OpenRaiser/Intern-Atlas huggingface dataset:
Multigranular Evaluation for Brain Visual Decoding
Venue: arXiv cs.CV 2025
Year: 2025

Existing evaluation protocols for brain visual decoding predominantly rely on coarse metrics that obscure inter-model differences, lack neuroscientific foundation, and fail to capture fine-grained visual distinctions. To address these limitations, we introduce BASIC, a unified, multigranular evaluation framework that jointly quantifies structural fidelity, inferential alignment, and contextual coherence between decoded and ground-truth images. For the structural level, we introduce a hierarchical suite of segmentation-based metrics, including foreground, semantic, instance, and component masks, anchored in granularity-aware correspondence across mask structures. For the semantic level, we extract structured scene representations encompassing objects, attributes, and relationships using multimodal large language models, enabling detailed, scalable, and context-rich comparisons with ground-truth stimuli. We benchmark a diverse set of visual decoding methods across multiple stimulus-neuroimaging datasets within this unified evaluation framework. Together, these criteria provide a more discriminative, interpretable, and comprehensive foundation for evaluating brain visual decoding methods.
Unveiling Effective In-Context Configurations for Image Captioning: An External & Internal Analysis
Venue: arXiv cs.CL 2025
Year: 2025

The evolution of large models has witnessed the emergence of In-Context Learning (ICL) capabilities. In Natural Language Processing (NLP), numerous studies have demonstrated the effectiveness of ICL. Inspired by the success of Large Language Models (LLMs), researchers have developed Large Multimodal Models (LMMs) with ICL capabilities. However, explorations of demonstration configuration for multimodal ICL remain preliminary. Additionally, the controllability of In-Context Examples (ICEs) provides an efficient and cost-effective means to observe and analyze the inference characteristics of LMMs under varying inputs. This paper conducts a comprehensive external and internal investigation of multimodal in-context learning on the image captioning task. Externally, we explore demonstration configuration strategies through three dimensions: shot number, image retrieval, and caption assignment. We employ multiple metrics to systematically and thoroughly evaluate and summarize key findings. Internally, we analyze typical LMM attention characteristics and develop attention-based metrics to quantify model behaviors. We also conduct auxiliary experiments to explore the feasibility of attention-driven model acceleration and compression. We further compare performance variations between LMMs with identical model design and pretraining strategies and explain the differences from the angles of pre-training data features. Our study reveals both how ICEs configuration strategies impact model performance through external experiments and characteristic typical patterns through internal inspection, providing dual perspectives for understanding multimodal ICL in LMMs. Our method of combining external and internal analysis to investigate large models, along with our newly proposed metrics, can be applied to broader research areas.
Development of a Canada-Wide Morphology Map for the ITU-R P. 1411 Propagation Model
Venue: arXiv cs.CV 2025
Year: 2025

This paper outlines the development of a Canada-wide morphology map classifying regions into residential, urban low-rise, and urban high-rise environments, following the ITU-R P.1411-12 propagation model guidelines. To address the qualitative nature of the environment-type descriptors found in the Recommendation, a machine learning approach is employed to automate the classification process. Extensive experimentation optimized classification accuracy, resulting in a Canada-wide morphology map that ensures more accurate path loss estimations for outdoor short-range propagation at frequencies ranging from 300 MHz to 100 GHz.
Integrating External Tools with Large Language Models to Improve Accuracy
Venue: arXiv cs.CL 2025
Year: 2025

This paper deals with improving querying large language models (LLMs). It is well-known that without relevant contextual information, LLMs can provide poor quality responses or tend to hallucinate. Several initiatives have proposed integrating LLMs with external tools to provide them with up-to-date data to improve accuracy. In this paper, we propose a framework to integrate external tools to enhance the capabilities of LLMs in answering queries in educational settings. Precisely, we develop a framework that allows accessing external APIs to request additional relevant information. Integrated tools can also provide computational capabilities such as calculators or calendars. The proposed framework has been evaluated using datasets from the Multi-Modal Language Understanding (MMLU) collection. The data consists of questions on mathematical and scientific reasoning. Results compared to state-of-the-art language models show that the proposed approach significantly improves performance. Our Athena framework achieves 83% accuracy in mathematical reasoning and 88% in scientific reasoning, substantially outperforming all tested models including GPT-4o, LLaMA-Large, Mistral-Large, Phi-Large, and GPT-3.5, with the best baseline model (LLaMA-Large) achieving only 67% and 79% respectively. These promising results open the way to creating complex computing ecosystems around LLMs to make their use more natural to support various tasks and activities.
Barriers in Integrating Medical Visual Question Answering into Radiology Workflows: A Scoping Review and Clinicians' Insights
Venue: arXiv cs.CL 2025
Year: 2025

Medical Visual Question Answering (MedVQA) is a promising tool to assist radiologists by automating medical image interpretation through question answering. Despite advances in models and datasets, MedVQA's integration into clinical workflows remains limited. This study systematically reviews 68 publications (2018-2024) and surveys 50 clinicians from India and Thailand to examine MedVQA's practical utility, challenges, and gaps. Following the Arksey and O'Malley scoping review framework, we used a two-pronged approach: (1) reviewing studies to identify key concepts, advancements, and research gaps in radiology workflows, and (2) surveying clinicians to capture their perspectives on MedVQA's clinical relevance. Our review reveals that nearly 60% of QA pairs are non-diagnostic and lack clinical relevance. Most datasets and models do not support multi-view, multi-resolution imaging, EHR integration, or domain knowledge, features essential for clinical diagnosis. Furthermore, there is a clear mismatch between current evaluation metrics and clinical needs. The clinician survey confirms this disconnect: only 29.8% consider MedVQA systems highly useful. Key concerns include the absence of patient history or domain knowledge (87.2%), preference for manually curated datasets (51.1%), and the need for multi-view image support (78.7%). Additionally, 66% favor models focused on specific anatomical regions, and 89.4% prefer dialogue-based interactive systems. While MedVQA shows strong potential, challenges such as limited multimodal analysis, lack of patient context, and misaligned evaluation approaches must be addressed for effective clinical integration.
Towards Evaluating Robustness of Prompt Adherence in Text to Image Models
Venue: arXiv cs.CV 2025
Year: 2025

The advancements in the domain of LLMs in recent years have surprised many, showcasing their remarkable capabilities and diverse applications. Their potential applications in various real-world scenarios have led to significant research on their reliability and effectiveness. On the other hand, multimodal LLMs and Text-to-Image models have only recently gained prominence, especially when compared to text-only LLMs. Their reliability remains constrained due to insufficient research on assessing their performance and robustness. This paper aims to establish a comprehensive evaluation framework for Text-to-Image models, concentrating particularly on their adherence to prompts. We created a novel dataset that aimed to assess the robustness of these models in generating images that conform to the specified factors of variation in the input text prompts. Our evaluation studies present findings on three variants of Stable Diffusion models: Stable Diffusion 3 Medium, Stable Diffusion 3.5 Large, and Stable Diffusion 3.5 Large Turbo, and two variants of Janus models: Janus Pro 1B and Janus Pro 7B. We introduce a pipeline that leverages text descriptions generated by the gpt-4o model for our ground-truth images, which are then used to generate artificial images by passing these descriptions to the Text-to-Image models. We then pass these generated images again through gpt-4o using the same system prompt and compare the variation between the two descriptions. Our results reveal that these models struggle to create simple binary images with only two factors of variation: a simple geometric shape and its location. We also show, using pre-trained VAEs on our dataset, that they fail to generate images that follow our input dataset distribution.
Krul: Efficient State Restoration for Multi-turn Conversations with Dynamic Cross-layer KV Sharing
Venue: arXiv cs.CL 2025
Year: 2025

Efficient state restoration in multi-turn conversations with large language models (LLMs) remains a critical challenge, primarily due to the overhead of recomputing or loading full key-value (KV) caches for all historical tokens. To address this, existing approaches compress KV caches across adjacent layers with highly similar attention patterns. However, these methods often apply a fixed compression scheme across all conversations, selecting the same layer pairs for compression without considering conversation-specific attention dynamics. This static strategy overlooks variability in attention pattern similarity across different conversations, which can lead to noticeable accuracy degradation. We present Krul, a multi-turn LLM inference system that enables accurate and efficient KV cache restoration. Krul dynamically selects compression strategies based on attention similarity across layer pairs and uses a recomputation-loading pipeline to restore the KV cache. It introduces three key innovations: 1) a preemptive compression strategy selector to preserve critical context for future conversation turns and selects a customized strategy for the conversation; 2) a token-wise heterogeneous attention similarity estimator to mitigate the attention similarity computation and storage overhead during model generation; 3) a bubble-free restoration scheduler to reduce potential bubbles brought by the imbalance of recomputing and loading stream due to compressed KV caches. Empirical evaluations on real-world tasks demonstrate that Krul achieves a 1.5x-2.68x reduction in time-to-first-token (TTFT) and a 1.33x-2.35x reduction in KV cache storage compared to state-of-the-art methods without compromising generation quality.
Modèle physique variationnel pour l'estimation de réponses impulsionnelles de salles
Venue: arXiv cs.SD 2025
Year: 2025

Room impulse response estimation is essential for tasks like speech dereverberation, which improves automatic speech recognition. Most existing methods rely on either statistical signal processing or deep neural networks designed to replicate signal processing principles. However, combining statistical and physical modeling for RIR estimation remains largely unexplored. This paper proposes a novel approach integrating both aspects through a theoretically grounded model. The RIR is decomposed into interpretable parameters: white Gaussian noise filtered by a frequency-dependent exponential decay (e.g. modeling wall absorption) and an autoregressive filter (e.g. modeling microphone response). A variational free-energy cost function enables practical parameter estimation. As a proof of concept, we show that given dry and reverberant speech signals, the proposed method outperforms classical deconvolution in noisy environments, as validated by objective metrics.
Audit, Alignment, and Optimization of LM-Powered Subroutines with Application to Public Comment Processing
Venue: arXiv cs.CL 2025
Year: 2025

The advent of language models (LMs) has the potential to dramatically accelerate tasks that may be cast to text-processing; however, real-world adoption is hindered by concerns regarding safety, explainability, and bias. How can we responsibly leverage LMs in a transparent, auditable manner -- minimizing risk and allowing human experts to focus on informed decision-making rather than data-processing or prompt engineering? In this work, we propose a framework for declaring statically typed, LM-powered subroutines (i.e., callable, function-like procedures) for use within conventional asynchronous code -- such that sparse feedback from human experts is used to improve the performance of each subroutine online (i.e., during use). In our implementation, all LM-produced artifacts (i.e., prompts, inputs, outputs, and data-dependencies) are recorded and exposed to audit on demand. We package this framework as a library to support its adoption and continued development. While this framework may be applicable across several real-world decision workflows (e.g., in healthcare and legal fields), we evaluate it in the context of public comment processing as mandated by the 1969 National Environmental Protection Act (NEPA): Specifically, we use this framework to develop "CommentNEPA," an application that compiles, organizes, and summarizes a corpus of public commentary submitted in response to a project requiring environmental review. We quantitatively evaluate the application by comparing its outputs (when operating without human feedback) to historical ``ground-truth'' data as labelled by human annotators during the preparation of official environmental impact statements.
Imitation Learning for Obstacle Avoidance Using End-to-End CNN-Based Sensor Fusion
Venue: arXiv cs.RO 2025
Year: 2025

Obstacle avoidance is crucial for mobile robots' navigation in both known and unknown environments. This research designs, trains, and tests two custom Convolutional Neural Networks (CNNs), using color and depth images from a depth camera as inputs. Both networks adopt sensor fusion to produce an output: the mobile robot's angular velocity, which serves as the robot's steering command. A newly obtained visual dataset for navigation was collected in diverse environments with varying lighting conditions and dynamic obstacles. During data collection, a communication link was established over Wi-Fi between a remote server and the robot, using Robot Operating System (ROS) topics. Velocity commands were transmitted from the server to the robot, enabling synchronized recording of visual data and the corresponding steering commands. Various evaluation metrics, such as Mean Squared Error, Variance Score, and Feed-Forward time, provided a clear comparison between the two networks and clarified which one to use for the application.
Quasi-Random Physics-informed Neural Networks
Venue: arXiv cs.LG 2025
Year: 2025

Physics-informed neural networks have shown promise in solving partial differential equations (PDEs) by integrating physical constraints into neural network training, but their performance is sensitive to the sampling of points. Based on the impressive performance of quasi Monte-Carlo methods in high dimensional problems, this paper proposes Quasi-Random Physics-Informed Neural Networks (QRPINNs), which use low-discrepancy sequences for sampling instead of random points directly from the domain. Theoretically, QRPINNs have been proven to have a better convergence rate than PINNs. Empirically, experiments demonstrate that QRPINNs significantly outperform PINNs and some representative adaptive sampling methods, especially in high-dimensional PDEs. Furthermore, combining QRPINNs with adaptive sampling can further improve the performance.
Beyond the Worst Case: Extending Differential Privacy Guarantees to Realistic Adversaries
Venue: arXiv cs.CR 2025
Year: 2025

Differential Privacy (DP) is a family of definitions that bound the worst-case privacy leakage of a mechanism. One important feature of the worst-case DP guarantee is it naturally implies protections against adversaries with less prior information, more sophisticated attack goals, and complex measures of a successful attack. However, the analytical tradeoffs between the adversarial model and the privacy protections conferred by DP are not well understood thus far. To that end, this work sheds light on what the worst-case guarantee of DP implies about the success of attackers that are more representative of real-world privacy risks. In this paper, we present a single flexible framework that generalizes and extends the patchwork of bounds on DP mechanisms found in prior work. Our framework allows us to compute high-probability guarantees for DP mechanisms on a large family of natural attack settings that previous bounds do not capture. One class of such settings is the approximate reconstruction of multiple individuals' data, such as inferring nearly entire columns of a tabular data set from noisy marginals and extracting sensitive information from DP-trained language models. We conduct two empirical case studies to illustrate the versatility of our bounds and compare them to the success of state-of-the-art attacks. Specifically, we study attacks that extract non-uniform PII from a DP-trained language model, as well as multi-column reconstruction attacks where the adversary has access to some columns in the clear and attempts to reconstruct the remaining columns for each person's record. We find that the absolute privacy risk of attacking non-uniform data is highly dependent on the adversary's prior probability of success. Our high probability bounds give us a nuanced understanding of the privacy leakage of DP mechanisms in a variety of previously understudied attack settings.
GPUHammer: Rowhammer Attacks on GPU Memories are Practical
Venue: arXiv cs.CR 2025
Year: 2025

Rowhammer is a read disturbance vulnerability in modern DRAM that causes bit-flips, compromising security and reliability. While extensively studied on Intel and AMD CPUs with DDR and LPDDR memories, its impact on GPUs using GDDR memories, critical for emerging machine learning applications, remains unexplored. Rowhammer attacks on GPUs face unique challenges: (1) proprietary mapping of physical memory to GDDR banks and rows, (2) high memory latency and faster refresh rates that hinder effective hammering, and (3) proprietary mitigations in GDDR memories, difficult to reverse-engineer without FPGA-based test platforms. We introduce GPUHammer, the first Rowhammer attack on NVIDIA GPUs with GDDR6 DRAM. GPUHammer proposes novel techniques to reverse-engineer GDDR DRAM row mappings, and employs GPU-specific memory access optimizations to amplify hammering intensity and bypass mitigations. Thus, we demonstrate the first successful Rowhammer attack on a discrete GPU, injecting up to 8 bit-flips across 4 DRAM banks on an NVIDIA A6000 with GDDR6 memory. We also show how an attacker can use these to tamper with ML models, causing significant accuracy drops (up to 80%).
Supporting Intel(r) SGX on Multi-Package Platforms
Venue: arXiv cs.DC 2025
Year: 2025

Intel(r) Software Guard Extensions (SGX) was originally released on client platforms and later extended to single socket server platforms. As developers have become familiar with the capabilities of the technology, the applicability of this capability in the cloud has been tested. Various Cloud Service Providers (CSPs) are demonstrating the value of using SGX based Trusted Execution Environments (TEE) to create a new paradigm of Confidential Cloud Computing. This paper describes the additional platform enhancements we believe are necessary to deliver a user programmable Trusted Execution Environment that scales to cloud usages, performs and is secure on multi-package platforms.
TruthTorchLM: A Comprehensive Library for Predicting Truthfulness in LLM Outputs
Venue: arXiv cs.CL 2025
Year: 2025

Generative Large Language Models (LLMs)inevitably produce untruthful responses. Accurately predicting the truthfulness of these outputs is critical, especially in high-stakes settings. To accelerate research in this domain and make truthfulness prediction methods more accessible, we introduce TruthTorchLM an open-source, comprehensive Python library featuring over 30 truthfulness prediction methods, which we refer to as Truth Methods. Unlike existing toolkits such as Guardrails, which focus solely on document-grounded verification, or LM-Polygraph, which is limited to uncertainty-based methods, TruthTorchLM offers a broad and extensible collection of techniques. These methods span diverse tradeoffs in computational cost, access level (e.g., black-box vs white-box), grounding document requirements, and supervision type (self-supervised or supervised). TruthTorchLM is seamlessly compatible with both HuggingFace and LiteLLM, enabling support for locally hosted and API-based models. It also provides a unified interface for generation, evaluation, calibration, and long-form truthfulness prediction, along with a flexible framework for extending the library with new methods. We conduct an evaluation of representative truth methods on three datasets, TriviaQA, GSM8K, and FactScore-Bio. The code is available at https://github.com/Ybakman/TruthTorchLM
Distilling Spectrograms into Tokens: Fast and Lightweight Bioacoustic Classification for BirdCLEF+ 2025
Venue: arXiv cs.SD 2025
Year: 2025

The BirdCLEF+ 2025 challenge requires classifying 206 species, including birds, mammals, insects, and amphibians, from soundscape recordings under a strict 90-minute CPU-only inference deadline, making many state-of-the-art deep learning approaches impractical. To address this constraint, the DS@GT BirdCLEF team explored two strategies. First, we establish competitive baselines by optimizing pre-trained models from the Bioacoustics Model Zoo for CPU inference. Using TFLite, we achieved a nearly 10x inference speedup for the Perch model, enabling it to run in approximately 16 minutes and achieve a final ROC-AUC score of 0.729 on the public leaderboard post-competition and 0.711 on the private leaderboard. The best model from the zoo was BirdSetEfficientNetB1, with a public score of 0.810 and a private score of 0.778. Second, we introduce a novel, lightweight pipeline named Spectrogram Token Skip-Gram (STSG) that treats bioacoustics as a sequence modeling task. This method converts audio into discrete "spectrogram tokens" by clustering Mel-spectrograms using Faiss K-means and then learns high-quality contextual embeddings for these tokens in an unsupervised manner with a Word2Vec skip-gram model. For classification, embeddings within a 5-second window are averaged and passed to a linear model. With a projected inference time of 6 minutes for a 700-minute test set, the STSG approach achieved a final ROC-AUC public score of 0.559 and a private score of 0.520, demonstrating the viability of fast tokenization approaches with static embeddings for bioacoustic classification. Supporting code for this paper can be found at https://github.com/dsgt-arc/birdclef-2025.