OpenRaiser/Intern-Atlas huggingface dataset:
Intent Aware Context Retrieval for Multi-Turn Agricultural Question Answering
Venue: arXiv cs.CL 2025
Year: 2025

Indian farmers often lack timely, accessible, and language-friendly agricultural advice, especially in rural areas with low literacy. To address this gap in accessibility, this paper presents a novel AI-powered agricultural chatbot, Krishi Sathi, designed to support Indian farmers by providing personalized, easy-to-understand answers to their queries through both text and speech. The system's intelligence stems from an IFT model, subsequently refined through fine-tuning on Indian agricultural knowledge across three curated datasets. Unlike traditional chatbots that respond to one-off questions, Krishi Sathi follows a structured, multi-turn conversation flow to gradually collect the necessary details from the farmer, ensuring the query is fully understood before generating a response. Once the intent and context are extracted, the system performs Retrieval-Augmented Generation (RAG) by first fetching information from a curated agricultural database and then generating a tailored response using the IFT model. The chatbot supports both English and Hindi languages, with speech input and output features (via ASR and TTS) to make it accessible for users with low literacy or limited digital skills. This work demonstrates how combining intent-driven dialogue flows, instruction-tuned models, and retrieval-based generation can improve the quality and accessibility of digital agricultural support in India. This approach yielded strong results, with the system achieving a query response accuracy of 97.53%, 91.35% contextual relevance and personalization, and a query completion rate of 97.53%. The average response time remained under 6 seconds, ensuring timely support for users across both English and Hindi interactions.
Multimodal Video Emotion Recognition with Reliable Reasoning Priors
Venue: arXiv cs.CV 2025
Year: 2025

This study investigates the integration of trustworthy prior reasoning knowledge from MLLMs into multimodal emotion recognition. We employ Gemini to generate fine-grained, modality-separable reasoning traces, which are injected as priors during the fusion stage to enrich cross-modal interactions. To mitigate the pronounced class-imbalance in multimodal emotion recognition, we introduce Balanced Dual-Contrastive Learning, a loss formulation that jointly balances inter-class and intra-class distributions. Applied to the MER2024 benchmark, our prior-enhanced framework yields substantial performance gains, demonstrating that the reliability of MLLM-derived reasoning can be synergistically combined with the domain adaptability of lightweight fusion networks for robust, scalable emotion recognition.
Hierarchical Verification of Speculative Beams for Accelerating LLM Inference
Venue: arXiv cs.CL 2025
Year: 2025

Large language models (LLMs) have achieved remarkable success across diverse natural language processing tasks but face persistent challenges in inference efficiency due to their autoregressive nature. While speculative decoding and beam sampling offer notable improvements, traditional methods verify draft sequences sequentially without prioritization, leading to unnecessary computational overhead. This work proposes the Hierarchical Verification Tree (HVT), a novel framework that restructures speculative beam decoding by prioritizing high-likelihood drafts and enabling early pruning of suboptimal candidates. Theoretical foundations and a formal verification-pruning algorithm are developed to ensure correctness and efficiency. Integration with standard LLM inference pipelines is achieved without requiring retraining or architecture modification. Experimental evaluations across multiple datasets and models demonstrate that HVT consistently outperforms existing speculative decoding schemes, achieving substantial reductions in inference time and energy consumption while maintaining or enhancing output quality. The findings highlight the potential of hierarchical verification strategies as a new direction for accelerating large language model inference.
PILOT-C: Physics-Informed Low-Distortion Optimal Trajectory Compression
Venue: arXiv cs.LG 2025
Year: 2025

Location-aware devices continuously generate massive volumes of trajectory data, creating demand for efficient compression. Line simplification is a common solution but typically assumes 2D trajectories and ignores time synchronization and motion continuity. We propose PILOT-C, a novel trajectory compression framework that integrates frequency-domain physics modeling with error-bounded optimization. Unlike existing line simplification methods, PILOT-C supports trajectories in arbitrary dimensions, including 3D, by compressing each spatial axis independently. Evaluated on four real-world datasets, PILOT-C achieves superior performance across multiple dimensions. In terms of compression ratio, PILOT-C outperforms CISED-W, the current state-of-the-art SED-based line simplification algorithm, by an average of 19.2%. For trajectory fidelity, PILOT-C achieves an average of 32.6% reduction in error compared to CISED-W. Additionally, PILOT-C seamlessly extends to three-dimensional trajectories while maintaining the same computational complexity, achieving a 49% improvement in compression ratios over SQUISH-E, the most efficient line simplification algorithm on 3D datasets.
CX-Mind: A Pioneering Multimodal Large Language Model for Interleaved Reasoning in Chest X-ray via Curriculum-Guided Reinforcement Learning
Venue: arXiv cs.LG 2025
Year: 2025

Chest X-ray (CXR) imaging is one of the most widely used diagnostic modalities in clinical practice, encompassing a broad spectrum of diagnostic tasks. Recent advancements have seen the extensive application of reasoning-based multimodal large language models (MLLMs) in medical imaging to enhance diagnostic efficiency and interpretability. However, existing multimodal models predominantly rely on "one-time" diagnostic approaches, lacking verifiable supervision of the reasoning process. This leads to challenges in multi-task CXR diagnosis, including lengthy reasoning, sparse rewards, and frequent hallucinations. To address these issues, we propose CX-Mind, the first generative model to achieve interleaved "think-answer" reasoning for CXR tasks, driven by curriculum-based reinforcement learning and verifiable process rewards (CuRL-VPR). Specifically, we constructed an instruction-tuning dataset, CX-Set, comprising 708,473 images and 2,619,148 samples, and generated 42,828 high-quality interleaved reasoning data points supervised by clinical reports. Optimization was conducted in two stages under the Group Relative Policy Optimization framework: initially stabilizing basic reasoning with closed-domain tasks, followed by transfer to open-domain diagnostics, incorporating rule-based conditional process rewards to bypass the need for pretrained reward models. Extensive experimental results demonstrate that CX-Mind significantly outperforms existing medical and general-domain MLLMs in visual understanding, text generation, and spatiotemporal alignment, achieving an average performance improvement of 25.1% over comparable CXR-specific models. On real-world clinical dataset (Rui-CXR), CX-Mind achieves a mean recall@1 across 14 diseases that substantially surpasses the second-best results, with multi-center expert evaluations further confirming its clinical utility across multiple dimensions.
GlaBoost: A multimodal Structured Framework for Glaucoma Risk Stratification
Venue: arXiv cs.LG 2025
Year: 2025

Early and accurate detection of glaucoma is critical to prevent irreversible vision loss. However, existing methods often rely on unimodal data and lack interpretability, limiting their clinical utility. In this paper, we present GlaBoost, a multimodal gradient boosting framework that integrates structured clinical features, fundus image embeddings, and expert-curated textual descriptions for glaucoma risk prediction. GlaBoost extracts high-level visual representations from retinal fundus photographs using a pretrained convolutional encoder and encodes free-text neuroretinal rim assessments using a transformer-based language model. These heterogeneous signals, combined with manually assessed risk scores and quantitative ophthalmic indicators, are fused into a unified feature space for classification via an enhanced XGBoost model. Experiments conducted on a real-world annotated dataset demonstrate that GlaBoost significantly outperforms baseline models, achieving a validation accuracy of 98.71%. Feature importance analysis reveals clinically consistent patterns, with cup-to-disc ratio, rim pallor, and specific textual embeddings contributing most to model decisions. GlaBoost offers a transparent and scalable solution for interpretable glaucoma diagnosis and can be extended to other ophthalmic disorders.
LRTuckerRep: Low-rank Tucker Representation Model for Multi-dimensional Data Completion
Venue: arXiv cs.LG 2025
Year: 2025

Multi-dimensional data completion is a critical problem in computational sciences, particularly in domains such as computer vision, signal processing, and scientific computing. Existing methods typically leverage either global low-rank approximations or local smoothness regularization, but each suffers from notable limitations: low-rank methods are computationally expensive and may disrupt intrinsic data structures, while smoothness-based approaches often require extensive manual parameter tuning and exhibit poor generalization. In this paper, we propose a novel Low-Rank Tucker Representation (LRTuckerRep) model that unifies global and local prior modeling within a Tucker decomposition. Specifically, LRTuckerRep encodes low rankness through a self-adaptive weighted nuclear norm on the factor matrices and a sparse Tucker core, while capturing smoothness via a parameter-free Laplacian-based regularization on the factor spaces. To efficiently solve the resulting nonconvex optimization problem, we develop two iterative algorithms with provable convergence guarantees. Extensive experiments on multi-dimensional image inpainting and traffic data imputation demonstrate that LRTuckerRep achieves superior completion accuracy and robustness under high missing rates compared to baselines.
Development of management systems using artificial intelligence systems and machine learning methods for boards of directors (preprint, unofficial translation)
Venue: arXiv cs.CY 2025
Year: 2025

The study addresses the paradigm shift in corporate management, where AI is moving from a decision support tool to an autonomous decision-maker, with some AI systems already appointed to leadership roles in companies. A central problem identified is that the development of AI technologies is far outpacing the creation of adequate legal and ethical guidelines. The research proposes a "reference model" for the development and implementation of autonomous AI systems in corporate management. This model is based on a synthesis of several key components to ensure legitimate and ethical decision-making. The model introduces the concept of "computational law" or "algorithmic law". This involves creating a separate legal framework for AI systems, with rules and regulations translated into a machine-readable, algorithmic format to avoid the ambiguity of natural language. The paper emphasises the need for a "dedicated operational context" for autonomous AI systems, analogous to the "operational design domain" for autonomous vehicles. This means creating a specific, clearly defined environment and set of rules within which the AI can operate safely and effectively. The model advocates for training AI systems on controlled, synthetically generated data to ensure fairness and ethical considerations are embedded from the start. Game theory is also proposed as a method for calculating the optimal strategy for the AI to achieve its goals within these ethical and legal constraints. The provided analysis highlights the importance of explainable AI (XAI) to ensure the transparency and accountability of decisions made by autonomous systems. This is crucial for building trust and for complying with the "right to explanation".
When Agents Break Down in Multiagent Path Finding
Venue: arXiv cs.MA 2025
Year: 2025

In Multiagent Path Finding (MAPF), the goal is to compute efficient, collision-free paths for multiple agents navigating a network from their sources to targets, minimizing the schedule's makespan-the total time until all agents reach their destinations. We introduce a new variant that formally models scenarios where some agents may experience delays due to malfunctions, posing significant challenges for maintaining optimal schedules. Recomputing an entirely new schedule from scratch after each malfunction is often computationally infeasible. To address this, we propose a framework for dynamic schedule adaptation that does not rely on full replanning. Instead, we develop protocols enabling agents to locally coordinate and adjust their paths on the fly. We prove that following our primary communication protocol, the increase in makespan after k malfunctions is bounded by k additional turns, effectively limiting the impact of malfunctions on overall efficiency. Moreover, recognizing that agents may have limited computational capabilities, we also present a secondary protocol that shifts the necessary computations onto the network's nodes, ensuring robustness without requiring enhanced agent processing power. Our results demonstrate that these protocols provide a practical, scalable approach to resilient multiagent navigation in the face of agent failures.
Are Inherently Interpretable Models More Robust? A Study In Music Emotion Recognition
Venue: arXiv cs.SD 2025
Year: 2025

One of the desired key properties of deep learning models is the ability to generalise to unseen samples. When provided with new samples that are (perceptually) similar to one or more training samples, deep learning models are expected to produce correspondingly similar outputs. Models that succeed in predicting similar outputs for similar inputs are often called robust. Deep learning models, on the other hand, have been shown to be highly vulnerable to minor (adversarial) perturbations of the input, which manage to drastically change a model's output and simultaneously expose its reliance on spurious correlations. In this work, we investigate whether inherently interpretable deep models, i.e., deep models that were designed to focus more on meaningful and interpretable features, are more robust to irrelevant perturbations in the data, compared to their black-box counterparts. We test our hypothesis by comparing the robustness of an interpretable and a black-box music emotion recognition (MER) model when challenged with adversarial examples. Furthermore, we include an adversarially trained model, which is optimised to be more robust, in the comparison. Our results indicate that inherently more interpretable models can indeed be more robust than their black-box counterparts, and achieve similar levels of robustness as adversarially trained models, at lower computational cost.
AttnTrace: Attention-based Context Traceback for Long-Context LLMs
Venue: arXiv cs.CL 2025
Year: 2025

Long-context large language models (LLMs), such as Gemini-2.5-Pro and Claude-Sonnet-4, are increasingly used to empower advanced AI systems, including retrieval-augmented generation (RAG) pipelines and autonomous agents. In these systems, an LLM receives an instruction along with a context--often consisting of texts retrieved from a knowledge database or memory--and generates a response that is contextually grounded by following the instruction. Recent studies have designed solutions to trace back to a subset of texts in the context that contributes most to the response generated by the LLM. These solutions have numerous real-world applications, including performing post-attack forensic analysis and improving the interpretability and trustworthiness of LLM outputs. While significant efforts have been made, state-of-the-art solutions such as TracLLM often lead to a high computation cost, e.g., it takes TracLLM hundreds of seconds to perform traceback for a single response-context pair. In this work, we propose AttnTrace, a new context traceback method based on the attention weights produced by an LLM for a prompt. To effectively utilize attention weights, we introduce two techniques designed to enhance the effectiveness of AttnTrace, and we provide theoretical insights for our design choice. We also perform a systematic evaluation for AttnTrace. The results demonstrate that AttnTrace is more accurate and efficient than existing state-of-the-art context traceback methods. We also show that AttnTrace can improve state-of-the-art methods in detecting prompt injection under long contexts through the attribution-before-detection paradigm. As a real-world application, we demonstrate that AttnTrace can effectively pinpoint injected instructions in a paper designed to manipulate LLM-generated reviews. The code is at https://github.com/Wang-Yanting/AttnTrace.
A Type System for Data Privacy Compliance in Active Object Languages
Venue: arXiv cs.PL 2025
Year: 2025

Data protection laws such as GDPR aim to give users unprecedented control over their personal data. Compliance with these regulations requires systematically considering information flow and interactions among entities handling sensitive data. Privacy-by-design principles advocate embedding data protection into system architectures as a default. However, translating these abstract principles into concrete, explicit methods remains a significant challenge. This paper addresses this gap by proposing a language-based approach to privacy integration, combining static and runtime techniques. By employing type checking and type inference in an active object language, the framework enables the tracking of authorised data flows and the automatic generation of constraints checked at runtime based on user consent. This ensures that personal data is processed in compliance with GDPR constraints. The key contribution of this work is a type system that gather the compliance checks and the changes to users consent and integrates data privacy compliance verification into system execution. The paper demonstrates the feasibility of this approach through a soundness proof and several examples, illustrating how the proposed language addresses common GDPR requirements, such as user consent, purpose limitation, and data subject rights. This work advances the state of the art in privacy-aware system design by offering a systematic and automated method for integrating GDPR compliance into programming languages. This capability has implications for building trustworthy systems in domains such as healthcare or finance, where data privacy is crucial.
Two-dimensional Sparse Parallelism for Large Scale Deep Learning Recommendation Model Training
Venue: arXiv cs.DC 2025
Year: 2025

The increasing complexity of deep learning recommendation models (DLRM) has led to a growing need for large-scale distributed systems that can efficiently train vast amounts of data. In DLRM, the sparse embedding table is a crucial component for managing sparse categorical features. Typically, these tables in industrial DLRMs contain trillions of parameters, necessitating model parallelism strategies to address memory constraints. However, as training systems expand with massive GPUs, the traditional fully parallelism strategies for embedding table post significant scalability challenges, including imbalance and straggler issues, intensive lookup communication, and heavy embedding activation memory. To overcome these limitations, we propose a novel two-dimensional sparse parallelism approach. Rather than fully sharding tables across all GPUs, our solution introduces data parallelism on top of model parallelism. This enables efficient all-to-all communication and reduces peak memory consumption. Additionally, we have developed the momentum-scaled row-wise AdaGrad algorithm to mitigate performance losses associated with the shift in training paradigms. Our extensive experiments demonstrate that the proposed approach significantly enhances training efficiency while maintaining model performance parity. It achieves nearly linear training speed scaling up to 4K GPUs, setting a new state-of-the-art benchmark for recommendation model training.
CASH: Context-Aware Smart Handover for Reliable UAV Connectivity on Aerial Corridors
Venue: arXiv cs.NI 2025
Year: 2025

Urban Air Mobility (UAM) envisions aerial corridors for Unmanned Aerial Vehicles (UAVs) to reduce ground traffic congestion by supporting 3D mobility, such as air taxis. A key challenge in these high-mobility aerial corridors is ensuring reliable connectivity, where frequent handovers can degrade network performance. To resolve this, we present a Context-Aware Smart Handover (CASH) protocol that uses a forward-looking scoring mechanism based on UAV trajectory to make proactive handover decisions. We evaluate the performance of the proposed CASH against existing handover protocols in a custom-built simulator. Results show that CASH reduces handover frequency by up to 78% while maintaining low outage probability. We then investigate the impact of base station density and safety margin on handover performance, where their optimal setups are empirically obtained to ensure reliable UAM communication.
Deep learning framework for crater detection and identification on the Moon and Mars
Venue: arXiv cs.CV 2025
Year: 2025

Impact craters are among the most prominent geomorphological features on planetary surfaces and are of substantial significance in planetary science research. Their spatial distribution and morphological characteristics provide critical information on planetary surface composition, geological history, and impact processes. In recent years, the rapid advancement of deep learning models has fostered significant interest in automated crater detection. In this paper, we apply advancements in deep learning models for impact crater detection and identification. We use novel models, including Convolutional Neural Networks (CNNs) and variants such as YOLO and ResNet. We present a framework that features a two-stage approach where the first stage features crater identification using simple classic CNN, ResNet-50 and YOLO. In the second stage, our framework employs YOLO-based detection for crater localisation. Therefore, we detect and identify different types of craters and present a summary report with remote sensing data for a selected region. We consider selected regions for craters and identification from Mars and the Moon based on remote sensing data. Our results indicate that YOLO demonstrates the most balanced crater detection performance, while ResNet-50 excels in identifying large craters with high precision.
Point-Based Shape Representation Generation with a Correspondence-Preserving Diffusion Model
Venue: arXiv cs.CV 2025
Year: 2025

We propose a diffusion model designed to generate point-based shape representations with correspondences. Traditional statistical shape models have considered point correspondences extensively, but current deep learning methods do not take them into account, focusing on unordered point clouds instead. Current deep generative models for point clouds do not address generating shapes with point correspondences between generated shapes. This work aims to formulate a diffusion model that is capable of generating realistic point-based shape representations, which preserve point correspondences that are present in the training data. Using shape representation data with correspondences derived from Open Access Series of Imaging Studies 3 (OASIS-3), we demonstrate that our correspondence-preserving model effectively generates point-based hippocampal shape representations that are highly realistic compared to existing methods. We further demonstrate the applications of our generative model by downstream tasks, such as conditional generation of healthy and AD subjects and predicting morphological changes of disease progression by counterfactual generation.