OpenRaiser/Intern-Atlas huggingface dataset:
Acoustic RIS for Massive Spatial Multiplexing: Unleashing Degrees of Freedom and Capacity in Underwater Communications
Venue: arXiv cs.NI 2025
Year: 2025

Underwater acoustic (UWA) communications are essential for high-speed marine data transmission but remain severely constrained by limited bandwidth, significant propagation loss, and sparse multipath structures. Conventional underwater acoustic multiple-input multiple-output (MIMO) systems primarily utilize spatial diversity but suffer from limited array resolution, causing angular ambiguity and insufficient spatial degrees of freedom (DoFs). This paper addresses these limitations through acoustic Reconfigurable Intelligent Surfaces (aRIS) to actively generate orthogonally distinguishable virtual paths, significantly enhancing spatial DoFs and channel capacity. An ocean-specific DoF-channel coupling model is established, explicitly deriving conditions for spatial rank enhancement. Subsequently, the optimal geometric locus, termed the Light-Point, is analytically identified, where deploying a single aRIS maximizes DoFs by introducing two and three additional resolvable paths in deep-sea and shallow-sea environments, respectively. Furthermore, an active simultaneous transmitting and reflecting (ASTAR) aRIS architecture with independent beam control and adaptive beam-tracking mechanism integrating unmanned underwater vehicles (UUVs) and acoustic intensity gradient sensing is proposed. Extensive simulations validate the proposed joint aRIS deployment and beamforming framework, demonstrating substantial UWA channel capacity improvements-up to 265% and 170% in shallow-sea and deep-sea scenarios, respectively.
Batch Normalization-Free Fully Integer Quantized Neural Networks via Progressive Tandem Learning
Venue: arXiv cs.LG 2025
Year: 2025

Quantised neural networks (QNNs) shrink models and reduce inference energy through low-bit arithmetic, yet most still depend on a running statistics batch normalisation (BN) layer, preventing true integer-only deployment. Prior attempts remove BN by parameter folding or tailored initialisation; while helpful, they rarely recover BN's stability and accuracy and often impose bespoke constraints. We present a BN-free, fully integer QNN trained via a progressive, layer-wise distillation scheme that slots into existing low-bit pipelines. Starting from a pretrained BN-enabled teacher, we use layer-wise targets and progressive compensation to train a student that performs inference exclusively with integer arithmetic and contains no BN operations. On ImageNet with AlexNet, the BN-free model attains competitive Top-1 accuracy under aggressive quantisation. The procedure integrates directly with standard quantisation workflows, enabling end-to-end integer-only inference for resource-constrained settings such as edge and embedded devices.
XTC, A Research Platform for Optimizing AI Workload Operators
Venue: arXiv cs.PF 2025
Year: 2025

Achieving high efficiency on AI operators demands precise control over computation and data movement. However, existing scheduling languages are locked into specific compiler ecosystems, preventing fair comparison, reuse, and evaluation across frameworks. No unified interface currently decouples scheduling specification from code generation and measurement. We introduce XTC, a platform that unifies scheduling and performance evaluation across compilers. With its common API and reproducible measurement framework, XTC enables portable experimentation and accelerates research on optimization strategies.
ParamExplorer: A framework for exploring parameters in generative art
Venue: arXiv cs.AI 2025
Year: 2025

Generative art systems often involve high-dimensional and complex parameter spaces in which aesthetically compelling outputs occupy only small, fragmented regions. Because of this combinatorial explosion, artists typically rely on extensive manual trial-and-error, leaving many potentially interesting configurations undiscovered. In this work we make two contributions. First, we introduce ParamExplorer, an interactive and modular framework inspired by reinforcement learning that helps the exploration of parameter spaces in generative art algorithms, guided by human-in-the-loop or even automated feedback. The framework also integrates seamlessly with existing p5js projects. Second, within this framework we implement and evaluate several exploration strategies, referred to as agents.
From Personalization to Prejudice: Bias and Discrimination in Memory-Enhanced AI Agents for Recruitment
Venue: arXiv cs.AI 2025
Year: 2025

Large Language Models (LLMs) have empowered AI agents with advanced capabilities for understanding, reasoning, and interacting across diverse tasks. The addition of memory further enhances them by enabling continuity across interactions, learning from past experiences, and improving the relevance of actions and responses over time; termed as memory-enhanced personalization. Although such personalization through memory offers clear benefits, it also introduces risks of bias. While several previous studies have highlighted bias in ML and LLMs, bias due to memory-enhanced personalized agents is largely unexplored. Using recruitment as an example use case, we simulate the behavior of a memory-enhanced personalized agent, and study whether and how bias is introduced and amplified in and across various stages of operation. Our experiments on agents using safety-trained LLMs reveal that bias is systematically introduced and reinforced through personalization, emphasizing the need for additional protective measures or agent guardrails in memory-enhanced LLM-based AI agents.
InfoDCL: Informative Noise Enhanced Diffusion Based Contrastive Learning
Venue: arXiv cs.IR 2025
Year: 2025

Contrastive learning has demonstrated promising potential in recommender systems. Existing methods typically construct sparser views by randomly perturbing the original interaction graph, as they have no idea about the authentic user preferences. Owing to the sparse nature of recommendation data, this paradigm can only capture insufficient semantic information. To address the issue, we propose InfoDCL, a novel diffusion-based contrastive learning framework for recommendation. Rather than injecting randomly sampled Gaussian noise, we employ a single-step diffusion process that integrates noise with auxiliary semantic information to generate signals and feed them to the standard diffusion process to generate authentic user preferences as contrastive views. Besides, based on a comprehensive analysis of the mutual influence between generation and preference learning in InfoDCL, we build a collaborative training objective strategy to transform the interference between them into mutual collaboration. Additionally, we employ multiple GCN layers only during inference stage to incorporate higher-order co-occurrence information while maintaining training efficiency. Extensive experiments on five real-world datasets demonstrate that InfoDCL significantly outperforms state-of-the-art methods. Our InfoDCL offers an effective solution for enhancing recommendation performance and suggests a novel paradigm for applying diffusion method in contrastive learning frameworks.
Online Competitive Searching for Rays in the Half-plane
Venue: arXiv cs.CG 2025
Year: 2025

We consider the problem of searching for rays (or lines) in the half-plane. The given problem turns out to be a very natural extension of the cow-path problem that is lifted into the half-plane and the problem can also directly be motivated by a 1.5-dimensional terrain search problem. We present and analyse an efficient strategy for our setting and guarantee a competitive ratio of less than 9.12725 in the worst case and also prove a lower bound of at least 9.06357 for any strategy. Thus the given strategy is almost optimal, the gap is less than 0.06368. By appropriate adjustments for the terrain search problem we can improve on former results and present geometrically motivated proof arguments. As expected, the terrain itself can only be helpful for the searcher that competes against the unknown shortest path. We somehow extract the core of the problem.
Efficient Bitcoin Meta-Protocol Transaction and Data Discovery Through nLockTime Field Repurposing
Venue: arXiv cs.CR 2025
Year: 2025

We describe the Lockchain Protocol, a lightweight Bitcoin meta-protocol that enables highly efficient transaction discovery at zero marginal block space cost, and data verification without introducing any new on-chain storage mechanism. The protocol repurposes the mandatory 4-byte nLockTime field of every Bitcoin transaction as a compact metadata header. By constraining values to an unused range of past Unix timestamps greater than or equal to 500,000,000, the field can encode a protocol signal, type, variant, and sequence identifier while remaining fully valid under Bitcoin consensus and policy rules. The primary contribution of the protocol is an efficient discovery layer. Indexers can filter candidate transactions by examining a fixed-size header field, independent of transaction payload size, and only then selectively inspect heavier data such as OP RETURN outputs or witness fields. The Lockchain Protocol applies established protocol design patterns to an under-optimised problem domain, namely transaction discovery at scale, and does not claim new cryptographic primitives or storage methods.
SDFoam: Signed-Distance Foam for explicit surface reconstruction
Venue: arXiv cs.CV 2025
Year: 2025

Neural radiance fields (NeRF) have driven impressive progress in view synthesis by using ray-traced volumetric rendering. Splatting-based methods such as 3D Gaussian Splatting (3DGS) provide faster rendering by rasterizing 3D primitives. RadiantFoam (RF) brought ray tracing back, achieving throughput comparable to Gaussian Splatting by organizing radiance with an explicit Voronoi Diagram (VD). Yet, all the mentioned methods still struggle with precise mesh reconstruction. We address this gap by jointly learning an explicit VD with an implicit Signed Distance Field (SDF). The scene is optimized via ray tracing and regularized by an Eikonal objective. The SDF introduces metric-consistent isosurfaces, which, in turn, bias near-surface Voronoi cell faces to align with the zero level set. The resulting model produces crisper, view-consistent surfaces with fewer floaters and improved topology, while preserving photometric quality and maintaining training speed on par with RadiantFoam. Across diverse scenes, our hybrid implicit-explicit formulation, which we name SDFoam, substantially improves mesh reconstruction accuracy (Chamfer distance) with comparable appearance (PSNR, SSIM), without sacrificing efficiency.
KOSS: Kalman-Optimal Selective State Spaces for Long-Term Sequence Modeling
Venue: arXiv cs.LG 2025
Year: 2025

Recent selective state space models (SSMs), such as Mamba and Mamba-2, have demonstrated strong performance in sequence modeling owing to input-dependent selection mechanisms. However, these mechanisms lack theoretical grounding and cannot support context-aware selection from latent state dynamics. To address these limitations, we propose KOSS, a Kalman-optimal Selective State Space model that formulates selection as latent state uncertainty minimization. Derived from estimation theory, KOSS adopts a continuous-time latent update driven by a Kalman gain that dynamically modulates information propagation based on content and context, enabling a closed-loop, context-aware selectivity mechanism. To ensure stable computation and near-linear scalability, KOSS employs global spectral differentiation for frequency-domain derivative estimation, along with a segment-wise scan for hardware-efficient processing. On a selective copying task with distractors, KOSS achieves over 79\% accuracy while baselines drop below 20\%, demonstrating robust context-aware selection. Furthermore, across nine long-term forecasting benchmarks, KOSS reduces MSE by 2.92--36.23\% and consistently outperforms state-of-the-art models in both accuracy and stability. To assess real-world applicability, a case study on secondary surveillance radar (SSR) tracking confirms KOSS's robustness under irregular intervals and noisy conditions and demonstrates its effectiveness in real-world applications. Finally, supplementary experiments verify Kalman gain convergence and the frequency response of spectral differentiation, providing theoretical support for the proposed closed-loop design.
Discovering and Learning Probabilistic Models of Black-Box AI Capabilities
Venue: arXiv cs.AI 2025
Year: 2025

Black-box AI (BBAI) systems such as foundational models are increasingly being used for sequential decision making. To ensure that such systems are safe to operate and deploy, it is imperative to develop efficient methods that can provide a sound and interpretable representation of the BBAI's capabilities. This paper shows that PDDL-style representations can be used to efficiently learn and model an input BBAI's planning capabilities. It uses the Monte-Carlo tree search paradigm to systematically create test tasks, acquire data, and prune the hypothesis space of possible symbolic models. Learned models describe a BBAI's capabilities, the conditions under which they can be executed, and the possible outcomes of executing them along with their associated probabilities. Theoretical results show soundness, completeness and convergence of the learned models. Empirical results with multiple BBAI systems illustrate the scope, efficiency, and accuracy of the presented methods.
Secure Event-triggered MolecularvCommunication - Information Theoretic Perspective and Optimal Performance
Venue: arXiv cs.IT 2025
Year: 2025

Molecular Communication (MC) is an emerging field of research focused on understanding how cells in the human body communicate and exploring potential medical applications. In theoretical analysis, the goal is to investigate cellular communication mechanisms and develop nanomachine-assisted therapies to combat diseases. Since cells transmit information by releasing molecules at varying intensities, this process is commonly modeled using Poisson channels. In our study, we consider a discrete-time Poisson channel (DTPC). MC is often event-driven, making traditional Shannon communication an unsuitable performance metric. Instead, we adopt the identification framework introduced by Ahlswede and Dueck. In this approach, the receiver is only concerned with detecting whether a specific message of interest has been transmitted. Unlike Shannon transmission codes, the size of identification (ID) codes for a discrete memoryless channel (DMC) increases doubly exponentially with blocklength when using randomized encoding. This remarkable property makes the ID paradigm significantly more efficient than classical Shannon transmission in terms of energy consumption and hardware requirements. Another critical aspect of MC, influenced by the concept of the Internet of Bio-NanoThings, is security. In-body communication must be protected against potential eavesdroppers. To address this, we first analyze the DTPC for randomized identification (RI) and then extend our study to secure randomized identification (SRI). We derive capacity formulas for both RI and SRI, providing a comprehensive understanding of their performance and security implications.
FlowDet: Unifying Object Detection and Generative Transport Flows
Venue: arXiv cs.CV 2025
Year: 2025

We present FlowDet, the first formulation of object detection using modern Conditional Flow Matching techniques. This work follows from DiffusionDet, which originally framed detection as a generative denoising problem in the bounding box space via diffusion. We revisit and generalise this formulation to a broader class of generative transport problems, while maintaining the ability to vary the number of boxes and inference steps without re-training. In contrast to the curved stochastic transport paths induced by diffusion, FlowDet learns simpler and straighter paths resulting in faster scaling of detection performance as the number of inference steps grows. We find that this reformulation enables us to outperform diffusion based detection systems (as well as non-generative baselines) across a wide range of experiments, including various precision/recall operating points using multiple feature backbones and datasets. In particular, when evaluating under recall-constrained settings, we can highlight the effects of the generative transport without over-compensating with large numbers of proposals. This provides gains of up to +3.6% AP and +4.2% AP$_{rare}$ over DiffusionDet on the COCO and LVIS datasets, respectively.
R3ST: A Synthetic 3D Dataset With Realistic Trajectories
Venue: arXiv cs.CV 2025
Year: 2025

Datasets are essential to train and evaluate computer vision models used for traffic analysis and to enhance road safety. Existing real datasets fit real-world scenarios, capturing authentic road object behaviors, however, they typically lack precise ground-truth annotations. In contrast, synthetic datasets play a crucial role, allowing for the annotation of a large number of frames without additional costs or extra time. However, a general drawback of synthetic datasets is the lack of realistic vehicle motion, since trajectories are generated using AI models or rule-based systems. In this work, we introduce R3ST (Realistic 3D Synthetic Trajectories), a synthetic dataset that overcomes this limitation by generating a synthetic 3D environment and integrating real-world trajectories derived from SinD, a bird's-eye-view dataset recorded from drone footage. The proposed dataset closes the gap between synthetic data and realistic trajectories, advancing the research in trajectory forecasting of road vehicles, offering both accurate multimodal ground-truth annotations and authentic human-driven vehicle trajectories.
Delay-Aware Multi-Stage Edge Server Upgrade with Budget Constraint
Venue: arXiv cs.DC 2025
Year: 2025

In this paper, the Multi-stage Edge Server Upgrade (M-ESU) is proposed as a new network planning problem, involving the upgrading of an existing multi-access edge computing (MEC) system through multiple stages (e.g., over several years). More precisely, the problem considers two key decisions: (i) whether to deploy additional edge servers or upgrade those already installed, and (ii) how tasks should be offloaded so that the average number of tasks that meet their delay requirement is maximized. The framework specifically involves: (i) deployment of new servers combined with capacity upgrades for existing servers, and (ii) the optimal task offloading to maximize the average number of tasks with a delay requirement. It also considers the following constraints: (i) budget per stage, (ii) server deployment and upgrade cost (in $) and cost depreciation rate, (iii) computation resource of servers, (iv) number of tasks and their growth rate (in %), and (v) the increase in task sizes and stricter delay requirements over time. We present two solutions: a Mixed Integer Linear Programming (MILP) model and an efficient heuristic algorithm (M-ESU/H). MILP yields the optimal solution for small networks, whereas M-ESU/H is used in large-scale networks. For small networks, the simulation results show that the solution computed by M-ESU/H is within 1.25% of the optimal solution while running several orders of magnitude faster. For large networks, M-ESU/H is compared against three alternative heuristic solutions that consider only server deployment, or giving priority to server deployment or upgrade. Our experiments show that M-ESU/H yields up to 21.57% improvement in task satisfaction under identical budget and demand growth conditions, confirming its scalability and practical value for long-term MEC systems.
From Facts to Conclusions : Integrating Deductive Reasoning in Retrieval-Augmented LLMs
Venue: arXiv cs.CL 2025
Year: 2025

Retrieval-Augmented Generation (RAG) grounds large language models (LLMs) in external evidence, but fails when retrieved sources conflict or contain outdated or subjective information. Prior work address these issues independently but lack unified reasoning supervision. We propose a reasoning-trace-augmented RAG framework that adds structured, interpretable reasoning across three stages : (1) document-level adjudication, (2) conflict analysis, and (3) grounded synthesis, producing citation-linked answers or justified refusals. A Conflict-Aware Trust-Score (CATS) pipeline is introduced which evaluates groundedness, factual correctness, refusal accuracy, and conflict-behavior alignment using an LLM-as-a-Judge. Our 539-query reasoning dataset and evaluation pipeline establish a foundation for conflict-aware, interpretable RAG systems. Experimental results demonstrate substantial gains over baselines, most notably with Qwen, where Supervised Fine-Tuning improved End-to-End answer correctness from 0.069 to 0.883 and behavioral adherence from 0.074 to 0.722.
Exploration of Augmentation Strategies in Multi-modal Retrieval-Augmented Generation for the Biomedical Domain: A Case Study Evaluating Question Answering in Glycobiology
Venue: arXiv cs.CL 2025
Year: 2025

Multi-modal retrieval-augmented generation (MM-RAG) promises grounded biomedical QA, but it is unclear when to (i) convert figures/tables into text versus (ii) use optical character recognition (OCR)-free visual retrieval that returns page images and leaves interpretation to the generator. We study this trade-off in glycobiology, a visually dense domain. We built a benchmark of 120 multiple-choice questions (MCQs) from 25 papers, stratified by retrieval difficulty (easy text, medium figures/tables, hard cross-evidence). We implemented four augmentations-None, Text RAG, Multi-modal conversion, and late-interaction visual retrieval (ColPali)-using Docling parsing and Qdrant indexing. We evaluated mid-size open-source and frontier proprietary models (e.g., Gemma-3-27B-IT, GPT-4o family). Additional testing used the GPT-5 family and multiple visual retrievers (ColPali/ColQwen/ColFlor). Accuracy with Agresti-Coull 95% confidence intervals (CIs) was computed over 5 runs per configuration. With Gemma-3-27B-IT, Text and Multi-modal augmentation outperformed OCR-free retrieval (0.722-0.740 vs. 0.510 average accuracy). With GPT-4o, Multi-modal achieved 0.808, with Text 0.782 and ColPali 0.745 close behind; within-model differences were small. In follow-on experiments with the GPT-5 family, the best results with ColPali and ColFlor improved by ~2% to 0.828 in both cases. In general, across the GPT-5 family, ColPali, ColQwen, and ColFlor were statistically indistinguishable. GPT-5-nano trailed larger GPT-5 variants by roughly 8-10%. Pipeline choice is capacity-dependent: converting visuals to text lowers the reader burden and is more reliable for mid-size models, whereas OCR-free visual retrieval becomes competitive under frontier models. Among retrievers, ColFlor offers parity with heavier options at a smaller footprint, making it an efficient default when strong generators are available.