OpenRaiser/Intern-Atlas huggingface dataset:
One-Shot Secure Aggregation: A Hybrid Cryptographic Protocol for Private Federated Learning in IoT
Venue: arXiv cs.CR 2025
Year: 2025

Federated Learning (FL) offers a promising approach to collaboratively train machine learning models without centralizing raw data, yet its scalability is often throttled by excessive communication overhead. This challenge is magnified in Internet of Things (IoT) environments, where devices face stringent bandwidth, latency, and energy constraints. Conventional secure aggregation protocols, while essential for protecting model updates, frequently require multiple interaction rounds, large payload sizes, and per-client costs rendering them impractical for many edge deployments. In this work, we present Hyb-Agg, a lightweight and communication-efficient secure aggregation protocol that integrates Multi-Key CKKS (MK-CKKS) homomorphic encryption with Elliptic Curve Diffie-Hellman (ECDH)-based additive masking. Hyb-Agg reduces the secure aggregation process to a single, non-interactive client-to-server transmission per round, ensuring that per-client communication remains constant regardless of the number of participants. This design eliminates partial decryption exchanges, preserves strong privacy under the RLWE, CDH, and random oracle assumptions, and maintains robustness against collusion by the server and up to $N-2$ clients. We implement and evaluate Hyb-Agg on both high-performance and resource-constrained devices, including a Raspberry Pi 4, demonstrating that it delivers sub-second execution times while achieving a constant communication expansion factor of approximately 12x over plaintext size. By directly addressing the communication bottleneck, Hyb-Agg enables scalable, privacy-preserving federated learning that is practical for real-world IoT deployments.
Time Series Forecasting via Direct Per-Step Probability Distribution Modeling
Venue: arXiv cs.LG 2025
Year: 2025

Deep neural network-based time series prediction models have recently demonstrated superior capabilities in capturing complex temporal dependencies. However, it is challenging for these models to account for uncertainty associated with their predictions, because they directly output scalar values at each time step. To address such a challenge, we propose a novel model named interleaved dual-branch Probability Distribution Network (interPDN), which directly constructs discrete probability distributions per step instead of a scalar. The regression output at each time step is derived by computing the expectation of the predictive distribution on a predefined support set. To mitigate prediction anomalies, a dual-branch architecture is introduced with interleaved support sets, augmented by coarse temporal-scale branches for long-term trend forecasting. Outputs from another branch are treated as auxiliary signals to impose self-supervised consistency constraints on the current branch's prediction. Extensive experiments on multiple real-world datasets demonstrate the superior performance of interPDN.
OctoMed: Data Recipes for State-of-the-Art Multimodal Medical Reasoning
Venue: arXiv cs.AI 2025
Year: 2025

High-quality and carefully curated data is a cornerstone of training medical large language models, as it directly impacts both generalization and robustness to unseen clinical tasks. We investigate strategies for training and data curation to develop a robust multimodal reasoning model in the medical domain. Our work focuses on supervised fine-tuning (SFT) and explores data recipes that leverage structured reasoning traces. Using our proposed data recipe, we scale experiments to a dataset of over 8 million examples and 6.8 billion response tokens, achieving state-of-the-art performance among open-source models across diverse out-of-distribution medical benchmark tasks. Our results further indicate that curating a high-quality, diverse training dataset with varying structured reasoning trace lengths enables the fine-tuned model to self-calibrate its reasoning trajectory lengths based on the downstream task, without explicit supervision. We present key insights, describe the data curation strategy, and outline next steps toward developing robust medical vision-language reasoning system.
Beyond Curve Fitting: Neuro-Symbolic Agents for Context-Aware Epidemic Forecasting
Venue: arXiv cs.LG 2025
Year: 2025

Effective surveillance of hand, foot and mouth disease (HFMD) requires forecasts accounting for epidemiological patterns and contextual drivers like school calendars and weather. While classical models and recent foundation models (e.g., Chronos, TimesFM) incorporate covariates, they often lack the semantic reasoning to interpret the causal interplay between conflicting drivers. In this work, we propose a two-agent framework decoupling contextual interpretation from probabilistic forecasting. An LLM "event interpreter" processes heterogeneous signals-including school schedules, meteorological summaries, and reports-into a scalar transmission-impact signal. A neuro-symbolic core then combines this with historical case counts to produce calibrated probabilistic forecasts. We evaluate the framework on real-world HFMD datasets from Hong Kong (2023-2024) and Lishui, China (2024). Compared to traditional and foundation-model baselines, our approach achieves competitive point forecasting accuracy while providing robust 90% prediction intervals (coverage 0.85-1.00) and human-interpretable rationales. Our results suggest that structurally integrating domain knowledge through LLMs can match state-of-the-art performance while yielding context-aware forecasts that align with public health workflows. Code is available at https://github.com/jw-chae/forecast_MED .
MCP vs RAG vs NLWeb vs HTML: A Comparison of the Effectiveness and Efficiency of Different Agent Interfaces to the Web (Technical Report)
Venue: arXiv cs.CL 2025
Year: 2025

Large language model agents are increasingly used to automate web tasks such as product search, offer comparison, and checkout. Current research explores different interfaces through which these agents interact with websites, including traditional HTML browsing, retrieval-augmented generation (RAG) over pre-crawled content, communication via Web APIs using the Model Context Protocol (MCP), and natural-language querying through the NLWeb interface. However, no prior work has compared these four architectures within a single controlled environment using identical tasks. To address this gap, we introduce a testbed consisting of four simulated e-shops, each offering its products via HTML, MCP, and NLWeb interfaces. For each interface (HTML, RAG, MCP, and NLWeb) we develop specialized agents that perform the same sets of tasks, ranging from simple product searches and price comparisons to complex queries for complementary or substitute products and checkout processes. We evaluate the agents using GPT 4.1, GPT 5, GPT 5 mini, and Claude Sonnet 4 as underlying LLM. Our evaluation shows that the RAG, MCP and NLWeb agents outperform HTML on both effectiveness and efficiency. Averaged over all tasks, F1 rises from 0.67 for HTML to between 0.75 and 0.77 for the other agents. Token usage falls from about 241k for HTML to between 47k and 140k per task. The runtime per task drops from 291 seconds to between 50 and 62 seconds. The best overall configuration is RAG with GPT 5 achieving an F1 score of 0.87 and a completion rate of 0.79. Also taking cost into consideration, RAG with GPT 5 mini offers a good compromise between API usage fees and performance. Our experiments show the choice of the interaction interface has a substantial impact on both the effectiveness and efficiency of LLM-based web agents.
Multi-Modal Scene Graph with Kolmogorov-Arnold Experts for Audio-Visual Question Answering
Venue: arXiv cs.AI 2025
Year: 2025

In this paper, we propose a novel Multi-Modal Scene Graph with Kolmogorov-Arnold Expert Network for Audio-Visual Question Answering (SHRIKE). The task aims to mimic human reasoning by extracting and fusing information from audio-visual scenes, with the main challenge being the identification of question-relevant cues from the complex audio-visual content. Existing methods fail to capture the structural information within video, and suffer from insufficient fine-grained modeling of multi-modal features. To address these issues, we are the first to introduce a new multi-modal scene graph that explicitly models the objects and their relationship as a visually grounded, structured representation of the audio-visual scene. Furthermore, we design a Kolmogorov-Arnold Network~(KAN)-based Mixture of Experts (MoE) to enhance the expressive power of the temporal integration stage. This enables more fine-grained modeling of cross-modal interactions within the question-aware fused audio-visual representation, leading to capture richer and more nuanced patterns and then improve temporal reasoning performance. We evaluate the model on the established MUSIC-AVQA and MUSIC-AVQA v2 benchmarks, where it achieves state-of-the-art performance. Code and model checkpoints will be publicly released.
Toward Automatic Safe Driving Instruction: A Large-Scale Vision Language Model Approach
Venue: arXiv cs.CV 2025
Year: 2025

Large-scale Vision Language Models (LVLMs) exhibit advanced capabilities in tasks that require visual information, including object detection. These capabilities have promising applications in various industrial domains, such as autonomous driving. For example, LVLMs can generate safety-oriented descriptions of videos captured by road-facing cameras. However, ensuring comprehensive safety requires monitoring driver-facing views as well to detect risky events, such as the use of mobiles while driving. Thus, the ability to process synchronized inputs is necessary from both driver-facing and road-facing cameras. In this study, we develop models and investigate the capabilities of LVLMs by constructing a dataset and evaluating their performance on this dataset. Our experimental results demonstrate that while pre-trained LVLMs have limited effectiveness, fine-tuned LVLMs can generate accurate and safety-aware driving instructions. Nonetheless, several challenges remain, particularly in detecting subtle or complex events in the video. Our findings and error analysis provide valuable insights that can contribute to the improvement of LVLM-based systems in this domain.
Efficient Estimation of Sum-Parameters for Multi-Component Complex Exponential Signals with Theoretical Cramer-Rao Bound Analysis
Venue: arXiv cs.IT 2025
Year: 2025

This paper addresses the challenging problem of parameter estimation for multicomponent complex exponential signals, commonly known as sums of cisoids. Traditional approaches that estimate individual component parameters face significant difficulties when the number of components is large, including permutation ambiguity, computational complexity from high-dimensional Fisher information matrix inversion, and model order selection issues. We introduce a novel framework based on low-dimensional sum-parameters that capture essential global characteristics of the signal ensemble. These parameters include the sum of amplitudes, the power-weighted frequency, and the phase-related sum. These quantities possess clear physical interpretations representing total signal strength, power-weighted average frequency, and composite phase information, while completely avoiding permutation ambiguities. We derive exact closed-form Cramer-Rao bounds for these sum-parameters under both deterministic and stochastic signal models. Our analysis reveals that the frequency sumparameter achieves statistical efficiency comparable to single-component estimators while automatically benefiting from power pooling across all signal components. The proposed Efficient Global Estimation Method (EGEM) demonstrates asymptotic efficiency across a wide range of signal-to-noise ratios, significantly outperforming established techniques such as Zoom-Interpolated FFT and Root-MUSIC in both long- and short-sample regimes. Extensive numerical simulations involving 2000 Monte-Carlo trials confirm that EGEM closely approaches the theoretical performance bounds even with relatively small sample sizes of 250 observations.
Chart2Code-MoLA: Efficient Multi-Modal Code Generation via Adaptive Expert Routing
Venue: arXiv cs.SE 2025
Year: 2025

Chart-to-code generation is a critical task in automated data visualization, translating complex chart structures into executable programs. While recent Multi-modal Large Language Models (MLLMs) improve chart representation, existing approaches still struggle to achieve cross-type generalization, memory efficiency, and modular design. To address these challenges, this paper proposes C2C-MoLA, a multimodal framework that synergizes Mixture of Experts (MoE) with Low-Rank Adaptation (LoRA). The MoE component uses a complexity-aware routing mechanism with domain-specialized experts and load-balanced sparse gating, dynamically allocating inputs based on learnable structural metrics like element count and chart complexity. LoRA enables parameter-efficient updates for resource-conscious tuning, further supported by a tailored training strategy that aligns routing stability with semantic accuracy. Experiments on Chart2Code-160k show that the proposed model improves generation accuracy by up to 17%, reduces peak GPU memory by 18%, and accelerates convergence by 20%, when compared to standard fine-tuning and LoRA-only baselines, particularly on complex charts. Ablation studies validate optimal designs, such as 8 experts and rank-8 LoRA, and confirm scalability for real-world multimodal code generation.
Predicting Startup-VC Fund Matches with Structural Embeddings and Temporal Investment Data
Venue: arXiv cs.CE 2025
Year: 2025

This study proposes a method for predicting startup inclusion, estimating the probability that a venture capital fund will invest in a given startup. Unlike general recommendation systems, which typically rank multiple candidates, our approach formulates the problem as a binary classification task tailored to each fund-startup pair. Each startup is represented by integrating textual, numerical, and structural features, with Node2Vec capturing network context and multihead attention enabling feature fusion. Fund investment histories are encoded as LSTM based sequences of past investees. Experiments on Japanese startup data demonstrate that the proposed method achieves higher accuracy than a static baseline. The results indicate that incorporating structural features and modeling temporal investment dynamics are effective in capturing fund-startup compatibility.
Scaling HuBERT for African Languages: From Base to Large and XL
Venue: arXiv cs.CL 2025
Year: 2025

Despite recent progress in multilingual speech processing, African languages remain under-represented in both research and deployed systems, particularly when it comes to strong, open-weight encoders that transfer well under low-resource supervision. Self-supervised learning has proven especially promising in such settings, yet most publicly released models targeting African speech remain at BASE scale, leaving unanswered whether larger encoders, trained exclusively on Africa-centric audio, offer tangible benefits and how model capacity interacts with data composition. This work addresses that gap by introducing SSA-HuBERT-Large (317M parameters) and SSA-HuBERT-XL (964M parameters), the first large models trained solely on African speech, alongside a BASE size counterpart. We release these models as open weights: see https://huggingface.co/collections/Orange/african-speech-foundation-models. By conducting a carefully controlled experimental study focused exclusively on Sub-Saharan languages, covering automatic speech recognition (ASR) and language identification (LID) tasks, we demonstrate that larger architectures significantly improve performance by effectively leveraging large audio datasets.
Joint Resource Allocation to Transparently Integrate 5G TDD Uplink with Time-Aware TSN
Venue: arXiv cs.NI 2025
Year: 2025

To enable mobility in industrial communication systems, the seamless integration of 5G with Time-Sensitive Networking (TSN) is a promising approach. Deterministic communication across heterogeneous 5G-TSN systems requires joint scheduling between both domains. A key prerequisite for time-aware end-to-end scheduling is determining the forwarding delay for each TSN Traffic Class at every bridge, referred to as Bridge Delay (BD). Hence, to integrate 5G as a transparent TSN bridge, the 5G BD must be determined and guaranteed. Unlike wired bridges, the 5G BD relies on wireless resource management characteristics, such as the Time Division Duplex pattern and radio resource allocation procedure. In particular, traditional Uplink (UL) schedulers are optimized for throughput but often fail to meet the deadline requirements. To address this challenge, we propose a heterogeneous radio resource scheduler that integrates static and dynamic scheduling. The algorithm pre-allocates resources for time-sensitive periodic streams based on the reported BDs, ensuring alignment with the TSN mechanisms Time-Aware Shaper and Per-Stream Filtering and Policing. Meanwhile, remaining resources are dynamically allocated to non-deterministic flows using established strategies such as Proportional Fair, Max C/I, or a Quality of Service-aware priority-based scheduler. The scheduler's performance is evaluated through OMNeT++ simulations. The results demonstrate support for diverse TSN flows while ensuring deadline-aware scheduling of time-sensitive UL traffic in mobility scenarios. Periodic time-sensitive flows are end-to-end scheduled across domains, improving the resource efficiency by 28% compared to the Configured Grant baseline. While reliability is preserved, non-deterministic rate-sensitive flows benefit from the improved resource utilization, resulting in higher throughput
AugGen: Augmenting Task-Based Learning in Professional Creative Software with LLM-Generated Scaffolded UIs
Venue: arXiv cs.HC 2025
Year: 2025

Professional creative software often presents steep learning curves due to complex interfaces, lack of structured task-aware guidance, and unfamiliar domain terminology. To address these challenges and augment user learning experience, we introduce AugGen, a method for generating scaffolded user interfaces that simplify interface complexity and support task-based learning. With the user's task, our method surfaces task-relevant tools to reduce distracting features, organizes the tools around task workflow stages to offer execution guidance, connects tools with domain concepts to foster learning engagement, and progressively discloses advanced features to manage learning progress. To evaluate the method, we used our LLM-assisted pipeline to generate two task-specific scaffolded UIs and deployed them in Blender, our professional 3D modeling testbed. We invited both beginner (N=32) and expert (N=8) users to evaluate our implemented interfaces. Results show that the scaffolded interfaces significantly reduced user-perceived task load, enhanced task performance via embedded guidance, and augmented concept learning during task execution.
MegaChat: A Synthetic Persian Q&A Dataset for High-Quality Sales Chatbot Evaluation
Venue: arXiv cs.CL 2025
Year: 2025

Small and medium-sized enterprises (SMEs) in Iran increasingly leverage Telegram for sales, where real-time engagement is essential for conversion. However, developing AI-driven chatbots for this purpose requires large, high-quality question-and-answer (Q&A) datasets, which are typically expensive and resource-intensive to produce, especially for low-resource languages like Persian. In this paper, we introduce MegaChat, the first fully synthetic Persian Q&A dataset designed to evaluate intelligent sales chatbots in Telegram-based e-commerce. We propose a novel, automated multi-agent architecture that generates persona-aware Q&A pairs by collecting data from active Telegram shopping channels. The system employs specialized agents for question generation, validation, and refinement, ensuring the production of realistic and diverse conversational data. To evaluate answer generation, we compare three classic retrieval-augmented generation (RAG) models with our advanced agentic system, which features multi-query retrieval, reranking, and persona-aligned response synthesis. Using GPT-5.1 for evaluation across six quality dimensions, our results show that the agentic architecture outperformed traditional RAG models in 4 out of 5 diverse channels, demonstrating its ability to generate scalable, high-quality datasets without relying on expensive human annotation or complex fine-tuning. MegaChat provides SMEs with an efficient, cost-effective solution for building intelligent customer engagement systems in specialized commercial domains, enabling advancements in multilingual conversational AI for low-resource languages. Download: https://github.com/MegaChat-Tech/MegaChat-DataSet
MANTA: Physics-Informed Generalized Underwater Object Tracking
Venue: arXiv cs.CV 2025
Year: 2025

Underwater object tracking is challenging due to wavelength dependent attenuation and scattering, which severely distort appearance across depths and water conditions. Existing trackers trained on terrestrial data fail to generalize to these physics-driven degradations. We present MANTA, a physics-informed framework integrating representation learning with tracking design for underwater scenarios. We propose a dual-positive contrastive learning strategy coupling temporal consistency with Beer-Lambert augmentations to yield features robust to both temporal and underwater distortions. We further introduce a multi-stage pipeline augmenting motion-based tracking with a physics-informed secondary association algorithm that integrates geometric consistency and appearance similarity for re-identification under occlusion and drift. To complement standard IoU metrics, we propose Center-Scale Consistency (CSC) and Geometric Alignment Score (GAS) to assess geometric fidelity. Experiments on four underwater benchmarks (WebUOT-1M, UOT32, UTB180, UWCOT220) show that MANTA achieves state-of-the-art performance, improving Success AUC by up to 6 percent, while ensuring stable long-term generalized underwater tracking and efficient runtime.