OpenRaiser/Intern-Atlas huggingface dataset:
EasyVitessce: auto-magically adding interactivity to Scverse single-cell and spatial biology plots
Venue: arXiv cs.HC 2025
Year: 2025

EasyVitessce is a Python package that turns existing static Scanpy and SpatialData plots into interactive visualizations by virtue of adding a single line of Python code. The package uses Vitessce internally to render interactive plots, and abstracts away technical details involved with configuration of Vitessce. The resulting interactive plots can be viewed in computational notebook environments or their configurations can be exported for usage in other contexts such as web applications, enhancing the utility of popular Scverse Python plotting APIs. EasyVitessce is released under the MIT License and available on the Python Package Index (PyPI). The source code is publicly available on GitHub.
CBDiff:Conditional Bernoulli Diffusion Models for Image Forgery Localization
Venue: arXiv cs.CV 2025
Year: 2025

Image Forgery Localization (IFL) is a crucial task in image forensics, aimed at accurately identifying manipulated or tampered regions within an image at the pixel level. Existing methods typically generate a single deterministic localization map, which often lacks the precision and reliability required for high-stakes applications such as forensic analysis and security surveillance. To enhance the credibility of predictions and mitigate the risk of errors, we introduce an advanced Conditional Bernoulli Diffusion Model (CBDiff). Given a forged image, CBDiff generates multiple diverse and plausible localization maps, thereby offering a richer and more comprehensive representation of the forgery distribution. This approach addresses the uncertainty and variability inherent in tampered regions. Furthermore, CBDiff innovatively incorporates Bernoulli noise into the diffusion process to more faithfully reflect the inherent binary and sparse properties of forgery masks. Additionally, CBDiff introduces a Time-Step Cross-Attention (TSCAttention), which is specifically designed to leverage semantic feature guidance with temporal steps to improve manipulation detection. Extensive experiments on eight publicly benchmark datasets demonstrate that CBDiff significantly outperforms existing state-of-the-art methods, highlighting its strong potential for real-world deployment.
Unmanned Aerial Vehicles Control in a Digital Twin: Exploring the Effect of Different Points of View on User Experience in Virtual Reality
Venue: arXiv cs.HC 2025
Year: 2025

Controlling Unmanned Aerial Vehicles (UAVs) is a cognitively demanding task, with accidents often arising from insufficient situational awareness, inadequate training, and poor user experiences. Providing more intuitive and immersive visual feedback, particularly through Digital Twin technologies, offers new opportunities to enhance pilot awareness and overall experience quality. In this study, we investigate how different virtual points of view (POVs) influence user experience and performance during UAV piloting in Virtual Reality (VR), utilizing a digital twin that faithfully replicates the real-world flight environment. We developed a VR application that enables participants to control a physical DJI Mini 4 Pro drone while immersed in a digital twin with four distinct camera perspectives: Baseline View (static external), First-Person View, Chase View, and Third-Person View. Nineteen participants completed a series of ring-based obstacle courses from each perspective. In addition to objective flight data, we collected standardized subjective assessments of user experience, presence, workload, cybersickness, and situational awareness. Quantitative analyses revealed that the First-Person View was associated with significantly higher mental demand and effort, greater trajectory deviation, but smoother control inputs compared to the Third-Person and Chase perspectives. Complementing these findings, preference data indicated that the Third-Person View was most consistently favored, whereas the First-Person View elicited polarized reactions.
Beyond sparse denoising in frames: minimax estimation with a scattering transform
Venue: arXiv cs.CV 2025
Year: 2025

A considerable amount of research in harmonic analysis has been devoted to non-linear estimators of signals contaminated by additive Gaussian noise. They are implemented by thresholding coefficients in a frame, which provide a sparse signal representation, or by minimising their $\ell^1$ norm. However, sparse estimators in frames are not sufficiently rich to adapt to complex signal regularities. For cartoon images whose edges are piecewise $\bf C^α$ curves, wavelet, curvelet and Xlet frames are suboptimal if the Lipschitz exponent $α\leq 2$ is an unknown parameter. Deep convolutional neural networks have recently obtained much better numerical results, which reach the minimax asymptotic bounds for all $α$. Wavelet scattering coefficients have been introduced as simplified convolutional neural network models. They are computed by transforming the modulus of wavelet coefficients with a second wavelet transform. We introduce a denoising estimator by jointly minimising and maximising the $\ell^1$ norms of different subsets of scattering coefficients. We prove that these $\ell^1$ norms capture different types of geometric image regularity. Numerical experiments show that this denoising estimator reaches the minimax asymptotic bound for cartoon images for all Lipschitz exponents $α\leq 2$. We state this numerical result as a mathematical conjecture. It provides a different harmonic analysis approach to suppress noise from signals, and to specify the geometric regularity of functions. It also opens a mathematical bridge between harmonic analysis and denoising estimators with deep convolutional network.
FidelityGPT: Correcting Decompilation Distortions with Retrieval Augmented Generation
Venue: arXiv cs.SE 2025
Year: 2025

Decompilation converts machine code into human-readable form, enabling analysis and debugging without source code. However, fidelity issues often degrade the readability and semantic accuracy of decompiled output. Existing methods, such as variable renaming or structural simplification, provide partial improvements but lack robust detection and correction, particularly for complex closed-source binaries. We present FidelityGPT, a framework that enhances decompiled code accuracy and readability by systematically detecting and correcting semantic distortions. FidelityGPT introduces distortion-aware prompt templates tailored to closed-source settings and integrates Retrieval-Augmented Generation (RAG) with a dynamic semantic intensity algorithm to locate distorted lines and retrieve semantically similar code from a database. A variable dependency algorithm further mitigates long-context limitations by analyzing redundant variables and integrating their dependencies into the prompt context. Evaluated on 620 function pairs from a binary similarity benchmark, FidelityGPT achieved an average detection accuracy of 89% and a precision of 83%. Compared to the state-of-the-art DeGPT (Fix Rate 83%, Corrected Fix Rate 37%), FidelityGPT attained 94% FR and 64% CFR, demonstrating significant gains in accuracy and readability. These results highlight its potential to advance LLM-based decompilation and reverse engineering.
CrossNews-UA: A Cross-lingual News Semantic Similarity Benchmark for Ukrainian, Polish, Russian, and English
Venue: arXiv cs.CL 2025
Year: 2025

In the era of social networks and rapid misinformation spread, news analysis remains a critical task. Detecting fake news across multiple languages, particularly beyond English, poses significant challenges. Cross-lingual news comparison offers a promising approach to verify information by leveraging external sources in different languages (Chen and Shu, 2024). However, existing datasets for cross-lingual news analysis (Chen et al., 2022a) were manually curated by journalists and experts, limiting their scalability and adaptability to new languages. In this work, we address this gap by introducing a scalable, explainable crowdsourcing pipeline for cross-lingual news similarity assessment. Using this pipeline, we collected a novel dataset CrossNews-UA of news pairs in Ukrainian as a central language with linguistically and contextually relevant languages-Polish, Russian, and English. Each news pair is annotated for semantic similarity with detailed justifications based on the 4W criteria (Who, What, Where, When). We further tested a range of models, from traditional bag-of-words, Transformer-based architectures to large language models (LLMs). Our results highlight the challenges in multilingual news analysis and offer insights into models performance.
HSCodeComp: A Realistic and Expert-level Benchmark for Deep Search Agents in Hierarchical Rule Application
Venue: arXiv cs.AI 2025
Year: 2025

Effective deep search agents must not only access open-domain and domain-specific knowledge but also apply complex rules-such as legal clauses, medical manuals and tariff rules. These rules often feature vague boundaries and implicit logic relationships, making precise application challenging for agents. However, this critical capability is largely overlooked by current agent benchmarks. To fill this gap, we introduce HSCodeComp, the first realistic, expert-level e-commerce benchmark designed to evaluate deep search agents in hierarchical rule application. In this task, the deep reasoning process of agents is guided by these rules to predict 10-digit Harmonized System Code (HSCode) of products with noisy but realistic descriptions. These codes, established by the World Customs Organization, are vital for global supply chain efficiency. Built from real-world data collected from large-scale e-commerce platforms, our proposed HSCodeComp comprises 632 product entries spanning diverse product categories, with these HSCodes annotated by several human experts. Extensive experimental results on several state-of-the-art LLMs, open-source, and closed-source agents reveal a huge performance gap: best agent achieves only 46.8% 10-digit accuracy, far below human experts at 95.0%. Besides, detailed analysis demonstrates the challenges of hierarchical rule application, and test-time scaling fails to improve performance further.
LLavaCode: Compressed Code Representations for Retrieval-Augmented Code Generation
Venue: arXiv cs.CL 2025
Year: 2025

Retrieval-augmented generation has emerged as one of the most effective approaches for code completion, particularly when context from a surrounding repository is essential. However, incorporating context significantly extends sequence length, leading to slower inference - a critical limitation for interactive settings such as IDEs. In this work, we introduce LlavaCode, a framework that compresses code into compact, semantically rich representations interpretable by code LLM, enhancing generation quality while reducing the retrieved context to only a few compressed single-token vectors. Using a small projector module we can significantly increase the EM and ES metrics of coding model with negligible latency increase. Our experiments demonstrate that compressed context enables 20-38% reduction in Time-to-First-Token (TTFT) on line completion tasks compared to full-RAG pipelines.
Parameter Estimation in River Transport Models With Immobile Phase Exchange Using Dimensional Analysis and Reduced-Order Models
Venue: arXiv cs.CE 2025
Year: 2025

We propose a framework for parameter estimation in river transport models using breakthrough curve data, which we refer to as Dimensionless Synthetic Transport Estimation (DSTE). We utilize this framework to parameterize the one-dimensional advection-dispersion equation model, incorporating immobile phase exchange through a memory function. We solve the governing equation analytically in the Laplace domain and numerically invert it to generate synthetic breakthrough curves for different memory functions and boundary conditions. A dimensionless formulation enables decoupling the estimation of advection velocity from other parameters, significantly reducing the number of required forward solutions. To improve computational efficiency, we apply a Karhunen-Loeve (KL) expansion to transform the synthetic dataset into a reduced-order space. Given a measured breakthrough curve, we estimate the advection velocity by minimizing the distance from the measurement to the synthetic data in KL space, and infer the remaining dimensionless parameters by Projected Barycentric Interpolation (PBI). We benchmark our method against several alternatives, including Laplace domain fitting, moment matching, global random optimization, and variations of the DSTE framework using nearest-neighbor interpolation and neural network-based estimation. Applied to 295 breakthrough curves from 54 tracer tests in 25 rivers, DSTE delivers accurate parameter estimates. The resulting labeled dataset allows researchers to link transport parameters with hydraulic conditions, site characteristics, and measured concentrations. The synthetic dataset can be leveraged for the analysis of new breakthrough curves, eliminating the need for additional forward simulations.
Directive, Metacognitive or a Blend of Both? A Comparison of AI-Generated Feedback Types on Student Engagement, Confidence, and Outcomes
Venue: arXiv cs.HC 2025
Year: 2025

Feedback is one of the most powerful influences on student learning, with extensive research examining how best to implement it in educational settings. Increasingly, feedback is being generated by artificial intelligence (AI), offering scalable and adaptive responses. Two widely studied approaches are directive feedback, which gives explicit explanations and reduces cognitive load to speed up learning, and metacognitive feedback which prompts learners to reflect, track their progress, and develop self-regulated learning (SRL) skills. While both approaches have clear theoretical advantages, their comparative effects on engagement, confidence, and quality of work remain underexplored. This study presents a semester-long randomised controlled trial with 329 students in an introductory design and programming course using an adaptive educational platform. Participants were assigned to receive directive, metacognitive, or hybrid AI-generated feedback that blended elements of both directive and metacognitive feedback. Results showed that revision behaviour differed across feedback conditions, with Hybrid prompting the most revisions compared to Directive and Metacognitive. Confidence ratings were uniformly high, and resource quality outcomes were comparable across conditions. These findings highlight the promise of AI in delivering feedback that balances clarity with reflection. Hybrid approaches, in particular, show potential to combine actionable guidance for immediate improvement with opportunities for self-reflection and metacognitive growth.
Problems from Optimization and Computational Algebra Equivalent to Hilbert's Nullstellensatz
Venue: arXiv cs.CC 2025
Year: 2025

Efficient algorithms for many problems in optimization and computational algebra often arise from casting them as systems of polynomial equations. Blum, Shub, and Smale formalized this as Hilbert's Nullstellensatz Problem $HN_R$: given multivariate polynomials over a ring $R$, decide whether they have a common solution in $R$. We can also view $HN_R$ as a complexity class by taking the downward closure of the problem $HN_R$ under polynomial-time many-one reductions. In this work, we show that many important problems from optimization and algebra are complete or hard for this class. We first consider the Affine Polynomial Projection Problem: given polynomials $f,g$, does an affine projection of the variables transform $f$ into $g$? We show that this problem is at least as hard as $HN_F$ for any field $F$. Then we consider the Sparse Shift Problem: given a polynomial, can its number of monomials be reduced by an affine shift of the variables? Prior $HN_R$-hardness for this problem was known for non-field integral domains $R$, which we extend to fields. For the special case of the real field, HN captures the existential theory of the reals and its complement captures the universal theory of the reals. We prove that the problems of deciding real stability, convexity, and hyperbolicity of a given polynomial are all complete for the universal theory of the reals, thereby pinning down their exact complexity.
Fast Inference via Hierarchical Speculative Decoding
Venue: arXiv cs.LG 2025
Year: 2025

Transformer language models generate text autoregressively, making inference latency proportional to the number of tokens generated. Speculative decoding reduces this latency without sacrificing output quality, by leveraging a small draft model to propose tokens that the larger target model verifies in parallel. In practice, however, there may exist a set of potential draft models- ranging from faster but less inaccurate, to slower yet more reliable. We introduce Hierarchical Speculative Decoding (HSD), an algorithm that stacks these draft models into a hierarchy, where each model proposes tokens, and the next larger model verifies them in a single forward pass, until finally the target model verifies tokens. We derive an expression for the expected latency of any such hierarchy and show that selecting the latency-optimal hierarchy can be done in polynomial time. Empirically, HSD gives up to 1.2x speed-up over the best single-draft baseline, demonstrating the practicality of our algorithm in reducing generation latency beyond previous techniques.
CommonSense: Efficient Set Intersection (SetX) Protocol Based on Compressed Sensing
Venue: arXiv cs.DC 2025
Year: 2025

In the set reconciliation (\textsf{SetR}) problem, two parties Alice and Bob, holding sets $\mathsf{A}$ and $\mathsf{B}$, communicate to learn the symmetric difference $\mathsf{A} Δ\mathsf{B}$. In this work, we study a related but under-explored problem: set intersection (\textsf{SetX})~\cite{Ozisik2019}, where both parties learn $\mathsf{A} \cap \mathsf{B}$ instead. However, existing solutions typically reuse \textsf{SetR} protocols due to the absence of dedicated \textsf{SetX} protocols and the misconception that \textsf{SetR} and \textsf{SetX} have comparable costs. Observing that \textsf{SetX} is fundamentally cheaper than \textsf{SetR}, we developed a multi-round \textsf{SetX} protocol that outperforms the information-theoretic lower bound of \textsf{SetR} problem. In our \textsf{SetX} protocol, Alice sends Bob a compressed sensing (CS) sketch of $\mathsf{A}$ to help Bob identify his unique elements (those in $\mathsf{B \setminus A}$). This solves the \textsf{SetX} problem, if $\mathsf{A} \subseteq \mathsf{B}$. Otherwise, Bob sends a CS sketch of the residue (a set of elements he cannot decode) back to Alice for her to decode her unique elements (those in $\mathsf{A \setminus B}$). As such, Alice and Bob communicate back and forth %with a set membership filter (SMF) of estimated $\mathsf{B \setminus A}$. Alice updates $\mathsf{A}$ and communication repeats until both parties agrees on $\mathsf{A} \cap \mathsf{B}$. On real world datasets, experiments show that our $\mathsf{SetX}$ protocol reduces the communication cost by 8 to 10 times compared to the IBLT-based $\mathsf{SetR}$ protocol.
Statistical Inference for Linear Functionals of Online Least-squares SGD when $t \gtrsim d^{1+δ}$
Venue: arXiv cs.LG 2025
Year: 2025

Stochastic Gradient Descent (SGD) has become a cornerstone method in modern data science. However, deploying SGD in high-stakes applications necessitates rigorous quantification of its inherent uncertainty. In this work, we establish \emph{non-asymptotic Berry--Esseen bounds} for linear functionals of online least-squares SGD, thereby providing a Gaussian Central Limit Theorem (CLT) in a \emph{growing-dimensional regime}. Existing approaches to high-dimensional inference for projection parameters, such as~\cite{chang2023inference}, rely on inverting empirical covariance matrices and require at least $t \gtrsim d^{3/2}$ iterations to achieve finite-sample Berry--Esseen guarantees, rendering them computationally expensive and restrictive in the allowable dimensional scaling. In contrast, we show that a CLT holds for SGD iterates when the number of iterations grows as $t \gtrsim d^{1+δ}$ for any $δ> 0$, significantly extending the dimensional regime permitted by prior works while improving computational efficiency. The proposed online SGD-based procedure operates in $\mathcal{O}(td)$ time and requires only $\mathcal{O}(d)$ memory, in contrast to the $\mathcal{O}(td^2 + d^3)$ runtime of covariance-inversion methods. To render the theory practically applicable, we further develop an \emph{online variance estimator} for the asymptotic variance appearing in the CLT and establish \emph{high-probability deviation bounds} for this estimator. Collectively, these results yield the first fully online and data-driven framework for constructing confidence intervals for SGD iterates in the near-optimal scaling regime $t \gtrsim d^{1+δ}$.
Under Pressure: Security Analysis and Process Impacts of a Commercial Smart Air Compressor
Venue: arXiv cs.CR 2025
Year: 2025

The integration of Industrial Internet of Things (IIoT) devices into manufacturing environments has accelerated the transition to Industry 4.0, but has also introduced new cybersecurity risks. This paper conducts a comprehensive security analysis of a commercial smart air compressor, revealing critical vulnerabilities including hardcoded credentials, unauthenticated APIs, and an insecure update mechanism. It includes a formal threat model, demonstrates practical attack scenarios in a testbed environment, and evaluates their subsequent impact on an industrial process, leading to denial of service and the corruption of critical process telemetry. In addition, an analysis of the device's supply chain reveals how product integration from multiple vendors and limited security considerations can expose a device to threats. The findings underscore the necessity of incorporating cybersecurity principles into both IIoT device design and supply chain governance to enhance resilience against emerging industrial cyber threats.
GaLLoP: Gradient-based Sparse Learning on Low-Magnitude Parameters
Venue: arXiv cs.LG 2025
Year: 2025

Sparse fine-tuning techniques adapt LLMs to downstream tasks by only tuning a sparse subset of model parameters. However, the effectiveness of sparse adaptation depends on optimally selecting the model parameters to be fine-tuned. In this work, we introduce a novel sparse fine-tuning technique named GaLLoP: Gradient-based Sparse Learning on Low-Magnitude Parameters, which fine-tunes only those model parameters which have the largest gradient magnitudes on downstream tasks and the smallest pre-trained magnitudes, intuitively prioritizing parameters that are highly task-relevant, but minimally disruptive to pre-trained knowledge. Our experimentation with LLaMA3 8B and Gemma 2B as base models shows that GaLLoP consistently improves or matches the in-distribution as well as out-of-distribution performance obtained via the usage of other leading parameter-efficient fine-tuning techniques, including LoRA, DoRA, and SAFT. Our analysis demonstrates that GaLLoP mitigates catastrophic forgetting and memorization of task data, as important pre-trained parameters remain unchanged, and stabilizes performance relative to other fine-tuning techniques, robustly generalizing across most random seeds.