OpenRaiser/Intern-Atlas huggingface dataset:
Zero-shot Performance of Generative AI in Brazilian Portuguese Medical Exam
Venue: arXiv cs.CL 2025
Year: 2025

Artificial intelligence (AI) has shown the potential to revolutionize healthcare by improving diagnostic accuracy, optimizing workflows, and personalizing treatment plans. Large Language Models (LLMs) and Multimodal Large Language Models (MLLMs) have achieved notable advancements in natural language processing and medical applications. However, the evaluation of these models has focused predominantly on the English language, leading to potential biases in their performance across different languages. This study investigates the capability of six LLMs (GPT-4.0 Turbo, LLaMA-3-8B, LLaMA-3-70B, Mixtral 8x7B Instruct, Titan Text G1-Express, and Command R+) and four MLLMs (Claude-3.5-Sonnet, Claude-3-Opus, Claude-3-Sonnet, and Claude-3-Haiku) to answer questions written in Brazilian spoken portuguese from the medical residency entrance exam of the Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo (HCFMUSP) - the largest health complex in South America. The performance of the models was benchmarked against human candidates, analyzing accuracy, processing time, and coherence of the generated explanations. The results show that while some models, particularly Claude-3.5-Sonnet and Claude-3-Opus, achieved accuracy levels comparable to human candidates, performance gaps persist, particularly in multimodal questions requiring image interpretation. Furthermore, the study highlights language disparities, emphasizing the need for further fine-tuning and data set augmentation for non-English medical AI applications. Our findings reinforce the importance of evaluating generative AI in various linguistic and clinical settings to ensure a fair and reliable deployment in healthcare. Future research should explore improved training methodologies, improved multimodal reasoning, and real-world clinical integration of AI-driven medical assistance.
TS-Insight: Visualizing Thompson Sampling for Verification and XAI
Venue: arXiv cs.HC 2025
Year: 2025

Thompson Sampling (TS) and its variants are powerful Multi-Armed Bandit algorithms used to balance exploration and exploitation strategies in active learning. Yet, their probabilistic nature often turns them into a "black box", hindering debugging and trust. We introduce TS-Insight, a visual analytics tool explicitly designed to shed light on the internal decision mechanisms of Thompson Sampling-based algorithms, for model developers. It comprises multiple plots, tracing for each arm the evolving posteriors, evidence counts, and sampling outcomes, enabling the verification, diagnosis, and explainability of exploration/exploitation dynamics. This tool aims at fostering trust and facilitating effective debugging and deployment in complex binary decision-making scenarios especially in sensitive domains requiring interpretable decision-making.
A Gold Standard Dataset and Evaluation Framework for Depression Detection and Explanation in Social Media using LLMs
Venue: arXiv cs.CL 2025
Year: 2025

Early detection of depression from online social media posts holds promise for providing timely mental health interventions. In this work, we present a high-quality, expert-annotated dataset of 1,017 social media posts labeled with depressive spans and mapped to 12 depression symptom categories. Unlike prior datasets that primarily offer coarse post-level labels \cite{cohan-etal-2018-smhd}, our dataset enables fine-grained evaluation of both model predictions and generated explanations. We develop an evaluation framework that leverages this clinically grounded dataset to assess the faithfulness and quality of natural language explanations generated by large language models (LLMs). Through carefully designed prompting strategies, including zero-shot and few-shot approaches with domain-adapted examples, we evaluate state-of-the-art proprietary LLMs including GPT-4.1, Gemini 2.5 Pro, and Claude 3.7 Sonnet. Our comprehensive empirical analysis reveals significant differences in how these models perform on clinical explanation tasks, with zero-shot and few-shot prompting. Our findings underscore the value of human expertise in guiding LLM behavior and offer a step toward safer, more transparent AI systems for psychological well-being.
LLMControl: Grounded Control of Text-to-Image Diffusion-based Synthesis with Multimodal LLMs
Venue: arXiv cs.CV 2025
Year: 2025

Recent spatial control methods for text-to-image (T2I) diffusion models have shown compelling results. However, these methods still fail to precisely follow the control conditions and generate the corresponding images, especially when encountering the textual prompts that contain multiple objects or have complex spatial compositions. In this work, we present a LLM-guided framework called LLM\_Control to address the challenges of the controllable T2I generation task. By improving grounding capabilities, LLM\_Control is introduced to accurately modulate the pre-trained diffusion models, where visual conditions and textual prompts influence the structures and appearance generation in a complementary way. We utilize the multimodal LLM as a global controller to arrange spatial layouts, augment semantic descriptions and bind object attributes. The obtained control signals are injected into the denoising network to refocus and enhance attention maps according to novel sampling constraints. Extensive qualitative and quantitative experiments have demonstrated that LLM\_Control achieves competitive synthesis quality compared to other state-of-the-art methods across various pre-trained T2I models. It is noteworthy that LLM\_Control allows the challenging input conditions on which most of the existing methods
AF-CLIP: Zero-Shot Anomaly Detection via Anomaly-Focused CLIP Adaptation
Venue: arXiv cs.CV 2025
Year: 2025

Visual anomaly detection has been widely used in industrial inspection and medical diagnosis. Existing methods typically demand substantial training samples, limiting their utility in zero-/few-shot scenarios. While recent efforts have leveraged CLIP's zero-shot recognition capability for this task, they often ignore optimizing visual features to focus on local anomalies, reducing their efficacy. In this work, we propose AF-CLIP (Anomaly-Focused CLIP) by dramatically enhancing its visual representations to focus on local defects. Our approach introduces a lightweight adapter that emphasizes anomaly-relevant patterns in visual features, simultaneously optimizing both class-level features for image classification and patch-level features for precise localization. To capture anomalies of different sizes and improve detection accuracy, prior to the adapter, we develop a multi-scale spatial aggregation mechanism to effectively consolidate neighborhood context. Complementing these visual enhancements, we design learnable textual prompts that generically characterize normal and abnormal states. After optimization on auxiliary datasets using a composite objective function, AF-CLIP demonstrates strong zero-shot detection capability. Our method is also extended to few-shot scenarios by extra memory banks. Experimental results across diverse industrial and medical datasets demonstrate the effectiveness and generalization of our proposed method. Code is available at https://github.com/Faustinaqq/AF-CLIP.
PDLogger: Automated Logging Framework for Practical Software Development
Venue: arXiv cs.SE 2025
Year: 2025

Logging is indispensable for maintaining the reliability and diagnosability of modern software, yet developers still struggle to decide where and how to log effectively. Existing automated logging techniques focus on isolated sub-tasks - predicting a single log position, level, or message - and therefore cannot produce complete, high-quality log statements that reflect real-world practice in which multiple logs often appear inside one method. They also neglect deeper semantic dependencies among methods and consider only a narrow set of candidate variables, leading to superficial or incomplete logs. In this paper, we present PDLogger, the first end-to-end log generation technique expressly designed for practical, multi-log scenarios. PDLogger operates in three phases. (1) Log position prediction: block-type-aware structured prompts guide a large language model (LLM) to suggest candidate positions across all control-flow blocks of a method. (2) Log generation: backward program slicing supplies precise inter-procedural control and data-dependency context, while an expanded variable extractor captures both member and external function expressions; the enriched prompt enables the LLM to emit a full log statement (position, level, message, variables). (3) Log refinement: level correction and context-sensitive deduplication prune false positives and redundant logs. We evaluate PDLogger on 3,113 log statements drawn from two widely used Java projects. Compared with the strongest prior systems, PDLogger improves log-position precision by 139.0 percent, F1 by 69.2 percent, level accuracy by 82.3 percent, variable precision by 131.8 percent, and message quality (BERTScore) by 65.7 percent. The framework consistently performs well with different mainstream LLMs, demonstrating robustness and generality. PDLogger's implementation is available as open source to foster future research and adoption.
Predicting Brain Responses To Natural Movies With Multimodal LLMs
Venue: arXiv cs.CV 2025
Year: 2025

We present MedARC's team solution to the Algonauts 2025 challenge. Our pipeline leveraged rich multimodal representations from various state-of-the-art pretrained models across video (V-JEPA2), speech (Whisper), text (Llama 3.2), vision-text (InternVL3), and vision-text-audio (Qwen2.5-Omni). These features extracted from the models were linearly projected to a latent space, temporally aligned to the fMRI time series, and finally mapped to cortical parcels through a lightweight encoder comprising a shared group head plus subject-specific residual heads. We trained hundreds of model variants across hyperparameter settings, validated them on held-out movies and assembled ensembles targeted to each parcel in each subject. Our final submission achieved a mean Pearson's correlation of 0.2085 on the test split of withheld out-of-distribution movies, placing our team in fourth place for the competition. We further discuss a last-minute optimization that would have raised us to second place. Our results highlight how combining features from models trained in different modalities, using a simple architecture consisting of shared-subject and single-subject components, and conducting comprehensive model selection and ensembling improves generalization of encoding models to novel movie stimuli. All code is available on GitHub.
What Does 'Human-Centred AI' Mean?
Venue: arXiv cs.AI 2025
Year: 2025

While it seems sensible that human-centred artificial intelligence (AI) means centring "human behaviour and experience," it cannot be any other way. AI, I argue, is usefully seen as a relationship between technology and humans where it appears that artifacts can perform, to a greater or lesser extent, human cognitive labour. This is evinced using examples that juxtapose technology with cognition, inter alia: abacus versus mental arithmetic; alarm clock versus knocker-upper; camera versus vision; and sweatshop versus tailor. Using novel definitions and analyses, sociotechnical relationships can be analysed into varying types of: displacement (harmful), enhancement (beneficial), and/or replacement (neutral) of human cognitive labour. Ultimately, all AI implicates human cognition; no matter what. Obfuscation of cognition in the AI context -- from clocks to artificial neural networks -- results in distortion, in slowing critical engagement, perverting cognitive science, and indeed in limiting our ability to truly centre humans and humanity in the engineering of AI systems. To even begin to de-fetishise AI, we must look the human-in-the-loop in the eyes.
A Scalable Resource Management Layer for FPGA SoCs in 6G Radio Units
Venue: arXiv cs.NI 2025
Year: 2025

This work presents a perspective on addressing the underutilization of computing resources in FPGA SoC devices deployed in 5G radio and edge computing infrastructure. The initial step in this approach involves developing a resource management layer capable of dynamically migrating and scaling functions within these devices in response to contextual events. This layer serves as the foundation for designing a hierarchical, data-driven micro-orchestrator responsible for managing the lifecycle of functions in FPGA SoC devices. In this paper, the proposed resource management layer is utilized to reconfigure a function based on events identified by a computer vision edge application.
Dimer-Enhanced Optimization: A First-Order Approach to Escaping Saddle Points in Neural Network Training
Venue: arXiv cs.LG 2025
Year: 2025

First-order optimization methods, such as SGD and Adam, are widely used for training large-scale deep neural networks due to their computational efficiency and robust performance. However, relying solely on gradient information, these methods often struggle to navigate complex loss landscapes with flat regions, plateaus, and saddle points. Second-order methods, which use curvature information from the Hessian matrix, can address these challenges but are computationally infeasible for large models. The Dimer method, a first-order technique that constructs two closely spaced points to probe the local geometry of a potential energy surface, efficiently estimates curvature using only gradient information. Inspired by its use in molecular dynamics simulations for locating saddle points, we propose Dimer-Enhanced Optimization (DEO), a novel framework to escape saddle points in neural network training. DEO adapts the Dimer method to explore a broader region of the loss landscape, approximating the Hessian's smallest eigenvector without computing the full matrix. By periodically projecting the gradient onto the subspace orthogonal to the minimum curvature direction, DEO guides the optimizer away from saddle points and flat regions, enhancing training efficiency with non-stepwise updates. Preliminary experiments on a Transformer toy model show DEO achieves competitive performance compared to standard first-order methods, improving navigation of complex loss landscapes. Our work repurposes physics-inspired, first-order curvature estimation to enhance neural network training in high-dimensional spaces.
Matching Game Preferences Through Dialogical Large Language Models: A Perspective
Venue: arXiv cs.AI 2025
Year: 2025

This perspective paper explores the future potential of "conversational intelligence" by examining how Large Language Models (LLMs) could be combined with GRAPHYP's network system to better understand human conversations and preferences. Using recent research and case studies, we propose a conceptual framework that could make AI rea-soning transparent and traceable, allowing humans to see and understand how AI reaches its conclusions. We present the conceptual perspective of "Matching Game Preferences through Dialogical Large Language Models (D-LLMs)," a proposed system that would allow multiple users to share their different preferences through structured conversations. This approach envisions personalizing LLMs by embedding individual user preferences directly into how the model makes decisions. The proposed D-LLM framework would require three main components: (1) reasoning processes that could analyze different search experiences and guide performance, (2) classification systems that would identify user preference patterns, and (3) dialogue approaches that could help humans resolve conflicting information. This perspective framework aims to create an interpretable AI system where users could examine, understand, and combine the different human preferences that influence AI responses, detected through GRAPHYP's search experience networks. The goal of this perspective is to envision AI systems that would not only provide answers but also show users how those answers were reached, making artificial intelligence more transparent and trustworthy for human decision-making.
A real-time full-chain wearable sensor-based musculoskeletal simulation: an OpenSim-ROS Integration
Venue: arXiv cs.RO 2025
Year: 2025

Musculoskeletal modeling and simulations enable the accurate description and analysis of the movement of biological systems with applications such as rehabilitation assessment, prosthesis, and exoskeleton design. However, the widespread usage of these techniques is limited by costly sensors, laboratory-based setups, computationally demanding processes, and the use of diverse software tools that often lack seamless integration. In this work, we address these limitations by proposing an integrated, real-time framework for musculoskeletal modeling and simulations that leverages OpenSimRT, the robotics operating system (ROS), and wearable sensors. As a proof-of-concept, we demonstrate that this framework can reasonably well describe inverse kinematics of both lower and upper body using either inertial measurement units or fiducial markers. Additionally, we show that it can effectively estimate inverse dynamics of the ankle joint and muscle activations of major lower limb muscles during daily activities, including walking, squatting and sit to stand, stand to sit when combined with pressure insoles. We believe this work lays the groundwork for further studies with more complex real-time and wearable sensor-based human movement analysis systems and holds potential to advance technologies in rehabilitation, robotics and exoskeleton designs.
PITA: Preference-Guided Inference-Time Alignment for LLM Post-Training
Venue: arXiv cs.AI 2025
Year: 2025

Inference-time alignment enables large language models (LLMs) to generate outputs aligned with end-user preferences without further training. Recent post-training methods achieve this by using small guidance models to modify token generation during inference. These methods typically optimize a reward function KL-regularized by the original LLM taken as the reference policy. A critical limitation, however, is their dependence on a pre-trained reward model, which requires fitting to human preference feedback--a potentially unstable process. In contrast, we introduce PITA, a novel framework that integrates preference feedback directly into the LLM's token generation, eliminating the need for a reward model. PITA learns a small preference-based guidance policy to modify token probabilities at inference time without LLM fine-tuning, reducing computational cost and bypassing the pre-trained reward model dependency. The problem is framed as identifying an underlying preference distribution, solved through stochastic search and iterative refinement of the preference-based guidance model. We evaluate PITA across diverse tasks, including mathematical reasoning and sentiment classification, demonstrating its effectiveness in aligning LLM outputs with user preferences.
Cryptographic Data Exchange for Nuclear Warheads
Venue: arXiv cs.CR 2025
Year: 2025

Nuclear arms control treaties have historically focused on strategic nuclear delivery systems, indirectly restricting strategic nuclear warhead numbers and leaving nonstrategic nuclear warheads (NSNWs) outside formal verification frameworks. This paper presents a cryptographic protocol for secure and verifiable warhead tracking, addressing challenges in nuclear warhead verification without requiring intrusive physical inspections. Our system leverages commitment schemes and zero-knowledge succinct non-interactive arguments of knowledge (zkSNARKs) to ensure compliance with treaty constraints while preserving the confidentiality of sensitive nuclear warhead data. We propose a cryptographic "Warhead Passport" tracking system that chains commitments to individual warheads over their life cycle, enabling periodic challenges and real-time verification of treaty compliance. Our implementation follows real-world treaty constraints, integrates U.S. and Russian dual-hash combiners (SHA-family and GOST R 34.11 family) for cryptographic robustness and political constraints, and ensures forward security by preventing retroactive data manipulation. This work builds on policy research from prior arms control studies and provides a practical foundation for implementing secure, auditable NSNW verification mechanisms.
Cluster Purge Loss: Structuring Transformer Embeddings for Equivalent Mutants Detection
Venue: arXiv cs.LG 2025
Year: 2025

Recent pre-trained transformer models achieve superior performance in various code processing objectives. However, although effective at optimizing decision boundaries, common approaches for fine-tuning them for downstream classification tasks - distance-based methods or training an additional classification head - often fail to thoroughly structure the embedding space to reflect nuanced intra-class semantic relationships. Equivalent code mutant detection is one of these tasks, where the quality of the embedding space is crucial to the performance of the models. We introduce a novel framework that integrates cross-entropy loss with a deep metric learning objective, termed Cluster Purge Loss. This objective, unlike conventional approaches, concentrates on adjusting fine-grained differences within each class, encouraging the separation of instances based on semantical equivalency to the class center using dynamically adjusted borders. Employing UniXCoder as the base model, our approach demonstrates state-of-the-art performance in the domain of equivalent mutant detection and produces a more interpretable embedding space.
KB-DMGen: Knowledge-Based Global Guidance and Dynamic Pose Masking for Human Image Generation
Venue: arXiv cs.CV 2025
Year: 2025

Recent methods using diffusion models have made significant progress in Human Image Generation (HIG) with various control signals such as pose priors. In HIG, both accurate human poses and coherent visual quality are crucial for image generation. However, most existing methods mainly focus on pose accuracy while neglecting overall image quality, often improving pose alignment at the cost of image quality. To address this, we propose Knowledge-Based Global Guidance and Dynamic pose Masking for human image Generation (KB-DMGen). The Knowledge Base (KB), implemented as a visual codebook, provides coarse, global guidance based on input text-related visual features, improving pose accuracy while maintaining image quality, while the Dynamic pose Mask (DM) offers fine-grained local control to enhance precise pose accuracy. By injecting KB and DM at different stages of the diffusion process, our framework enhances pose accuracy through both global and local control without compromising image quality. Experiments demonstrate the effectiveness of KB-DMGen, achieving new state-of-the-art results in terms of AP and CAP on the HumanArt dataset. The project page and code are available at https://lushbng.github.io/KBDMGen.