OpenRaiser/Intern-Atlas huggingface dataset:
FORMSpoT: A Decade of Tree-Level, Country-Scale Forest Monitoring
Venue: arXiv cs.CV 2025
Year: 2025

The recent decline of the European forest carbon sink highlights the need for spatially explicit and frequently updated forest monitoring tools. Yet, existing satellite-based disturbance products remain too coarse to detect changes at the scale of individual trees, typically below 100 m$^{2}$. Here, we introduce FORMSpoT (Forest Mapping with SPOT Time series), a decade-long (2014-2024) nationwide mapping of forest canopy height at 1.5 m resolution, together with annual disturbance polygons (FORMSpoT-$Δ$) covering mainland France. Canopy heights were derived from annual SPOT-6/7 composites using a hierarchical transformer model (PVTv2) trained on high-resolution airborne laser scanning (ALS) data. To enable robust change detection across heterogeneous acquisitions, we developed a dedicated post-processing pipeline combining co-registration and spatio-temporal total variation denoising. Validation against ALS revisits across 19 sites and 5,087 National Forest Inventory plots shows that FORMSpoT-$Δ$ substantially outperforms existing disturbance products. In mountainous forests, where disturbances are small and spatially fragmented, FORMSpoT-$Δ$ achieves an F1-score of 0.44, representing an order of magnitude higher than existing benchmarks. By enabling tree-level monitoring of forest dynamics at national scale, FORMSpoT-$Δ$ provides a unique tool to analyze management practices, detect early signals of forest decline, and better quantify carbon losses from subtle disturbances such as thinning or selective logging. These results underscore the critical importance of sustaining very high-resolution satellite missions like SPOT and open-data initiatives such as DINAMIS for monitoring forests under climate change.
Unexpected Knowledge: Auditing Wikipedia and Grokipedia Search Recommendations
Venue: arXiv cs.IR 2025
Year: 2025

Encyclopedic knowledge platforms are key gateways through which users explore information online. The recent release of Grokipedia, a fully AI-generated encyclopedia, introduces a new alternative to traditional, well-established platforms like Wikipedia. In this context, search engine mechanisms play an important role in guiding users exploratory paths, yet their behavior across different encyclopedic systems remains underexplored. In this work, we address this gap by providing the first comparative analysis of search engine in Wikipedia and Grokipedia. Using nearly 10,000 neutral English words and their substrings as queries, we collect over 70,000 search engine results and examine their semantic alignment, overlap, and topical structure. We find that both platforms frequently generate results that are weakly related to the original query and, in many cases, surface unexpected content starting from innocuous queries. Despite these shared properties, the two systems often produce substantially different recommendation sets for the same query. Through topical annotation and trajectory analysis, we further identify systematic differences in how content categories are surfaced and how search engine results evolve over multiple stages of exploration. Overall, our findings show that unexpected search engine outcomes are a common feature of both the platforms, even though they exhibit discrepancies in terms of topical distribution and query suggestions.
Flux-Preserving Adaptive Finite State Projection for Multiscale Stochastic Reaction Networks
Venue: arXiv cs.CE 2025
Year: 2025

The Finite State Projection (FSP) method approximates the Chemical Master Equation (CME) by restricting the dynamics to a finite subset of the (typically infinite) state space, enabling direct numerical solution with computable error bounds. Adaptive variants update this subset in time, but multiscale systems with widely separated reaction rates remain challenging, as low-probability bottleneck states can carry essential probability flux and the dynamics alternate between fast transients and slowly evolving stiff regimes. We propose a flux-based adaptive FSP method that uses probability flux to drive both state-space pruning and time-step selection. The pruning rule protects low-probability states with large outgoing flux, preserving connectivity in bottleneck systems, while the time-step rule adapts to the instantaneous total flux to handle rate constants spanning several orders of magnitude. Numerical experiments on stiff, oscillatory, and bottleneck reaction networks show that the method maintains accuracy while using substantially smaller state spaces.
Perturb Your Data: Paraphrase-Guided Training Data Watermarking
Venue: arXiv cs.CL 2025
Year: 2025

Training data detection is critical for enforcing copyright and data licensing, as Large Language Models (LLM) are trained on massive text corpora scraped from the internet. We present SPECTRA, a watermarking approach that makes training data reliably detectable even when it comprises less than 0.001% of the training corpus. SPECTRA works by paraphrasing text using an LLM and assigning a score based on how likely each paraphrase is, according to a separate scoring model. A paraphrase is chosen so that its score closely matches that of the original text, to avoid introducing any distribution shifts. To test whether a suspect model has been trained on the watermarked data, we compare its token probabilities against those of the scoring model. We demonstrate that SPECTRA achieves a consistent p-value gap of over nine orders of magnitude when detecting data used for training versus data not used for training, which is greater than all baselines tested. SPECTRA equips data owners with a scalable, deploy-before-release watermark that survives even large-scale LLM training.
Virtual Reality in Service Design for Plastics Recycling: Two Application Cases
Venue: arXiv cs.HC 2025
Year: 2025

Plastics recycling depends on everyday sorting practices and on how recycling services are communicated and experienced. Virtual reality (VR) can present these practices and services in situated, interactive form, yet its role in service design for plastics recycling is still emerging. This paper examines how VR tools can contribute to designing plastics recycling services through two application cases that address different stages of the recycling journey. The first case, Clean Cabin Escape, is a household scale VR escape room where players collect and sort waste items into locally relevant categories, with immediate feedback that supports practice with plastics recycling decisions. The second case is a VR simulation of a plastics recycling center that represents a real planned site and is used in service design workshops where stakeholders explore layout, signage and customer paths for plastics fractions. Across the cases, we analyse how VR supported learning, engagement and shared sensemaking, and how it interacted with other service design methods such as workshops, customer path mapping and physical artefacts. The findings show that VR can make domestic sorting tasks and complex recycling centers more concrete for both citizens and professionals, but also highlight trade offs related to hardware access, onboarding effort, visual fidelity and localisation of recycling rules. The paper concludes by outlining opportunities for integrating VR into broader service design toolsets for plastics recycling and circular economy services, and by pointing to directions for future research on long term impact and inclusive design.
Data Augmentation Supporting a Conversational Agent Designed for Smoking Cessation Support Groups
Venue: arXiv cs.CL 2025
Year: 2025

Online support groups for smoking cessation are economical and accessible, yet they often face challenges with low user engagement and stigma. The use of an automatic conversational agent would improve engagement by ensuring that all user comments receive a timely response.). We address the challenge of insufficient high-quality data by employing a two-level data augmentation strategy: synthetic data augmentation and real data augmentation. First, we fine-tuned an open source LLM to classify posts from our existing smoking cessation support groups and identify intents with low F1 (precision+recall) scores. Then, for these intents, we generate additional synthetic data using prompt engineering with the GPT model, with an average of 87\% of the generated synthetic posts deemed high quality by human annotators. Overall, the synthetic augmentation process resulted in 43\% of the original posts being selected for augmentation, followed by 140\% synthetic expansion of these posts. Additionally, we scraped more than 10,000 real posts from a related online support context, of which 73\% were validated as good quality by human annotators. Each synthetic or scraped post underwent rigorous validation involving human reviewers to ensure quality and relevance. The validated new data, combined with the original support group posts, formed an augmented dataset used to retrain the intent classifier. Performance evaluation of the retrained model demonstrated a 32\% improvement in F1, confirming the effectiveness of our data augmentation approach. Synthetic and real post augmentation led to similar performance improvements. This study provides a replicable framework for enhancing conversational agent performance in domains where data scarcity is a critical issue.
MIRGE: An Array-Based Computational Framework for Scientific Computing
Venue: arXiv cs.MS 2025
Year: 2025

MIRGE is a computational approach for scientific computing based on NumPy-like array computation, but using lazy evaluation to recast computation as data-flow graphs, where nodes represent immutable, multi-dimensional arrays. Evaluation of an array expression is deferred until its value is needed, at which point a pipeline is invoked that transforms high-level array expressions into lower-level intermediate representations (IR) and finally into executable code, through a multi-stage process. Domain-specific transformations, such as metadata-driven optimizations, GPU-parallelization strategies, and loop fusion techniques, improve performance and memory efficiency. MIRGE employs "array contexts" to abstract the interface between array expressions and heterogeneous execution environments (for example, lazy evaluation via OpenCL, or eager evaluation via NumPy or CuPy). The framework thus enables performance portability as well as separation of concerns between application logic, low-level implementation, and optimizations. By enabling scientific expressivity while facilitating performance tuning, MIRGE offers a robust, extensible platform for both computational research and scientific application development. This paper provides an overview of MIRGE. We further describe an application of MIRGE called MIRGE-Com, for supersonic combusting flows in a discontinuous Galerkin finite-element setting. We demonstrate its capabilities as a solver and highlight its performance characteristics on large-scale GPU hardware.
Biosecurity-Aware AI: Agentic Risk Auditing of Soft Prompt Attacks on ESM-Based Variant Predictors
Venue: arXiv cs.CR 2025
Year: 2025

Genomic Foundation Models (GFMs), such as Evolutionary Scale Modeling (ESM), have demonstrated remarkable success in variant effect prediction. However, their security and robustness under adversarial manipulation remain largely unexplored. To address this gap, we introduce the Secure Agentic Genomic Evaluator (SAGE), an agentic framework for auditing the adversarial vulnerabilities of GFMs. SAGE functions through an interpretable and automated risk auditing loop. It injects soft prompt perturbations, monitors model behavior across training checkpoints, computes risk metrics such as AUROC and AUPR, and generates structured reports with large language model-based narrative explanations. This agentic process enables continuous evaluation of embedding-space robustness without modifying the underlying model. Using SAGE, we find that even state-of-the-art GFMs like ESM2 are sensitive to targeted soft prompt attacks, resulting in measurable performance degradation. These findings reveal critical and previously hidden vulnerabilities in genomic foundation models, showing the importance of agentic risk auditing in securing biomedical applications such as clinical variant interpretation.
TCDE: Topic-Centric Dual Expansion of Queries and Documents with Large Language Models for Information Retrieval
Venue: arXiv cs.IR 2025
Year: 2025

Query Expansion (QE) enriches queries and Document Expansion (DE) enriches documents, and these two techniques are often applied separately. However, such separate application may lead to semantic misalignment between the expanded queries (or documents) and their relevant documents (or queries). To address this serious issue, we propose TCDE, a dual expansion strategy that leverages large language models (LLMs) for topic-centric enrichment on both queries and documents. In TCDE, we design two distinct prompt templates for processing each query and document. On the query side, an LLM is guided to identify distinct sub-topics within each query and generate a focused pseudo-document for each sub-topic. On the document side, an LLM is guided to distill each document into a set of core topic sentences. The resulting outputs are used to expand the original query and document. This topic-centric dual expansion process establishes semantic bridges between queries and their relevant documents, enabling better alignment for downstream retrieval models. Experiments on two challenging benchmarks, TREC Deep Learning and BEIR, demonstrate that TCDE achieves substantial improvements over strong state-of-the-art expansion baselines. In particular, on dense retrieval tasks, it outperforms several state-of-the-art methods, with a relative improvement of 2.8\% in NDCG@10 on the SciFact dataset. Experimental results validate the effectiveness of our topic-centric and dual expansion strategy.
ABE-CLIP: Training-Free Attribute Binding Enhancement for Compositional Image-Text Matching
Venue: arXiv cs.CV 2025
Year: 2025

Contrastive Language-Image Pretraining (CLIP) has achieved remarkable performance in various multimodal tasks. However, it still struggles with compositional image-text matching, particularly in accurately associating objects with their corresponding attributes, because its inherent global representation often overlooks fine-grained semantics for attribute binding. Existing methods often require additional training or extensive hard negative sampling, yet they frequently show limited generalization to novel compositional concepts and fail to fundamentally address the drawbacks of global representations. In this paper, we propose ABE-CLIP, a novel training-free Attribute Binding Enhancement method designed to strengthen attribute-object binding in CLIP-like models. Specifically, we employ a Semantic Refinement Mechanism to refine token embeddings for both object and attribute phrases in the text, thereby mitigating attribute confusion and improving semantic precision. We further introduce a Local Token-Patch Alignment strategy that computes similarity scores between refined textual tokens and their most relevant image patches. By aggregating localized similarity scores, ABE-CLIP computes the final image-text similarity. Experiments on multiple datasets demonstrate that ABE-CLIP significantly improves attribute-object binding performance, even surpassing methods that require extensive training.
CheXPO-v2: Preference Optimization for Chest X-ray VLMs with Knowledge Graph Consistency
Venue: arXiv cs.CV 2025
Year: 2025

Medical Vision-Language Models (VLMs) are prone to hallucinations, compromising clinical reliability. While reinforcement learning methods like Group Relative Policy Optimization (GRPO) offer a low-cost alignment solution, their reliance on sparse, outcome-based rewards inadvertently encourages models to "overthink" -- generating verbose, convoluted, and unverifiable Chain-of-Thought reasoning to justify answers. This focus on outcomes obscures factual errors and poses significant safety risks. To address this, we propose CheXPO-v2, a novel alignment framework that shifts from outcome to process supervision. Our core innovation is a Knowledge Graph Consistency Reward mechanism driven by Entity-Relation Matching. By explicitly parsing reasoning steps into structured "Disease, Relation, Anatomy" triplets, we provide fine-grained supervision that penalizes incoherent logic and hallucinations at the atomic level. Integrating this with a hard-example mining strategy, our approach significantly outperforms GRPO and state-of-the-art models on benchmarks like MIMIC-CXR-VQA. Crucially, CheXPO-v2 achieves new state-of-the-art accuracy using only 5k samples, demonstrating exceptional data efficiency while producing clinically sound and verifiable reasoning. The project source code is publicly available at: https://github.com/ecoxial2007/CheX-Phi4MM.
Fairly Dividing Non-identical Random Items: Just Sample or Match
Venue: arXiv cs.GT 2025
Year: 2025

We study the question of existence and fast computation of fair and efficient allocations of indivisible resources among agents with additive valuations. As such allocations may not exist for arbitrary instances, we ask if they exist for \textit{typical} or \textit{random} instances, meaning when the utility values of agents for the resources are drawn from certain distributions. If such allocations exist with high probability for typical instances, and furthermore if they can be computed efficiently, this would imply that we could quickly resolve a real world resource allocation scenario in a fair and efficient manner with high probability. This implication has made this setting popular and well studied in fair resource allocation. In this paper, we extend the previously studied formal models of this problem to non-identical items. We assume that every item is associated with a distribution $\mathcal{U}_j$, and every agent's utility value for the item is drawn independently from $\mathcal{U}_j$. We show that envy-free fair and maximum social welfare efficient allocations exist with high probability in the asymptotic setting, meaning when the number of agents $n$ and items $m$ are large. Further we show that when $m=O(n\log n),$ then by only sampling $O(\log m)$ or $O((\log m)^2)$ utility values per item instead of all the $n,$ we can compute these allocations in $\tilde{O}(m)$ time. Finally, we simulate our algorithms on randomly generated instances and show that even for small instances, we suffer small multiplicative losses in the fairness and efficiency guarantees even for small sized instances, and converge to fully optimal guarantees quickly.
Practical Framework for Privacy-Preserving and Byzantine-robust Federated Learning
Venue: arXiv cs.CR 2025
Year: 2025

Federated Learning (FL) allows multiple clients to collaboratively train a model without sharing their private data. However, FL is vulnerable to Byzantine attacks, where adversaries manipulate client models to compromise the federated model, and privacy inference attacks, where adversaries exploit client models to infer private data. Existing defenses against both backdoor and privacy inference attacks introduce significant computational and communication overhead, creating a gap between theory and practice. To address this, we propose ABBR, a practical framework for Byzantine-robust and privacy-preserving FL. We are the first to utilize dimensionality reduction to speed up the private computation of complex filtering rules in privacy-preserving FL. Additionally, we analyze the accuracy loss of vector-wise filtering in low-dimensional space and introduce an adaptive tuning strategy to minimize the impact of malicious models that bypass filtering on the global model. We implement ABBR with state-of-the-art Byzantine-robust aggregation rules and evaluate it on public datasets, showing that it runs significantly faster, has minimal communication overhead, and maintains nearly the same Byzantine-resilience as the baselines.
Seed-Prover 1.5: Mastering Undergraduate-Level Theorem Proving via Learning from Experience
Venue: arXiv cs.CL 2025
Year: 2025

Large language models have recently made significant progress to generate rigorous mathematical proofs. In contrast, utilizing LLMs for theorem proving in formal languages (such as Lean) remains challenging and computationally expensive, particularly when addressing problems at the undergraduate level and beyond. In this work, we present \textbf{Seed-Prover 1.5}, a formal theorem-proving model trained via large-scale agentic reinforcement learning, alongside an efficient test-time scaling (TTS) workflow. Through extensive interactions with Lean and other tools, the model continuously accumulates experience during the RL process, substantially enhancing the capability and efficiency of formal theorem proving. Furthermore, leveraging recent advancements in natural language proving, our TTS workflow efficiently bridges the gap between natural and formal languages. Compared to state-of-the-art methods, Seed-Prover 1.5 achieves superior performance with a smaller compute budget. It solves \textbf{88\% of PutnamBench} (undergraduate-level), \textbf{80\% of Fate-H} (graduate-level), and \textbf{33\% of Fate-X} (PhD-level) problems. Notably, using our system, we solved \textbf{11 out of 12 problems} from Putnam 2025 within 9 hours. Our findings suggest that scaling learning from experience, driven by high-quality formal feedback, holds immense potential for the future of formal mathematical reasoning.
Sensor Management System (SMS): Open-source software for FAIR sensor metadata management in Earth system sciences
Venue: arXiv cs.SE 2025
Year: 2025

Deriving reliable conclusions and insights from environmental observational data urgently requires the enrichment with consistent and comprehensive metadata, including time-resolved context such as changing deployments, configurations, and maintenance actions. We have therefore developed the Sensor Management System (SMS), which provides a user-friendly and feature-rich platform for modeling even the most complex sensor systems and managing all sensor-related information across their life cycle. Each entity is described via well-defined terms like Devices, Platforms and Configurations, as well as Sites that are further enhanced with attributes for, e.g., instrument manufacturers, contact information or measured quantities and complemented by a continuous history of system-related actions. By further linking the SMS to sub-sequent systems and services like PID-registration or controlled vocabularies and establishing a community of end-users, the SMS provides the central element of a digital ecosystem, that fosters a more consistent, sustainable and FAIR provision of sensor-related metadata.
Analytical Stackelberg Resource Allocation in Sequential Attacker--Defender Games
Venue: arXiv cs.GT 2025
Year: 2025

We develop an analytical Stackelberg game framework for optimal resource allocation in a sequential attacker--defender setting with a finite set of assets and probabilistic attacks. The defender commits to a mixed protection strategy, after which the attacker best-responds via backward induction. Closed-form expressions for equilibrium protection and attack strategies are derived for general numbers of assets and defensive resources. Necessary constraints on rewards and costs are established to ensure feasibility of the probability distributions. Three distinct payoff regimes for the defender are identified and analysed. An eight-asset numerical example illustrates the equilibrium structure and reveals a unique Pareto-dominant attack configuration.