OpenRaiser/Intern-Atlas huggingface dataset:
Understanding Generative Recommendation with Semantic IDs from a Model-scaling View
Venue: arXiv cs.AI 2025
Year: 2025

Recent advancements in generative models have allowed the emergence of a promising paradigm for recommender systems (RS), known as Generative Recommendation (GR), which tries to unify rich item semantics and collaborative filtering signals. One popular modern approach is to use semantic IDs (SIDs), which are discrete codes quantized from the embeddings of modality encoders (e.g., large language or vision models), to represent items in an autoregressive user interaction sequence modeling setup (henceforth, SID-based GR). While generative models in other domains exhibit well-established scaling laws, our work reveals that SID-based GR shows significant bottlenecks while scaling up the model. In particular, the performance of SID-based GR quickly saturates as we enlarge each component: the modality encoder, the quantization tokenizer, and the RS itself. In this work, we identify the limited capacity of SIDs to encode item semantic information as one of the fundamental bottlenecks. Motivated by this observation, as an initial effort to obtain GR models with better scaling behaviors, we revisit another GR paradigm that directly uses large language models (LLMs) as recommenders (henceforth, LLM-as-RS). Our experiments show that the LLM-as-RS paradigm has superior model scaling properties and achieves up to 20 percent improvement over the best achievable performance of SID-based GR through scaling. We also challenge the prevailing belief that LLMs struggle to capture collaborative filtering information, showing that their ability to model user-item interactions improves as LLMs scale up. Our analyses on both SID-based GR and LLMs across model sizes from 44M to 14B parameters underscore the intrinsic scaling limits of SID-based GR and position LLM-as-RS as a promising path toward foundation models for GR.
VISOR++: Universal Visual Inputs based Steering for Large Vision Language Models
Venue: arXiv cs.CV 2025
Year: 2025

As Vision Language Models (VLMs) are deployed across safety-critical applications, understanding and controlling their behavioral patterns has become increasingly important. Existing behavioral control methods face significant limitations: system prompting approaches could easily be overridden by user instructions, while applying activation-based steering vectors requires invasive runtime access to model internals, precluding deployment with API-based services and closed-source models. Finding steering methods that transfer across multiple VLMs is still an open area of research. To this end, we introduce universal visual input based steering for output redirection (VISOR++), to achieve behavioral control through optimized visual inputs alone. We demonstrate that a single VISOR++ image can be generated for an ensemble of VLMs to emulate each of their steering vectors. By crafting universal visual inputs that induce target activation patterns, VISOR++ eliminates the need for runtime model access while remaining deployment-agnostic. This means that when an underlying model supports multimodal capability, model behaviors can be steered by inserting an image input replacing runtime steering vector based interventions. We first demonstrate the effectiveness of the VISOR++ images on open-access models such as LLaVA-1.5-7B and IDEFICS2-8B along three alignment directions: refusal, sycophancy and survival instinct. Both the model-specific steering images and the jointly optimized images achieve performance parity closely following that of steering vectors for both positive and negative steering tasks. We also show the promise of VISOR++ images in achieving directional behavioral shifts for unseen models including both open-access and closed-access ones. Furthermore, VISOR++ images are able to preserve 99.9% performance on 14,000 unrelated MMLU evaluation tasks.
Evaluating Foundation Models with Pathological Concept Learning for Kidney Cancer
Venue: arXiv cs.AI 2025
Year: 2025

To evaluate the translational capabilities of foundation models, we develop a pathological concept learning approach focused on kidney cancer. By leveraging TNM staging guidelines and pathology reports, we build comprehensive pathological concepts for kidney cancer. Then, we extract deep features from whole slide images using foundation models, construct pathological graphs to capture spatial correlations, and trained graph neural networks to identify these concepts. Finally, we demonstrate the effectiveness of this approach in kidney cancer survival analysis, highlighting its explainability and fairness in identifying low- and high-risk patients. The source code has been released by https://github.com/shangqigao/RadioPath.
A(I)nimism: Re-enchanting the World Through AI-Mediated Object Interaction
Venue: arXiv cs.AI 2025
Year: 2025

Animist worldviews treat beings, plants, landscapes, and even tools as persons endowed with spirit, an orientation that has long shaped human-nonhuman relations through ritual and moral practice. While modern industrial societies have often imagined technology as mute and mechanical, recent advances in artificial intelligence (AI), especially large language models (LLMs), invite people to anthropomorphize and attribute inner life to devices. This paper introduces A(I)nimism, an interactive installation exploring how large language objects (LLOs) can mediate animistic relationships with everyday things. Housed within a physical 'portal', the system uses GPT-4 Vision, voice input, and memory-based agents to create evolving object-personas. Encounters unfold through light, sound, and touch in a ritual-like process of request, conversation, and transformation that is designed to evoke empathy, wonder, and reflection. We situate the project within anthropological perspectives, speculative design, and spiritual HCI. AI's opacity, we argue, invites animistic interpretation, allowing LLOs to re-enchant the mundane and spark new questions of agency, responsibility, and design.
Machine Learning Algorithms for Improving Black Box Optimization Solvers
Venue: arXiv cs.LG 2025
Year: 2025

Black-box optimization (BBO) addresses problems where objectives are accessible only through costly queries without gradients or explicit structure. Classical derivative-free methods -- line search, direct search, and model-based solvers such as Bayesian optimization -- form the backbone of BBO, yet often struggle in high-dimensional, noisy, or mixed-integer settings. Recent advances use machine learning (ML) and reinforcement learning (RL) to enhance BBO: ML provides expressive surrogates, adaptive updates, meta-learning portfolios, and generative models, while RL enables dynamic operator configuration, robustness, and meta-optimization across tasks. This paper surveys these developments, covering representative algorithms such as NNs with the modular model-based optimization framework (mlrMBO), zeroth-order adaptive momentum methods (ZO-AdaMM), automated BBO (ABBO), distributed block-wise optimization (DiBB), partition-based Bayesian optimization (SPBOpt), the transformer-based optimizer (B2Opt), diffusion-model-based BBO, surrogate-assisted RL for differential evolution (Surr-RLDE), robust BBO (RBO), coordinate-ascent model-based optimization with relative entropy (CAS-MORE), log-barrier stochastic gradient descent (LB-SGD), policy improvement with black-box (PIBB), and offline Q-learning with Mamba backbones (Q-Mamba). We also review benchmark efforts such as the NeurIPS 2020 BBO Challenge and the MetaBox framework. Overall, we highlight how ML and RL transform classical inexact solvers into more scalable, robust, and adaptive frameworks for real-world optimization.
Unsupervised Detection of Spatiotemporal Anomalies in PMU Data Using Transformer-Based BiGAN
Venue: arXiv cs.LG 2025
Year: 2025

Ensuring power grid resilience requires the timely and unsupervised detection of anomalies in synchrophasor data streams. We introduce T-BiGAN, a novel framework that integrates window-attention Transformers within a bidirectional Generative Adversarial Network (BiGAN) to address this challenge. Its self-attention encoder-decoder architecture captures complex spatio-temporal dependencies across the grid, while a joint discriminator enforces cycle consistency to align the learned latent space with the true data distribution. Anomalies are flagged in real-time using an adaptive score that combines reconstruction error, latent space drift, and discriminator confidence. Evaluated on a realistic hardware-in-the-loop PMU benchmark, T-BiGAN achieves an ROC-AUC of 0.95 and an average precision of 0.996, significantly outperforming leading supervised and unsupervised methods. It shows particular strength in detecting subtle frequency and voltage deviations, demonstrating its practical value for live, wide-area monitoring without relying on manually labeled fault data.
AutoLabs: Cognitive Multi-Agent Systems with Self-Correction for Autonomous Chemical Experimentation
Venue: arXiv cs.AI 2025
Year: 2025

The automation of chemical research through self-driving laboratories (SDLs) promises to accelerate scientific discovery, yet the reliability and granular performance of the underlying AI agents remain critical, under-examined challenges. In this work, we introduce AutoLabs, a self-correcting, multi-agent architecture designed to autonomously translate natural-language instructions into executable protocols for a high-throughput liquid handler. The system engages users in dialogue, decomposes experimental goals into discrete tasks for specialized agents, performs tool-assisted stoichiometric calculations, and iteratively self-corrects its output before generating a hardware-ready file. We present a comprehensive evaluation framework featuring five benchmark experiments of increasing complexity, from simple sample preparation to multi-plate timed syntheses. Through a systematic ablation study of 20 agent configurations, we assess the impact of reasoning capacity, architectural design (single- vs. multi-agent), tool use, and self-correction mechanisms. Our results demonstrate that agent reasoning capacity is the most critical factor for success, reducing quantitative errors in chemical amounts (nRMSE) by over 85% in complex tasks. When combined with a multi-agent architecture and iterative self-correction, AutoLabs achieves near-expert procedural accuracy (F1-score > 0.89) on challenging multi-step syntheses. These findings establish a clear blueprint for developing robust and trustworthy AI partners for autonomous laboratories, highlighting the synergistic effects of modular design, advanced reasoning, and self-correction to ensure both performance and reliability in high-stakes scientific applications. Code: https://github.com/pnnl/autolabs
Iterative Residual Cross-Attention Mechanism: An Integrated Approach for Audio-Visual Navigation Tasks
Venue: arXiv cs.AI 2025
Year: 2025

Audio-visual navigation represents a significant area of research in which intelligent agents utilize egocentric visual and auditory perceptions to identify audio targets. Conventional navigation methodologies typically adopt a staged modular design, which involves first executing feature fusion, then utilizing Gated Recurrent Unit (GRU) modules for sequence modeling, and finally making decisions through reinforcement learning. While this modular approach has demonstrated effectiveness, it may also lead to redundant information processing and inconsistencies in information transmission between the various modules during the feature fusion and GRU sequence modeling phases. This paper presents IRCAM-AVN (Iterative Residual Cross-Attention Mechanism for Audiovisual Navigation), an end-to-end framework that integrates multimodal information fusion and sequence modeling within a unified IRCAM module, thereby replacing the traditional separate components for fusion and GRU. This innovative mechanism employs a multi-level residual design that concatenates initial multimodal sequences with processed information sequences. This methodological shift progressively optimizes the feature extraction process while reducing model bias and enhancing the model's stability and generalization capabilities. Empirical results indicate that intelligent agents employing the iterative residual cross-attention mechanism exhibit superior navigation performance.
YOLO-Based Defect Detection for Metal Sheets
Venue: arXiv cs.CV 2025
Year: 2025

In this paper, we propose a YOLO-based deep learning (DL) model for automatic defect detection to solve the time-consuming and labor-intensive tasks in industrial manufacturing. In our experiments, the images of metal sheets are used as the dataset for training the YOLO model to detect the defects on the surfaces and in the holes of metal sheets. However, the lack of metal sheet images significantly degrades the performance of detection accuracy. To address this issue, the ConSinGAN is used to generate a considerable amount of data. Four versions of the YOLO model (i.e., YOLOv3, v4, v7, and v9) are combined with the ConSinGAN for data augmentation. The proposed YOLOv9 model with ConSinGAN outperforms the other YOLO models with an accuracy of 91.3%, and a detection time of 146 ms. The proposed YOLOv9 model is integrated into manufacturing hardware and a supervisory control and data acquisition (SCADA) system to establish a practical automated optical inspection (AOI) system. Additionally, the proposed automated defect detection is easily applied to other components in industrial manufacturing.
Growing Winning Subnetworks, Not Pruning Them: A Paradigm for Density Discovery in Sparse Neural Networks
Venue: arXiv cs.LG 2025
Year: 2025

The lottery ticket hypothesis suggests that dense networks contain sparse subnetworks that can be trained in isolation to match full-model performance. Existing approaches-iterative pruning, dynamic sparse training, and pruning at initialization-either incur heavy retraining costs or assume the target density is fixed in advance. We introduce Path Weight Magnitude Product-biased Random growth (PWMPR), a constructive sparse-to-dense training paradigm that grows networks rather than pruning them, while automatically discovering their operating density. Starting from a sparse seed, PWMPR adds edges guided by path-kernel-inspired scores, mitigates bottlenecks via randomization, and stops when a logistic-fit rule detects plateauing accuracy. Experiments on CIFAR, TinyImageNet, and ImageNet show that PWMPR approaches the performance of IMP-derived lottery tickets-though at higher density-at substantially lower cost (~1.5x dense vs. 3-4x for IMP). These results establish growth-based density discovery as a promising paradigm that complements pruning and dynamic sparsity.
Annotation-Efficient Active Test-Time Adaptation with Conformal Prediction
Venue: arXiv cs.LG 2025
Year: 2025

Active Test-Time Adaptation (ATTA) improves model robustness under domain shift by selectively querying human annotations at deployment, but existing methods use heuristic uncertainty measures and suffer from low data selection efficiency, wasting human annotation budget. We propose Conformal Prediction Active TTA (CPATTA), which first brings principled, coverage-guaranteed uncertainty into ATTA. CPATTA employs smoothed conformal scores with a top-K certainty measure, an online weight-update algorithm driven by pseudo coverage, a domain-shift detector that adapts human supervision, and a staged update scheme balances human-labeled and model-labeled data. Extensive experiments demonstrate that CPATTA consistently outperforms the state-of-the-art ATTA methods by around 5% in accuracy. Our code and datasets are available at https://github.com/tingyushi/CPATTA.
Detecting Hope Across Languages: Multiclass Classification for Positive Online Discourse
Venue: arXiv cs.CL 2025
Year: 2025

The detection of hopeful speech in social media has emerged as a critical task for promoting positive discourse and well-being. In this paper, we present a machine learning approach to multiclass hope speech detection across multiple languages, including English, Urdu, and Spanish. We leverage transformer-based models, specifically XLM-RoBERTa, to detect and categorize hope speech into three distinct classes: Generalized Hope, Realistic Hope, and Unrealistic Hope. Our proposed methodology is evaluated on the PolyHope dataset for the PolyHope-M 2025 shared task, achieving competitive performance across all languages. We compare our results with existing models, demonstrating that our approach significantly outperforms prior state-of-the-art techniques in terms of macro F1 scores. We also discuss the challenges in detecting hope speech in low-resource languages and the potential for improving generalization. This work contributes to the development of multilingual, fine-grained hope speech detection models, which can be applied to enhance positive content moderation and foster supportive online communities.
Are Classical Clone Detectors Good Enough For the AI Era?
Venue: arXiv cs.SE 2025
Year: 2025

The increasing adoption of AI-generated code has reshaped modern software development, introducing syntactic and semantic variations in cloned code. Unlike traditional human-written clones, AI-generated clones exhibit systematic syntactic patterns and semantic differences learned from large-scale training data. This shift presents new challenges for classical code clone detection (CCD) tools, which have historically been validated primarily on human-authored codebases and optimized to detect syntactic (Type 1-3) and limited semantic clones. Given that AI-generated code can produce both syntactic and complex semantic clones, it is essential to evaluate the effectiveness of classical CCD tools within this new paradigm. In this paper, we systematically evaluate nine widely used CCD tools using GPTCloneBench, a benchmark containing GPT-3-generated clones. To contextualize and validate our results, we further test these detectors on established human-authored benchmarks, BigCloneBench and SemanticCloneBench, to measure differences in performance between traditional and AI-generated clones. Our analysis demonstrates that classical CCD tools, particularly those enhanced by effective normalization techniques, retain considerable effectiveness against AI-generated clones, while some exhibit notable performance variation compared to traditional benchmarks. This paper contributes by (1) evaluating classical CCD tools against AI-generated clones, providing critical insights into their current strengths and limitations; (2) highlighting the role of normalization techniques in improving detection accuracy; and (3) delivering detailed scalability and execution-time analyses to support practical CCD tool selection.
HiFIRec Towards High-Frequency yet Low-Intention Behaviors for Multi-Behavior Recommendation
Venue: arXiv cs.IR 2025
Year: 2025

Multi behavior recommendation leverages multiple types of user-item interactions to address data sparsity and cold-start issues,providing personalized services in domains such as healthcare and ecommerce.Most existing methods utilize graph neural networks to model user intention in a unified manner,which inadequately considers the heterogeneity across different behaviors.Especially,high frequency yet low intention behaviors may implicitly contain noisy signals,and frequent patterns that are plausible while misleading,thereby hindering the learning of user intentions.To this end,this paper proposes a novel multi-behavior recommendation method,HiFIRec,that corrects the effect of high-frequency yet low-intention behaviors by differential behavior modeling.To revise the noisy signals,we hierarchically suppress it across layers by extracting neighborhood information through layer-wise neighborhood aggregation and further capturing user intentions through adaptive cross layer feature fusion.To correct plausible frequent patterns,we propose an intensity-aware non-sampling strategy that dynamically adjusts the weights of negative samples.Extensive experiments on two benchmarks show that HiFIRec relatively improves HR@10 by 4.21%-6.81% over several state-of-the-art methods.
Galton's Law of Mediocrity: Why Large Language Models Regress to the Mean and Fail at Creativity in Advertising
Venue: arXiv cs.AI 2025
Year: 2025

Large language models (LLMs) generate fluent text yet often default to safe, generic phrasing, raising doubts about their ability to handle creativity. We formalize this tendency as a Galton-style regression to the mean in language and evaluate it using a creativity stress test in advertising concepts. When ad ideas were simplified step by step, creative features such as metaphors, emotions, and visual cues disappeared early, while factual content remained, showing that models favor high-probability information. When asked to regenerate from simplified inputs, models produced longer outputs with lexical variety but failed to recover the depth and distinctiveness of the originals. We combined quantitative comparisons with qualitative analysis, which revealed that the regenerated texts often appeared novel but lacked true originality. Providing ad-specific cues such as metaphors, emotional hooks and visual markers improved alignment and stylistic balance, though outputs still relied on familiar tropes. Taken together, the findings show that without targeted guidance, LLMs drift towards mediocrity in creative tasks; structured signals can partially counter this tendency and point towards pathways for developing creativity-sensitive models.
Assessing Algorithmic Bias in Language-Based Depression Detection: A Comparison of DNN and LLM Approaches
Venue: arXiv cs.CL 2025
Year: 2025

This paper investigates algorithmic bias in language-based models for automated depression detection, focusing on socio-demographic disparities related to gender and race/ethnicity. Models trained using deep neural networks (DNN) based embeddings are compared to few-shot learning approaches with large language models (LLMs), evaluating both performance and fairness on clinical interview transcripts from the Distress Analysis Interview Corpus/Wizard-of-Oz (DAIC-WOZ). To mitigate bias, fairness-aware loss functions are applied to DNN-based models, while in-context learning with varied prompt framing and shot counts is explored for LLMs. Results indicate that LLMs outperform DNN-based models in depression classification, particularly for underrepresented groups such as Hispanic participants. LLMs also exhibit reduced gender bias compared to DNN-based embeddings, though racial disparities persist. Among fairness-aware techniques for mitigating bias in DNN-based embeddings, the worst-group loss, which is designed to minimize loss for the worst-performing demographic group, achieves a better balance between performance and fairness. In contrast, the fairness-regularized loss minimizes loss across all groups but performs less effectively. In LLMs, guided prompting with ethical framing helps mitigate gender bias in the 1-shot setting. However, increasing the number of shots does not lead to further reductions in disparities. For race/ethnicity, neither prompting strategy nor increasing $N$ in $N$-shot learning effectively reduces disparities.