As machine learning (ML) is increasingly implemented in hardware to address real-time challenges in scientific applications, the development of advanced toolchains has significantly reduced the time required to iterate on various designs. These advancements have solved major obstacles, but also exposed new challenges. For example, processes that were not previously considered bottlenecks, such as hardware synthesis, are becoming limiting factors in the rapid iteration of designs. To mitigate these emerging constraints, multiple efforts have been undertaken to develop an ML-based surrogate model that estimates resource usage of ML accelerator architectures. We introduce wa-hls4ml, a benchmark for ML accelerator resource and latency estimation, and its corresponding initial dataset of over 680,000 fully connected and convolutional neural networks, all synthesized using hls4ml and targeting Xilinx FPGAs. The benchmark evaluates the performance of resource and latency predictors against several common ML model architectures, primarily originating from scientific domains, as exemplar models, and the average performance across a subset of the dataset. Additionally, we introduce GNN- and transformer-based surrogate models that predict latency and resources for ML accelerators. We present the architecture and performance of the models and find that the models generally predict latency and resources for the 75% percentile within several percent of the synthesized resources on the synthetic test dataset.

Foundation models are increasingly central to high-stakes AI systems, and governance frameworks now depend on evaluations to assess their risks and capabilities. Although general capability evaluations are widespread, social impact assessments covering bias, fairness, privacy, environmental costs, and labor practices remain uneven across the AI ecosystem. To characterize this landscape, we conduct the first comprehensive analysis of both first-party and third-party social impact evaluation reporting across a wide range of model developers. Our study examines 186 first-party release reports and 183 post-release evaluation sources, and complements this quantitative analysis with interviews of model developers. We find a clear division of evaluation labor: first-party reporting is sparse, often superficial, and has declined over time in key areas such as environmental impact and bias, while third-party evaluators including academic researchers, nonprofits, and independent organizations provide broader and more rigorous coverage of bias, harmful content, and performance disparities. However, this complementarity has limits. Only model developers can authoritatively report on data provenance, content moderation labor, financial costs, and training infrastructure, yet interviews reveal that these disclosures are often deprioritized unless tied to product adoption or regulatory compliance. Our findings indicate that current evaluation practices leave major gaps in assessing AI's societal impacts, highlighting the urgent need for policies that promote developer transparency, strengthen independent evaluation ecosystems, and create shared infrastructure to aggregate and compare third-party evaluations in a consistent and accessible way.

Photon scattering has traditionally limited the ability of near-infrared spectroscopy (NIRS) to extract accurate, layer-specific information from the brain. This limitation restricts its clinical utility for precise neurological monitoring. To address this, we introduce an AI-driven, high-density NIRS system optimized to provide real-time, layer-specific oxygenation data from the brain cortex, specifically targeting acute neuro-emergencies. Our system integrates high-density NIRS reflectance data with a neural network trained on MRI-based synthetic datasets. This approach achieves robust cortical oxygenation accuracy across diverse anatomical variations. In simulations, our AI-assisted NIRS demonstrated a strong correlation (R2=0.913) with actual cortical oxygenation, markedly outperforming conventional methods (R2=0.469). Furthermore, biomimetic phantom experiments confirmed its superior anatomical reliability (R2=0.986) compared to standard commercial devices (R2=0.823). In clinical validation with healthy subjects and ischemic stroke patients, the system distinguished between the two groups with an AUC of 0.943. This highlights its potential as an accessible, high-accuracy diagnostic tool for emergency and point-of-care settings. These results underscore the system's capability to advance neuro-monitoring precision through AI, enabling timely, data-driven decisions in critical care environments.

While deep learning has achieved remarkable success across a wide range of applications, its theoretical understanding of representation learning remains limited. Deep neural kernels provide a principled framework to interpret over-parameterized neural networks by mapping hierarchical feature transformations into kernel spaces, thereby combining the expressive power of deep architectures with the analytical tractability of kernel methods. Recent advances, particularly neural tangent kernels (NTKs) derived by gradient inner products, have established connections between infinitely wide neural networks and nonparametric Bayesian inference. However, the existing NTK paradigm has been predominantly confined to the infinite-width regime, while overlooking the representational role of network depth. To address this gap, we propose a depth-induced NTK kernel based on a shortcut-related architecture, which converges to a Gaussian process as the network depth approaches infinity. We theoretically analyze the training invariance and spectrum properties of the proposed kernel, which stabilizes the kernel dynamics and mitigates degeneration. Experimental results further underscore the effectiveness of our proposed method. Our findings significantly extend the existing landscape of the neural kernel theory and provide an in-depth understanding of deep learning and the scaling law.

Department of Applied Mathematics Harbin Institute of T echnology, W eihai Weihai, China gaoxiang.zhao@stu.hit.edu.cn Abstract--Real-Time Auction (RT A) Interception aims to filter out invalid or irrelevant traffic to enhance the integrity and reliability of downstream data. However, two key challenges remain: (i) the need for accurate estimation of traffic quality together with sufficiently high confidence in the model's predictions--typically addressed through uncertainty modeling--and (ii) the efficiency bottlenecks that such uncertainty modeling introduces in real-time applications due to repeated inference. T o address these challenges, we propose DAUM, a joint modeling framework that integrates multi-objective learning with uncertainty modeling, yielding both traffic quality predictions and reliable confidence estimates. Building on DAUM, we further apply knowledge distillation to reduce the computational overhead of uncertainty modeling, while largely preserving predictive accuracy and retaining the benefits of uncertainty estimation. Experiments on the JD advertisement dataset demonstrate that DAUM consistently improves predictive performance, with the distilled model delivering a tenfold increase in inference speed. In online advertising, RT A mechanisms play a central role in determining which traffic are exposed to downstream systems. Since not all incoming traffic contributes equally to campaign performance, an effective interception process is needed to filter out unproductive requests while preserving those that align with predefined objectives. Achieving this goal is particularly challenging because it requires not only the accurate prediction of multiple user-behavior metrics but also dependable estimates of prediction confidence under highly dynamic conditions. A natural way to address these requirements is to combine multi-objective optimization with uncertainty modeling.

Vision-Language Models (VLMs) have shown strong performance in tasks like visual question answering and multimodal text generation, but their effectiveness in scientific domains such as materials science remains limited. While some machine learning methods have addressed specific challenges in this field, there is still a lack of foundation models designed for broad tasks like polymer property prediction using multimodal data. In this work, we present a multimodal polymer dataset to fine-tune VLMs through instruction-tuning pairs and assess the impact of multimodality on prediction performance. Our fine-tuned models, using LoRA, outperform unimodal and baseline approaches, demonstrating the benefits of multimodal learning. Additionally, this approach reduces the need to train separate models for different properties, lowering deployment and maintenance costs.

Reflection on one's thought process and making corrections to it if there exists dissatisfaction in its performance is, perhaps, one of the essential traits of intelligence. However, such high-level abstract concepts mandatory for Artificial General Intelligence can be modelled even at the low level of narrow Machine Learning algorithms. Here, we present the self-awareness mechanism emulation in the form of a supervising artificial neural network (ANN) observing patterns in activations of another underlying ANN in a search for indications of the high uncertainty of the underlying ANN and, therefore, the trustworthiness of its predictions. The underlying ANN is a convolutional neural network (CNN) ensemble employed for face recognition and facial expression tasks. The self-awareness ANN has a memory region where its past performance information is stored, and its learnable parameters are adjusted during the training to optimize the performance. The trustworthiness verdict triggers the active learning mode, giving elements of agency to the machine learning algorithm that asks for human help in high uncertainty and confusion conditions.

The rapid advancement of spatial transcriptomics (ST), i.e., spatial gene expressions, has made it possible to measure gene expression within original tissue, enabling us to discover molecular mechanisms. However, current ST platforms frequently suffer from low resolution, limiting the in-depth understanding of spatial gene expression. Super-resolution approaches promise to enhance ST maps by integrating histology images with gene expressions of profiled tissue spots. However, it remains a challenge to model the interactions between histology images and gene expressions for effective ST enhancement. This study presents a cross-modal cross-content contrastive diffusion framework, called C3-Diff, for ST enhancement with histology images as guidance. In C3-Diff, we firstly analyze the deficiency of traditional contrastive learning paradigm, which is then refined to extract both modal-invariant and content-invariant features of ST maps and histology images. Further, to overcome the problem of low sequencing sensitivity in ST maps, we perform nosing-based information augmentation on the surface of feature unit hypersphere. Finally, we propose a dynamic cross-modal imputation-based training strategy to mitigate ST data scarcity. We tested C3-Diff by benchmarking its performance on four public datasets, where it achieves significant improvements over competing methods. Moreover, we evaluate C3-Diff on downstream tasks of cell type localization, gene expression correlation and single-cell-level gene expression prediction, promoting AI-enhanced biotechnology for biomedical research and clinical applications. Codes are available at https://github.com/XiaofeiWang2018/C3-Diff.

Abstract--An adaptive structural learning method of Restricted Boltzmann Machine (RBM) and Deep Belief Network (DBN) has been developed as one of prominent deep learning models. The neuron generation-annihilation algorithm in R BM and layer generation algorithm in DBN make an optimal networ k structure for given input during the learning. In this paper, our model is applied to an automatic recognition method of road network system, called RoadTracer . A novel method of RoadTracer using the T eacher-Student base d ensemble learning model of Adaptive DBN is proposed, since t he road maps contain many complicated features so that a model with high representation power to detect should be required . The experimental results showed the detection accuracy of t he proposed model was improved from 40.0% to 89.0% on average in the seven major cities among the test dataset. In addition, we challenged to apply our method to the detection of availab le roads when landslide by natural disaster is occurred, in ord er to rapidly obtain a way of transportation. For fast inferenc e, a small size of the trained model was implemented on a small embedded edge device as lightweight deep learning. Recently there have been more cases of extreme climate events including unexpected and unusual weather. The atten - tion of these events has been received in the last few years, d ue to the significant loss of human lives and escalating economi c costs, as well as the impacts on landslides and changes in ecosystems. In Japan, the Japan Meteorological Agency (JMA) has issued "Climate Change Monitoring Report" every year informing the latest status of climate change. According to [1 ], during the Heavy Rain Event of July 2018, Japan experienced unprecedented heavy rainfall. Overall precipitation obse rved at AMeDAS stations throughout Japan in July 2018 was extremely high in comparison with past heavy rainfall event s since 1982. A prominent characteristic of this rain event is that the record-breaking local precipitation, particularly wi thin 48 to 72 hours, was observed extensively over western Japan and Tokyo region, including the Seto Inland Sea side of Chugoku and Shikoku regions. S. Kamada is with Hiroshima City University, Hiroshima, Jap an T. Ichimura is with Prefectural University of Hiroshima, Hi roshima, Japan In addition, lifelines such as wat er supply and communications damaged, and traffic obstacles occurred over a wide area. Due to the disruption of major roads and railroads, the supply was also suspended.

Music language models (Music LMs), like vision language models, leverage mul-timodal representations to answer natural language queries about musical audio recordings. Although Music LMs are reportedly improving, we find that current evaluations fail to capture whether their answers are correct. Specifically, for all Music LMs that we examine, widely-used evaluation metrics such as BLEU, METEOR, and BERTScore fail to measure anything beyond linguistic fluency of the model's responses. To measure the true performance of Music LMs, we propose (1) a better general-purpose evaluation metric for Music LMs adapted to the music domain and (2) a factual evaluation framework to quantify the correctness of a Music LM's responses. Our framework is agnostic to the modality of the question-answering model and could be generalized to quantify performance in other open-ended question-answering domains. We use open datasets in our experiments and will release all code on publication. Music Language Models (Music LMs) are an emerging family of multimodal models that consume both language and audio as input. Music LMs are typically benchmarked with Natural Language Processing (NLP) metrics such as BERTScore (Zhang et al., 2020), which compare reference text with model outputs using a question-answering (QA) dataset, e.g., MusicQA. Prior work has identified that these metrics may be inadequate (Gardner et al., 2024; Lee & Lee, 2024; Zang et al., 2025), but they remain the predominant approach for evaluating Music LMs. In this work, we show that the standard NLP metrics used to assess Music LMs are not just inadequate; they fail to measure any ability of these models to extract information from audio. Specifically, we propose a baseline experiment that pairs each question in a Music QA dataset with a random, unrelated music recording from the dataset; this baseline tells us how a Music LM scores when it receives no useful information with which to answer the question; nevertheless, the standard NLP metrics judge outputs of this baseline to be equally good as when the correct music is provided. Furthermore, we show that adversarially crafted answers achieve very high scores under the standard metrics, despite being factually incorrect.