OpenRaiser/Intern-Atlas huggingface dataset:
A Qualitative Comparative Evaluation of Cognitive and Generative Theories
Venue: arXiv cs.AI 2025
Year: 2025

Evaluation is a critical activity associated with any theory. Yet this has proven to be an exceptionally challenging activity for theories based on cognitive architectures. For an overlapping set of reasons, evaluation can also be challenging for theories based on generative neural architectures. This dual challenge is approached here by leveraging a broad perspective on theory evaluation to yield a wide-ranging, albeit qualitative, comparison of whole-mind-oriented cognitive and generative architectures and the full systems that are based on these architectures.
Omni-Embed-Nemotron: A Unified Multimodal Retrieval Model for Text, Image, Audio, and Video
Venue: arXiv cs.CL 2025
Year: 2025

We present Omni-Embed-Nemotron, a unified multimodal retrieval embedding model developed to handle the increasing complexity of real-world information needs. While Retrieval-Augmented Generation (RAG) has significantly advanced language models by incorporating external knowledge, existing text-based retrievers rely on clean, structured input and struggle with the visually and semantically rich content found in real-world documents such as PDFs, slides, or videos. Recent work such as ColPali has shown that preserving document layout using image-based representations can improve retrieval quality. Building on this, and inspired by the capabilities of recent multimodal models such as Qwen2.5-Omni, we extend retrieval beyond text and images to also support audio and video modalities. Omni-Embed-Nemotron enables both cross-modal (e.g., text - video) and joint-modal (e.g., text - video+audio) retrieval using a single model. We describe the architecture, training setup, and evaluation results of Omni-Embed-Nemotron, and demonstrate its effectiveness in text, image, and video retrieval.
DuPLUS: Dual-Prompt Vision-Language Framework for Universal Medical Image Segmentation and Prognosis
Venue: arXiv cs.CV 2025
Year: 2025

Deep learning for medical imaging is hampered by task-specific models that lack generalizability and prognostic capabilities, while existing 'universal' approaches suffer from simplistic conditioning and poor medical semantic understanding. To address these limitations, we introduce DuPLUS, a deep learning framework for efficient multi-modal medical image analysis. DuPLUS introduces a novel vision-language framework that leverages hierarchical semantic prompts for fine-grained control over the analysis task, a capability absent in prior universal models. To enable extensibility to other medical tasks, it includes a hierarchical, text-controlled architecture driven by a unique dual-prompt mechanism. For segmentation, DuPLUS is able to generalize across three imaging modalities, ten different anatomically various medical datasets, encompassing more than 30 organs and tumor types. It outperforms the state-of-the-art task specific and universal models on 8 out of 10 datasets. We demonstrate extensibility of its text-controlled architecture by seamless integration of electronic health record (EHR) data for prognosis prediction, and on a head and neck cancer dataset, DuPLUS achieved a Concordance Index (CI) of 0.69. Parameter-efficient fine-tuning enables rapid adaptation to new tasks and modalities from varying centers, establishing DuPLUS as a versatile and clinically relevant solution for medical image analysis. The code for this work is made available at: https://anonymous.4open.science/r/DuPLUS-6C52
Short-circuiting Rings for Low-Latency AllReduce
Venue: arXiv cs.NI 2025
Year: 2025

Efficient collective communication is critical for many distributed ML and HPC applications. In this context, it is widely believed that the Ring algorithm for the AllReduce collective communication operation is optimal only for large messages, while Recursive Doubling is preferable for small ones due to its logarithmic number of steps compared to the linear number for Ring. In this paper, we challenge this long-held assumption and show that the Ring algorithm can remain optimal even for short messages in ring-based GPU-to-GPU topologies, once realistic propagation delays and link capacity constraints are accounted for. We find that the total propagation delay for both Ring and Recursive Doubling essentially sums to the same value, but the latter incurs significantly higher congestion due to longer hop counts, leading to increased completion times. This surprising result motivates our case for in-collective adaptive topologies, particularly in the context of emerging photonic interconnects, which can break through the limitations of static topology designs at the collective communication granularity. We design a \emph{simple and fast} heuristic for circuit-switching that enables Recursive Doubling to exploit dynamically reconfigurable photonic paths, carefully balancing reconfiguration delays, propagation latencies, and link congestion to minimize overall completion time. Our preliminary evaluations, using realistic reconfiguration delays, show that our circuit-switching schedules enable faster completion times for Recursive Doubling, even compared to Ring AllReduce on static ring topologies. We conclude by highlighting key challenges and future research directions for realizing practical, in-collective photonic switching.
Digital-Twin Evaluation for Proactive Human-Robot Collision Avoidance via Prediction-Guided A-RRT*
Venue: arXiv cs.RO 2025
Year: 2025

Human-robot collaboration requires precise prediction of human motion over extended horizons to enable proactive collision avoidance. Unlike existing planners that rely solely on kinodynamic models, we present a prediction-driven safe planning framework that leverages granular, joint-by-joint human motion forecasting validated in a physics-based digital twin. A capsule-based artificial potential field (APF) converts these granular predictions into collision risk metrics, triggering an Adaptive RRT* (A-RRT*) planner when thresholds are exceeded. The depth camera is used to extract 3D skeletal poses and a convolutional neural network-bidirectional long short-term memory (CNN-BiLSTM) model to predict individual joint trajectories ahead of time. A digital twin model integrates real-time human posture prediction placed in front of a simulated robot to evaluate motions and physical contacts. The proposed method enables validation of planned trajectories ahead of time and bridging potential latency gaps in updating planned trajectories in real-time. In 50 trials, our method achieved 100% proactive avoidance with > 250 mm clearance and sub-2 s replanning, demonstrating superior precision and reliability compared to existing kinematic-only planners through the integration of predictive human modeling with digital twin validation.
A distributed routing protocol for sending data from things to the cloud leveraging fog technology in the large-scale IoT ecosystem
Venue: arXiv cs.NI 2025
Year: 2025

Fog computing integrates cloud and edge resources. According to an intelligent and decentralized method, this technology processes data generated by IoT sensors to seamlessly integrate physical and cyber environments. Internet of Things uses wireless and smart objects. They communicate with each other, monitor the environment, collect information, and respond to user requests. These objects have limited energy resources since they use batteries to supply energy. Also, they cannot replace their batteries. As a result, the network lifetime is limited and short. Thus, reducing energy consumption and accelerating the data transmission process are very important challenges in IoT networks to reduce the response time. In the data transmission process, selecting an appropriate cluster head node is very important because it can reduce the delay when sending data to the fog. In this paper, cluster head nodes are selected based on several important criteria such as distance, residual energy, received signal strength, and link expiration time. Then, objects send the processed data to the server hierarchically through a balanced tree. The simulation results show that the proposed method outperforms the energy-efficient centroid-based routing protocol (EECRP) and the Emergency Response IoT based on Global Information Decision (ERGID) in terms of packet delivery rate, delay, response time, and network lifetime.
Triplet-Structured Knowledge Integration for Multi-Turn Medical Reasoning
Venue: arXiv cs.CL 2025
Year: 2025

Large Language Models (LLMs) have shown strong performance on static medical Question Answering (QA) tasks, yet their reasoning often deteriorates in multi-turn clinical dialogues where patient information is scattered across turns. This paper introduces TriMediQ, a triplet-structured approach that enhances the reasoning reliability of LLMs through explicit knowledge integration. TriMediQ first employs a frozen triplet extraction LLM to convert patient responses into clinically grounded triplets, ensuring factual precision via constrained prompting. These triplets are incorporated into a patient-specific Knowledge Graph (KG), from which a trainable projection module consisting of a graph encoder and a projector captures relational dependencies while keeping all LLM parameters frozen. During inference, the projection module guides multi-hop reasoning over the KG, enabling coherent clinical dialogue understanding. Experiments on two interactive medical QA benchmarks show that TriMediQ achieves up to 10.4\% improvement in accuracy over five existing baselines on the iMedQA dataset. These results demonstrate that structuring patient information as triplets can effectively improve the reasoning capability of LLMs in multi-turn medical QA.
From Scope to Script: An Automated Report Generation Model for Gastrointestinal Endoscopy
Venue: arXiv cs.CV 2025
Year: 2025

Endoscopic procedures such as esophagogastroduodenoscopy (EGD) and colonoscopy play a critical role in diagnosing and managing gastrointestinal (GI) disorders. However, the documentation burden associated with these procedures place significant strain on gastroenterologists, contributing to inefficiencies in clinical workflows and physician burnout. To address this challenge, we propose a novel automated report generation model that leverages a transformer-based vision encoder and text decoder within a two-stage training framework. In the first stage, both components are pre-trained on image/text caption pairs to capture generalized vision-language features, followed by fine-tuning on images/report pairs to generate clinically meaningful findings. Our approach not only streamlines the documentation process but also holds promise for reducing physician workload and improving patient care.
Cosmological Hydrodynamics at Exascale: A Trillion-Particle Leap in Capability
Venue: arXiv cs.DC 2025
Year: 2025

Resolving the most fundamental questions in cosmology requires simulations that match the scale, fidelity, and physical complexity demanded by next-generation sky surveys. To achieve the realism needed for this critical scientific partnership, detailed gas dynamics, along with a host of astrophysical effects, must be treated self-consistently with gravity for end-to-end modeling of structure formation. As an important step on this roadmap, exascale computing enables simulations that span survey-scale volumes while incorporating key subgrid processes that shape complex cosmic structures. We present results from CRK-HACC, a cosmological hydrodynamics code built for the extreme scalability requirements set by modern cosmological surveys. Using separation-of-scale techniques, GPU-resident tree solvers, in situ analysis pipelines, and multi-tiered I/O, CRK-HACC executed Frontier-E: a four trillion particle full-sky simulation, over an order of magnitude larger than previous efforts. The run achieved 513.1 PFLOPs peak performance, processing 46.6 billion particles per second and writing more than 100 PB of data in just over one week of runtime.
CryptOracle: A Modular Framework to Characterize Fully Homomorphic Encryption
Venue: arXiv cs.CR 2025
Year: 2025

Privacy-preserving machine learning has become an important long-term pursuit in this era of artificial intelligence (AI). Fully Homomorphic Encryption (FHE) is a uniquely promising solution, offering provable privacy and security guarantees. Unfortunately, computational cost is impeding its mass adoption. Modern solutions are up to six orders of magnitude slower than plaintext execution. Understanding and reducing this overhead is essential to the advancement of FHE, particularly as the underlying algorithms evolve rapidly. This paper presents a detailed characterization of OpenFHE, a comprehensive open-source library for FHE, with a particular focus on the CKKS scheme due to its significant potential for AI and machine learning applications. We introduce CryptOracle, a modular evaluation framework comprising (1) a benchmark suite, (2) a hardware profiler, and (3) a predictive performance model. The benchmark suite encompasses OpenFHE kernels at three abstraction levels: workloads, microbenchmarks, and primitives. The profiler is compatible with standard and user-specified security parameters. CryptOracle monitors application performance, captures microarchitectural events, and logs power and energy usage for AMD and Intel systems. These metrics are consumed by a modeling engine to estimate runtime and energy efficiency across different configuration scenarios, with error geomean of $-7.02\%\sim8.40\%$ for runtime and $-9.74\%\sim15.67\%$ for energy. CryptOracle is open source, fully modular, and serves as a shared platform to facilitate the collaborative advancements of applications, algorithms, software, and hardware in FHE. The CryptOracle code can be accessed at https://github.com/UnaryLab/CryptOracle.
Explainable but Vulnerable: Adversarial Attacks on XAI Explanation in Cybersecurity Applications
Venue: arXiv cs.CR 2025
Year: 2025

Explainable Artificial Intelligence (XAI) has aided machine learning (ML) researchers with the power of scrutinizing the decisions of the black-box models. XAI methods enable looking deep inside the models' behavior, eventually generating explanations along with a perceived trust and transparency. However, depending on any specific XAI method, the level of trust can vary. It is evident that XAI methods can themselves be a victim of post-adversarial attacks that manipulate the expected outcome from the explanation module. Among such attack tactics, fairwashing explanation (FE), manipulation explanation (ME), and backdoor-enabled manipulation attacks (BD) are the notable ones. In this paper, we try to understand these adversarial attack techniques, tactics, and procedures (TTPs) on explanation alteration and thus the effect on the model's decisions. We have explored a total of six different individual attack procedures on post-hoc explanation methods such as SHAP (SHapley Additive exPlanations), LIME (Local Interpretable Model-agnostic Explanation), and IG (Integrated Gradients), and investigated those adversarial attacks in cybersecurity applications scenarios such as phishing, malware, intrusion, and fraudulent website detection. Our experimental study reveals the actual effectiveness of these attacks, thus providing an urgency for immediate attention to enhance the resiliency of XAI methods and their applications.
Towards Unsupervised Speech Recognition at the Syllable-Level
Venue: arXiv cs.CL 2025
Year: 2025

Training speech recognizers with unpaired speech and text -- known as unsupervised speech recognition (UASR) -- is a crucial step toward extending ASR to low-resource languages in the long-tail distribution and enabling multimodal learning from non-parallel data. However, existing approaches based on phones often rely on costly resources such as grapheme-to-phoneme converters (G2Ps) and struggle to generalize to languages with ambiguous phoneme boundaries due to training instability. In this paper, we address both challenges by introducing a syllable-level UASR framework based on masked language modeling, which avoids the need for G2P and the instability of GAN-based methods. Our approach achieves up to a 40\% relative reduction in character error rate (CER) on LibriSpeech and generalizes effectively to Mandarin, a language that has remained particularly difficult for prior methods. Code will be released upon acceptance.
A Novel Cloud-Based Diffusion-Guided Hybrid Model for High-Accuracy Accident Detection in Intelligent Transportation Systems
Venue: arXiv cs.CV 2025
Year: 2025

The integration of Diffusion Models into Intelligent Transportation Systems (ITS) is a substantial improvement in the detection of accidents. We present a novel hybrid model integrating guidance classification with diffusion techniques. By leveraging fine-tuned ExceptionNet architecture outputs as input for our proposed diffusion model and processing image tensors as our conditioning, our approach creates a robust classification framework. Our model consists of multiple conditional modules, which aim to modulate the linear projection of inputs using time embeddings and image covariate embeddings, allowing the network to adapt its behavior dynamically throughout the diffusion process. To address the computationally intensive nature of diffusion models, our implementation is cloud-based, enabling scalable and efficient processing. Our strategy overcomes the shortcomings of conventional classification approaches by leveraging diffusion models inherent capacity to effectively understand complicated data distributions. We investigate important diffusion characteristics, such as timestep schedulers, timestep encoding techniques, timestep count, and architectural design changes, using a thorough ablation study, and have conducted a comprehensive evaluation of the proposed model against the baseline models on a publicly available dataset. The proposed diffusion model performs best in image-based accident detection with an accuracy of 97.32%.
Robust Visual Embodiment: How Robots Discover Their Bodies in Real Environments
Venue: arXiv cs.RO 2025
Year: 2025

Robots with internal visual self-models promise unprecedented adaptability, yet existing autonomous modeling pipelines remain fragile under realistic sensing conditions such as noisy imagery and cluttered backgrounds. This paper presents the first systematic study quantifying how visual degradations--including blur, salt-and-pepper noise, and Gaussian noise--affect robotic self-modeling. Through both simulation and physical experiments, we demonstrate their impact on morphology prediction, trajectory planning, and damage recovery in state-of-the-art pipelines. To overcome these challenges, we introduce a task-aware denoising framework that couples classical restoration with morphology-preserving constraints, ensuring retention of structural cues critical for self-modeling. In addition, we integrate semantic segmentation to robustly isolate robots from cluttered and colorful scenes. Extensive experiments show that our approach restores near-baseline performance across simulated and physical platforms, while existing pipelines degrade significantly. These contributions advance the robustness of visual self-modeling and establish practical foundations for deploying self-aware robots in unpredictable real-world environments.
Backdoor-Powered Prompt Injection Attacks Nullify Defense Methods
Venue: arXiv cs.CR 2025
Year: 2025

With the development of technology, large language models (LLMs) have dominated the downstream natural language processing (NLP) tasks. However, because of the LLMs' instruction-following abilities and inability to distinguish the instructions in the data content, such as web pages from search engines, the LLMs are vulnerable to prompt injection attacks. These attacks trick the LLMs into deviating from the original input instruction and executing the attackers' target instruction. Recently, various instruction hierarchy defense strategies are proposed to effectively defend against prompt injection attacks via fine-tuning. In this paper, we explore more vicious attacks that nullify the prompt injection defense methods, even the instruction hierarchy: backdoor-powered prompt injection attacks, where the attackers utilize the backdoor attack for prompt injection attack purposes. Specifically, the attackers poison the supervised fine-tuning samples and insert the backdoor into the model. Once the trigger is activated, the backdoored model executes the injected instruction surrounded by the trigger. We construct a benchmark for comprehensive evaluation. Our experiments demonstrate that backdoor-powered prompt injection attacks are more harmful than previous prompt injection attacks, nullifying existing prompt injection defense methods, even the instruction hierarchy techniques.
Code4MeV2: a Research-oriented Code-completion Platform
Venue: arXiv cs.SE 2025
Year: 2025

The adoption of AI-powered code completion tools in software development has increased substantially, yet the user interaction data produced by these systems remain proprietary within large corporations. This creates a barrier for the academic community, as researchers must often develop dedicated platforms to conduct studies on human--AI interaction, making reproducible research and large-scale data analysis impractical. In this work, we introduce Code4MeV2, a research-oriented, open-source code completion plugin for JetBrains IDEs, as a solution to this limitation. Code4MeV2 is designed using a client--server architecture and features inline code completion and a context-aware chat assistant. Its core contribution is a modular and transparent data collection framework that gives researchers fine-grained control over telemetry and context gathering. Code4MeV2 achieves industry-comparable performance in terms of code completion, with an average latency of 200~ms. We assess our tool through a combination of an expert evaluation and a user study with eight participants. Feedback from both researchers and daily users highlights its informativeness and usefulness. We invite the community to adopt and contribute to this tool. More information about the tool can be found at https://app.code4me.me.
EvoEngineer: Mastering Automated CUDA Kernel Code Evolution with Large Language Models
Venue: arXiv cs.LG 2025
Year: 2025

CUDA kernel optimization has become a critical bottleneck for AI performance, as deep learning training and inference efficiency directly depends on highly optimized GPU kernels. Despite the promise of Large Language Models (LLMs) for automating kernel optimization, this field suffers from a fragmented ecosystem of isolated and incomparable approaches with unclear problem formulations. Furthermore, general-purpose LLM code evolution methods cannot meet strict correctness requirements of CUDA kernel optimization. We address these fundamental challenges by first formalizing CUDA kernel optimization as a code optimization task with a clear objective, constraints, and evaluation metrics. We then establish the first systematic LLM-based code evolution framework, EvoEngineer, that provides guidance for designing and adapting optimization strategies to achieve a balance between performance and correctness. Finally, we implement a kernel optimization system based on this framework and conduct extensive experiments on 91 real-world CUDA kernels. Our results demonstrate that EvoEngineer achieves a principled balance between performance and correctness, with the highest averaged median speedup of \textbf{2.72}$\times$ over baseline CUDA kernels and a code validity rate of \textbf{69.8}\%, outperforming existing methods on both dimensions. Our method achieves a maximum speedup of \textbf{36.75}$\times$ among all operations over PyTorch kernels and delivers the highest speedup on \textbf{28} (\textbf{56.0\%}) of 50 operations that achieve over \textbf{2$\times$} acceleration.