OpenRaiser/Intern-Atlas huggingface dataset:
A Multimodal Conversational Assistant for the Characterization of Agricultural Plots from Geospatial Open Data
Venue: arXiv cs.AI 2025
Year: 2025

The increasing availability of open Earth Observation (EO) and agricultural datasets holds great potential for supporting sustainable land management. However, their high technical entry barrier limits accessibility for non-expert users. This study presents an open-source conversational assistant that integrates multimodal retrieval and large language models (LLMs) to enable natural language interaction with heterogeneous agricultural and geospatial data. The proposed architecture combines orthophotos, Sentinel-2 vegetation indices, and user-provided documents through retrieval-augmented generation (RAG), allowing the system to flexibly determine whether to rely on multimodal evidence, textual knowledge, or both in formulating an answer. To assess response quality, we adopt an LLM-as-a-judge methodology using Qwen3-32B in a zero-shot, unsupervised setting, applying direct scoring in a multi-dimensional quantitative evaluation framework. Preliminary results show that the system is capable of generating clear, relevant, and context-aware responses to agricultural queries, while remaining reproducible and scalable across geographic regions. The primary contributions of this work include an architecture for fusing multimodal EO and textual knowledge sources, a demonstration of lowering the barrier to access specialized agricultural information through natural language interaction, and an open and reproducible design.
Is It Certainly a Deepfake? Reliability Analysis in Detection & Generation Ecosystem
Venue: arXiv cs.AI 2025
Year: 2025

As generative models are advancing in quality and quantity for creating synthetic content, deepfakes begin to cause online mistrust. Deepfake detectors are proposed to counter this effect, however, misuse of detectors claiming fake content as real or vice versa further fuels this misinformation problem. We present the first comprehensive uncertainty analysis of deepfake detectors, systematically investigating how generative artifacts influence prediction confidence. As reflected in detectors' responses, deepfake generators also contribute to this uncertainty as their generative residues vary, so we cross the uncertainty analysis of deepfake detectors and generators. Based on our observations, the uncertainty manifold holds enough consistent information to leverage uncertainty for deepfake source detection. Our approach leverages Bayesian Neural Networks and Monte Carlo dropout to quantify both aleatoric and epistemic uncertainties across diverse detector architectures. We evaluate uncertainty on two datasets with nine generators, with four blind and two biological detectors, compare different uncertainty methods, explore region- and pixel-based uncertainty, and conduct ablation studies. We conduct and analyze binary real/fake, multi-class real/fake, source detection, and leave-one-out experiments between the generator/detector combinations to share their generalization capability, model calibration, uncertainty, and robustness against adversarial attacks. We further introduce uncertainty maps that localize prediction confidence at the pixel level, revealing distinct patterns correlated with generator-specific artifacts. Our analysis provides critical insights for deploying reliable deepfake detection systems and establishes uncertainty quantification as a fundamental requirement for trustworthy synthetic media detection.
A Note on the Theoretical Support to Compute Dimension in Abelian Codes
Venue: arXiv cs.IT 2025
Year: 2025

In this note we give a theoretical support by means of quotient polynomial rings for the computation formulas of the dimension of abelian codes.
AutiHero: Leveraging Generative AI in Social Narratives to Engage Parents in Story-Driven Behavioral Guidance for Autistic Children
Venue: arXiv cs.HC 2025
Year: 2025

Social narratives are known to help autistic children understand and navigate social situations through stories. To ensure effectiveness, however, the materials need to be customized to reflect each child's unique behavioral context, requiring considerable time and effort for parents to practice at home. We present AutiHero, a generative AI-based social narrative system for behavioral guidance, which supports parents to create personalized stories for their autistic children and read them together. AutiHero generates text and visual illustrations that reflect their children's interests, target behaviors, and everyday contexts. In a two-week deployment study with 16 autistic child-parent dyads, parents created 218 stories and read an average of 4.25 stories per day, demonstrating a high level of engagement. AutiHero also provided an effective, low-demanding means to guide children's social behaviors, encouraging positive change. We discuss the implications of generative AI-infused tools to empower parents in guiding their children's behaviors, fostering their social learning.
Towards the State Space Interpretation (SSI): A Formalized Framework for Game Studies and Design
Venue: arXiv cs.HC 2025
Year: 2025

In this paper, we establish structural analogies between core concepts in quantum mechanics and games. By constructing the Quantum Coin Toss on a quantum circuit, we preliminarily investigate the similarity between quantum system behavior and game behavior, thereby formulating the state-operation paradigm. Using this paradigm, we introduce the conceptual prototype of the State Space Interpretation (SSI). Based on mathematical and physical theories, particularly linear algebra, quantum mechanics, and statistical mechanics, we define formal constructs including state space, evolution path, and derived concepts. With the SSI, a game is conceptualized as a state space, while a gameplay process corresponds to an evolution path within this space. We propose that the SSI constitutes a novel interpretation framework for game design and game studies. This framework aims to enhance understanding of games and function as a link between game studies and related fields.
SeqBattNet: A Discrete-State Physics-Informed Neural Network with Aging Adaptation for Battery Modeling
Venue: arXiv cs.LG 2025
Year: 2025

Accurate battery modeling is essential for reliable state estimation in modern applications, such as predicting the remaining discharge time and remaining discharge energy in battery management systems. Existing approaches face several limitations: model-based methods require a large number of parameters; data-driven methods rely heavily on labeled datasets; and current physics-informed neural networks (PINNs) often lack aging adaptation, or still depend on many parameters, or continuously regenerate states. In this work, we propose SeqBattNet, a discrete-state PINN with built-in aging adaptation for battery modeling, to predict terminal voltage during the discharge process. SeqBattNet consists of two components: (i) an encoder, implemented as the proposed HRM-GRU deep learning module, which generates cycle-specific aging adaptation parameters; and (ii) a decoder, based on the equivalent circuit model (ECM) combined with deep learning, which uses these parameters together with the input current to predict voltage. The model requires only three basic battery parameters and, when trained on data from a single cell, still achieves robust performance. Extensive evaluations across three benchmark datasets (TRI, RT-Batt, and NASA) demonstrate that SeqBattNet significantly outperforms classical sequence models and PINN baselines, achieving consistently lower RMSE while maintaining computational efficiency.
The Proof-Theoretic Origin of Double Negation Introduction & Elimination
Venue: arXiv cs.LO 2025
Year: 2025

This paper investigates the proof-theoretic foundations of double negation introduction (DNI) and double negation elimination (DNE) in classical logic. By examining both sequent calculus and natural deduction, it is shown that these rules originate in reductio ad absurdum. The paper demonstrates that both rules possess harmony, ensuring balance between introduction and elimination, and normalisation, which guarantees that derivations reduce to canonical form without detours. These features reveal double negation not as a redundancy, but as a mechanism of proof-theoretic stability, securing the disciplined integration of RAA into classical logic.
Evaluation Codes in Bottleneck Metrics
Venue: arXiv cs.IT 2025
Year: 2025

Analogs of Reed-Solomon codes are introduced within the framework of bottleneck poset metrics. These codes are proven to be maximum distance separable. Furthermore, the results are extended to the setting of Algebraic Geometry codes.
Predicting Depth Maps from Single RGB Images and Addressing Missing Information in Depth Estimation
Venue: arXiv cs.CV 2025
Year: 2025

Depth imaging is a crucial area in Autonomous Driving Systems (ADS), as it plays a key role in detecting and measuring objects in the vehicle's surroundings. However, a significant challenge in this domain arises from missing information in Depth images, where certain points are not measurable due to gaps or inconsistencies in pixel data. Our research addresses two key tasks to overcome this challenge. First, we developed an algorithm using a multi-layered training approach to generate Depth images from a single RGB image. Second, we addressed the issue of missing information in Depth images by applying our algorithm to rectify these gaps, resulting in Depth images with complete and accurate data. We further tested our algorithm on the Cityscapes dataset and successfully resolved the missing information in its Depth images, demonstrating the effectiveness of our approach in real-world urban environments.
Public Key Encryption with Equality Test from Tag-Based Encryption
Venue: arXiv cs.CR 2025
Year: 2025

Public key encryption with equality test (PKEET), proposed by Yang et al. (CT-RSA 2010), is a variant of public key encryption that enables an equality test to determine whether two ciphertexts correspond to the same plaintext. This test applies not only for ciphertexts generated under the same encryption key but also for those generated under different encryption keys. To date, several generic constructions of PKEET have been proposed. However, these generic constructions have the drawback of reliance on the random oracle model or a (hierarchical) identity-based encryption scheme. In this paper, we propose a generic construction of a PKEET scheme based on tag-based encryption without the random oracle model. Tag-based encryption is a weaker primitive than identity-based encryption. Our scheme allows to derive new PKEET schemes without the random oracle model. By instantiating our construction with the pairing-free tag-based encryption scheme by Kiltz (TCC 2006), we obtain a pairing-free PKEET scheme without the random oracle model. Moreover, by instantiating our construction with a tag-based encryption scheme based on the learning parity with noise (LPN) assumption, we obtain a PKEET scheme based on the LPN assumption without the random oracle model.
Empirical AI Ethics: Reconfiguring Ethics towards a Situated, Plural, and Transformative Approach
Venue: arXiv cs.CY 2025
Year: 2025

Mainstream AI ethics, with its reliance on top-down, principle-driven frameworks, fails to account for the situated realities of diverse communities affected by AI (Artificial Intelligence). Critics have argued that AI ethics frequently serves corporate interests through practices of 'ethics washing', operating more as a tool for public relations than as a means of preventing harm or advancing the common good. As a result, growing scepticism among critical scholars has cast the field as complicit in sustaining harmful systems rather than challenging or transforming them. In response, this paper adopts a Science and Technology Studies (STS) perspective to critically interrogate the field of AI ethics. It hence applies the same analytic tools STS has long directed at disciplines such as biology, medicine, and statistics to ethics. This perspective reveals a core tension between vertical (top-down, principle-based) and horizontal (risk-mitigating, implementation-oriented) approaches to ethics. By tracing how these models have shaped the discourse, we show how both fall short in addressing the complexities of AI as a socio-technical assemblage, embedded in practice and entangled with power. To move beyond these limitations, we propose a threefold reorientation of AI ethics. First, we call for a shift in foundations: from top-down abstraction to empirical grounding. Second, we advocate for pluralisation: moving beyond Western-centric frameworks toward a multiplicity of onto-epistemic perspectives. Finally, we outline strategies for reconfiguring AI ethics as a transformative force, moving from narrow paradigms of risk mitigation toward co-creating technologies of hope.
A Generative Framework for Personalized Sticker Retrieval
Venue: arXiv cs.IR 2025
Year: 2025

Formulating information retrieval as a variant of generative modeling, specifically using autoregressive models to generate relevant identifiers for a given query, has recently attracted considerable attention. However, its application to personalized sticker retrieval remains largely unexplored and presents unique challenges: existing relevance-based generative retrieval methods typically lack personalization, leading to a mismatch between diverse user expectations and the retrieved results. To address this gap, we propose PEARL, a novel generative framework for personalized sticker retrieval, and make two key contributions: (i) To encode user-specific sticker preferences, we design a representation learning model to learn discriminative user representations. It is trained on three prediction tasks that leverage personal information and click history; and (ii) To generate stickers aligned with a user's query intent, we propose a novel intent-aware learning objective that prioritizes stickers associated with higher-ranked intents. Empirical results from both offline evaluations and online tests demonstrate that PEARL significantly outperforms state-of-the-art methods.
Qwen3-Omni Technical Report
Venue: arXiv cs.CL 2025
Year: 2025

We present Qwen3-Omni, a single multimodal model that, for the first time, maintains state-of-the-art performance across text, image, audio, and video without any degradation relative to single-modal counterparts. Qwen3-Omni matches the performance of same-sized single-modal models within the Qwen series and excels particularly on audio tasks. Across 36 audio and audio-visual benchmarks, Qwen3-Omni achieves open-source SOTA on 32 benchmarks and overall SOTA on 22, outperforming strong closed-source models such as Gemini-2.5-Pro, Seed-ASR, and GPT-4o-Transcribe. Qwen3-Omni adopts a Thinker-Talker MoE architecture that unifies perception and generation across text, images, audio, and video, yielding fluent text and natural real-time speech. It supports text interaction in 119 languages, speech understanding in 19 languages, and speech generation in 10 languages. To reduce first-packet latency in streaming synthesis, Talker autoregressively predicts discrete speech codecs using a multi-codebook scheme. Leveraging the representational capacity of these codebooks, we replace computationally intensive block-wise diffusion with a lightweight causal ConvNet, enabling streaming from the first codec frame. In cold-start settings, Qwen3-Omni achieves a theoretical end-to-end first-packet latency of 234 ms. To further strengthen multimodal reasoning, we introduce a Thinking model that explicitly reasons over inputs from any modality. Since the research community currently lacks a general-purpose audio captioning model, we fine-tuned Qwen3-Omni-30B-A3B to obtain Qwen3-Omni-30B-A3B-Captioner, which produces detailed, low-hallucination captions for arbitrary audio inputs. Qwen3-Omni-30B-A3B, Qwen3-Omni-30B-A3B-Thinking, and Qwen3-Omni-30B-A3B-Captioner are publicly released under the Apache 2.0 license.
DIVERS-Bench: Evaluating Language Identification Across Domain Shifts and Code-Switching
Venue: arXiv cs.CL 2025
Year: 2025

Language Identification (LID) is a core task in multilingual NLP, yet current systems often overfit to clean, monolingual data. This work introduces DIVERS-BENCH, a comprehensive evaluation of state-of-the-art LID models across diverse domains, including speech transcripts, web text, social media texts, children's stories, and code-switched text. Our findings reveal that while models achieve high accuracy on curated datasets, performance degrades sharply on noisy and informal inputs. We also introduce DIVERS-CS, a diverse code-switching benchmark dataset spanning 10 language pairs, and show that existing models struggle to detect multiple languages within the same sentence. These results highlight the need for more robust and inclusive LID systems in real-world settings.
A Lightweight Approach for State Machine Replication
Venue: arXiv cs.DC 2025
Year: 2025

We present a lightweight solution for state machine replication with commitment certificates. Specifically, we adapt and analyze a median rule for the stabilizing consensus problem [Doerr11] to operate in a client-server setting where arbitrary servers may be blocked adaptively based on past system information. We further extend our protocol by compressing information about committed commands, thus keeping the protocol lightweight, while still enabling clients to easily prove that their commands have indeed been committed on the shared state. Our approach guarantees liveness as long as at most a constant fraction of servers are blocked, ensures safety under any number of blocked servers, and supports fast recovery even after all servers are blocked. In addition to offering near-optimal asymptotic performance in several respects, our method is fully decentralized, unlike other near-optimal solutions that rely on leaders. In particular, our solution is robust against adversaries that target key servers (which captures insider-based denial-of-service attacks), whereas leader-based approaches fail under such a blocking model.
Degradation-Aware All-in-One Image Restoration via Latent Prior Encoding
Venue: arXiv cs.CV 2025
Year: 2025

Real-world images often suffer from spatially diverse degradations such as haze, rain, snow, and low-light, significantly impacting visual quality and downstream vision tasks. Existing all-in-one restoration (AIR) approaches either depend on external text prompts or embed hand-crafted architectural priors (e.g., frequency heuristics); both impose discrete, brittle assumptions that weaken generalization to unseen or mixed degradations. To address this limitation, we propose to reframe AIR as learned latent prior inference, where degradation-aware representations are automatically inferred from the input without explicit task cues. Based on latent priors, we formulate AIR as a structured reasoning paradigm: (1) which features to route (adaptive feature selection), (2) where to restore (spatial localization), and (3) what to restore (degradation semantics). We design a lightweight decoding module that efficiently leverages these latent encoded cues for spatially-adaptive restoration. Extensive experiments across six common degradation tasks, five compound settings, and previously unseen degradations demonstrate that our method outperforms state-of-the-art (SOTA) approaches, achieving an average PSNR improvement of 1.68 dB while being three times more efficient.
Findings of the Fourth Shared Task on Multilingual Coreference Resolution: Can LLMs Dethrone Traditional Approaches?
Venue: arXiv cs.CL 2025
Year: 2025

The paper presents an overview of the fourth edition of the Shared Task on Multilingual Coreference Resolution, organized as part of the CODI-CRAC 2025 workshop. As in the previous editions, participants were challenged to develop systems that identify mentions and cluster them according to identity coreference. A key innovation of this year's task was the introduction of a dedicated Large Language Model (LLM) track, featuring a simplified plaintext format designed to be more suitable for LLMs than the original CoNLL-U representation. The task also expanded its coverage with three new datasets in two additional languages, using version 1.3 of CorefUD - a harmonized multilingual collection of 22 datasets in 17 languages. In total, nine systems participated, including four LLM-based approaches (two fine-tuned and two using few-shot adaptation). While traditional systems still kept the lead, LLMs showed clear potential, suggesting they may soon challenge established approaches in future editions.
TS-P$^2$CL: Plug-and-Play Dual Contrastive Learning for Vision-Guided Medical Time Series Classification
Venue: arXiv cs.CV 2025
Year: 2025

Medical time series (MedTS) classification is pivotal for intelligent healthcare, yet its efficacy is severely limited by poor cross-subject generation due to the profound cross-individual heterogeneity. Despite advances in architectural innovations and transfer learning techniques, current methods remain constrained by modality-specific inductive biases that limit their ability to learn universally invariant representations. To overcome this, we propose TS-P$^2$CL, a novel plug-and-play framework that leverages the universal pattern recognition capabilities of pre-trained vision models. We introduce a vision-guided paradigm that transforms 1D physiological signals into 2D pseudo-images, establishing a bridge to the visual domain. This transformation enables implicit access to rich semantic priors learned from natural images. Within this unified space, we employ a dual-contrastive learning strategy: intra-modal consistency enforces temporal coherence, while cross-modal alignment aligns time-series dynamics with visual semantics, thereby mitigating individual-specific biases and learning robust, domain-invariant features. Extensive experiments on six MedTS datasets demonstrate that TS-P$^2$CL consistently outperforms fourteen methods in both subject-dependent and subject-independent settings.
ContextFlow: Training-Free Video Object Editing via Adaptive Context Enrichment
Venue: arXiv cs.CV 2025
Year: 2025

Training-free video object editing aims to achieve precise object-level manipulation, including object insertion, swapping, and deletion. However, it faces significant challenges in maintaining fidelity and temporal consistency. Existing methods, often designed for U-Net architectures, suffer from two primary limitations: inaccurate inversion due to first-order solvers, and contextual conflicts caused by crude "hard" feature replacement. These issues are more challenging in Diffusion Transformers (DiTs), where the unsuitability of prior layer-selection heuristics makes effective guidance challenging. To address these limitations, we introduce ContextFlow, a novel training-free framework for DiT-based video object editing. In detail, we first employ a high-order Rectified Flow solver to establish a robust editing foundation. The core of our framework is Adaptive Context Enrichment (for specifying what to edit), a mechanism that addresses contextual conflicts. Instead of replacing features, it enriches the self-attention context by concatenating Key-Value pairs from parallel reconstruction and editing paths, empowering the model to dynamically fuse information. Additionally, to determine where to apply this enrichment (for specifying where to edit), we propose a systematic, data-driven analysis to identify task-specific vital layers. Based on a novel Guidance Responsiveness Metric, our method pinpoints the most influential DiT blocks for different tasks (e.g., insertion, swapping), enabling targeted and highly effective guidance. Extensive experiments show that ContextFlow significantly outperforms existing training-free methods and even surpasses several state-of-the-art training-based approaches, delivering temporally coherent, high-fidelity results.