGemma exhibited notably higher sensitivity in high-stakes clinical tasks, such as cancer and pneumonia detection. Quantitative analysis via confusion matrices and classification reports provides comprehensive insights into model performance across all categories. These results emphasize that domain-specific fine-tuning is essential for minimizing hallucinations in clinical implementation, positioning MedGemma as a sophisticated tool for complex, evidence-based medical reasoning.

Generating Verifiable Chain of Thoughts from Exection-Traces

✇cs.AI, q-bio.NC updates on arXiv.org
By: Shailja Thakur · Vaibhav Saxena · Rohan Kulkarni · Shivdeep Singh · Parameswaran Selvam · Hima Patel · Hiroshi Kanayama
30 December 2025 at 13:00
arXiv:2512.00127v2 Announce Type: replace-cross Abstract: Teaching language models to reason about code execution remains a fundamental challenge. While Chain-of-Thought (CoT) prompting has shown promise, current synthetic training data suffers from a critical weakness: the reasoning steps are often plausible-sounding explanations generated by teacher models, not verifiable accounts of what the code actually does. This creates a troubling failure mode where models learn to mimic superficially convincing but logically flawed reasoning patterns. We address this by grounding CoT generation directly in program execution traces. Our pipeline instruments code to capture its dynamic behavior, then narrates these execution traces into natural language and factually-grounded rationales that are verifiable by design. This execution-grounded approach ensures every reasoning step reflects what the program computes, eliminating logical hallucinations at the source. We evaluate our method on code reasoning tasks, code generation and explanation tasks from HumanEval. Models trained on our bi-directional trace-grounded data achieve substantial improvements on reasoning tasks, with gains of up to 30 points on output prediction and 28 points on input prediction over base models, alongside competitive explanation and code generation performance. https://github.ibm.com/IBM-Research-AI/Verified-Code-CoT

Metabolic signatures in gastroenteropancreatic neuroendocrine neoplasms: unraveling diagnostic and prognostic insights

✇(Multiomics OR Omics) AND (Lung OR gastric OR Hepatocellular)
By: Reydson Alcides de Lima-Souza · Tayná Figueiredo Maciel · Moisés Willian Aparecido Gonçalves · Fernanda Cristina Poscai Ribeiro · Rodrigo A Maioral · João Figueira Scarini · Heloisa P Soraes · Gary Chris Fillmore · Erika Said Abu Egal
29 December 2025 at 19:00

Front Endocrinol (Lausanne). 2025 Dec 11;16:1676021. doi: 10.3389/fendo.2025.1676021. eCollection 2025.

ABSTRACT

Gastroenteropancreatic neuroendocrine neoplasms (GEP-NENs) are a heterogeneous group of tumors characterized by diverse biological behaviors and variable clinical outcomes. Recent advances have highlighted the important role of metabolic reprogramming in tumorigenesis, progression, and therapeutic resistance in GEP-NENs. In this review, we synthesize the current evidence on metabolic biomarkers and altered metabolic pathways-particularly those involving glucose, lipid, and amino acid metabolism. Key biomarkers such as GLUT-1, FASN, and enzymes involved in ferroptosis, cholesterol biosynthesis, and amino acid catabolism demonstrate strong associations with tumor aggressiveness, hypoxia, and mTOR signaling. Moreover, metabolomic profiling and functional studies suggest that metabolic markers may inform prognosis and predict response to targeted therapies such as Everolimus. Although promising, the clinical translation of these markers is still limited and requires further validation in large, subtype-specific cohorts. Our findings highlight the importance of integrating metabolic profiling into the diagnostic and therapeutic landscape of GEP-NENs. Future research should prioritize biomarker standardization, multi-omics integration, and the development of metabolism-based therapeutic strategies tailored to tumor subtype and differentiation grade.

PMID:41458541 | PMC:PMC12738315 | DOI:10.3389/fendo.2025.1676021

There are no more articles
❌