OpenRaiser/Intern-Atlas huggingface dataset:
Maximal Achievable Service Rates of Codes and Connections to Combinatorial Designs
Venue: arXiv cs.IT 2025
Year: 2025

We investigate the service-rate region (SRR) of distributed storage systems that employ linear codes. We focus on systems where each server stores one code symbol, and a user recovers a data symbol by accessing any of its recovery groups, subject to per-server capacity limits. The SRR--the convex polytope of simultaneously achievable request rates--captures system throughput and scalability. We first derive upper and lower bounds on the maximum request rate of each data object. These bounds hold for all linear codes and depend only on the number of parity checks orthogonal to a particular set of codeword coordinates associated with that object, i.e., the equations used in majority-logic decoding, and on code parameters. We then check the bound saturation for 1) all non-systematic codes whose SRRs are already known and 2) systematic codes. For the former, we prove the bounds are tight. For systematic codes, we show that the upper bound is achieved whenever the supports of minimum-weight dual codewords form a 2-design. As an application, we determine the exact per-object demand limits for binary Hamming codes. Our framework provides a new lens to address the SRR problem through combinatorial design theory.
Style-Preserving Policy Optimization for Game Agents
Venue: arXiv cs.AI 2025
Year: 2025

Proficient game agents with diverse play styles enrich the gaming experience and enhance the replay value of games. However, recent advancements in game AI based on reinforcement learning have predominantly focused on improving proficiency, whereas methods based on evolution algorithms generate agents with diverse play styles but exhibit subpar performance compared to RL methods. To address this gap, this paper proposes Mixed Proximal Policy Optimization (MPPO), a method designed to improve the proficiency of existing suboptimal agents while retaining their distinct styles. MPPO unifies loss objectives for both online and offline samples and introduces an implicit constraint to approximate demonstrator policies by adjusting the empirical distribution of samples. Empirical results across environments of varying scales demonstrate that MPPO achieves proficiency levels comparable to, or even superior to, pure online algorithms while preserving demonstrators' play styles. This work presents an effective approach for generating highly proficient and diverse game agents, ultimately contributing to more engaging gameplay experiences.
When does FTP become FPT?
Venue: arXiv cs.DS 2025
Year: 2025

In the problem Fault-Tolerant Path (FTP), we are given an edge-weighted directed graph G = (V, E), a subset U \subseteq E of vulnerable edges, two vertices s, t \in V, and integers k and \ell. The task is to decide whether there exists a subgraph H of G with total cost at most \ell such that, after the removal of any k vulnerable edges, H still contains an s-t-path. We study whether Fault-Tolerant Path is fixed-parameter tractable (FPT) and whether it admits a polynomial kernel under various parameterizations. Our choices of parameters include: the number of vulnerable edges in the input graph, the number of safe (i.e, invulnerable) edges in the input graph, the budget \ell, the minimum number of safe edges in any optimal solution, the minimum number of vulnerable edges in any optimal solution, the required redundancy k, and natural above- and below-guarantee parameterizations. We provide an almost complete description of the complexity landscape of FTP for these parameters.
Juicy or Dry? A Comparative Study of User Engagement and Information Retention in Interactive Infographics
Venue: arXiv cs.HC 2025
Year: 2025

This study compares the impact of "juiciness" on user engagement and short-term information retention in interactive infographics. Juicy designs generally showed a slight advantage in overall user engagement scores compared to dry designs. Specifically, the juicy version of the Burcalories infographic had the highest engagement score. However, the differences in engagement were often small. Regarding information retention, the results were mixed. The juicy versions of The Daily Routines of Famous Creative People and The Main Chakras infographics showed marginally better average recall and more participants with higher recall. Conversely, the dry version of Burcalories led to more correct answers in multiple-choice questions. The study suggests that while juicy design elements can enhance user engagement and, in some cases, short-term information retention, their effectiveness depends on careful implementation. Excessive juiciness could be overwhelming or distracting, while well-implemented juicy elements contributed to a more entertaining experience. The findings emphasize the importance of balancing engaging feedback with clarity and usability.
Critical Appraisal of Fairness Metrics in Clinical Predictive AI
Venue: arXiv cs.LG 2025
Year: 2025

Predictive artificial intelligence (AI) offers an opportunity to improve clinical practice and patient outcomes, but risks perpetuating biases if fairness is inadequately addressed. However, the definition of "fairness" remains unclear. We conducted a scoping review to identify and critically appraise fairness metrics for clinical predictive AI. We defined a "fairness metric" as a measure quantifying whether a model discriminates (societally) against individuals or groups defined by sensitive attributes. We searched five databases (2014-2024), screening 820 records, to include 41 studies, and extracted 62 fairness metrics. Metrics were classified by performance-dependency, model output level, and base performance metric, revealing a fragmented landscape with limited clinical validation and overreliance on threshold-dependent measures. Eighteen metrics were explicitly developed for healthcare, including only one clinical utility metric. Our findings highlight conceptual challenges in defining and quantifying fairness and identify gaps in uncertainty quantification, intersectionality, and real-world applicability. Future work should prioritise clinically meaningful metrics.
Relaxed syntax modeling in Transformers for future-proof license plate recognition
Venue: arXiv cs.CV 2025
Year: 2025

Effective license plate recognition systems are required to be resilient to constant change, as new license plates are released into traffic daily. While Transformer-based networks excel in their recognition at first sight, we observe significant performance drop over time which proves them unsuitable for tense production environments. Indeed, such systems obtain state-of-the-art results on plates whose syntax is seen during training. Yet, we show they perform similarly to random guessing on future plates where legible characters are wrongly recognized due to a shift in their syntax. After highlighting the flows of positional and contextual information in Transformer encoder-decoders, we identify several causes for their over-reliance on past syntax. Following, we devise architectural cut-offs and replacements which we integrate into SaLT, an attempt at a Syntax-Less Transformer for syntax-agnostic modeling of license plate representations. Experiments on both real and synthetic datasets show that our approach reaches top accuracy on past syntax and most importantly nearly maintains performance on future license plates. We further demonstrate the robustness of our architecture enhancements by way of various ablations.
Tower+: Bridging Generality and Translation Specialization in Multilingual LLMs
Venue: arXiv cs.CL 2025
Year: 2025

Fine-tuning pretrained LLMs has been shown to be an effective strategy for reaching state-of-the-art performance on specific tasks like machine translation. However, this process of adaptation often implies sacrificing general-purpose capabilities, such as conversational reasoning and instruction-following, hampering the utility of the system in real-world applications that require a mixture of skills. In this paper, we introduce Tower+, a suite of models designed to deliver strong performance across both translation and multilingual general-purpose text capabilities. We achieve a Pareto frontier between translation specialization and multilingual general-purpose capabilities by introducing a novel training recipe that builds on Tower (Alves et al., 2024), comprising continued pretraining, supervised fine-tuning, preference optimization, and reinforcement learning with verifiable rewards. At each stage of training, we carefully generate and curate data to strengthen performance on translation as well as general-purpose tasks involving code generation, mathematics problem solving, and general instruction-following. We develop models at multiple scales: 2B, 9B, and 72B. Our smaller models often outperform larger general-purpose open-weight and proprietary LLMs (e.g., Llama 3.3 70B, GPT-4o). Our largest model delivers best-in-class translation performance for high-resource languages and top results in multilingual Arena Hard evaluations and in IF-MT, a benchmark we introduce for evaluating both translation and instruction-following. Our findings highlight that it is possible to rival frontier models in general capabilities, while optimizing for specific business domains, such as translation and localization.
Chain-of-Thought Prompting Obscures Hallucination Cues in Large Language Models: An Empirical Evaluation
Venue: arXiv cs.CL 2025
Year: 2025

Large Language Models (LLMs) often exhibit \textit{hallucinations}, generating factually incorrect or semantically irrelevant content in response to prompts. Chain-of-Thought (CoT) prompting can mitigate hallucinations by encouraging step-by-step reasoning, but its impact on hallucination detection remains underexplored. To bridge this gap, we conduct a systematic empirical evaluation. We begin with a pilot experiment, revealing that CoT reasoning significantly affects the LLM's internal states and token probability distributions. Building on this, we evaluate the impact of various CoT prompting methods on mainstream hallucination detection methods across both instruction-tuned and reasoning-oriented LLMs. Specifically, we examine three key dimensions: changes in hallucination score distributions, variations in detection accuracy, and shifts in detection confidence. Our findings show that while CoT prompting helps reduce hallucination frequency, it also tends to obscure critical signals used for detection, impairing the effectiveness of various detection methods. Our study highlights an overlooked trade-off in the use of reasoning. Code is publicly available at: https://github.com/ECNU-Text-Computing/cot-hallu-detect .
A Note on Proper Relational Structures
Venue: arXiv cs.LO 2025
Year: 2025

In this note we provide an algorithm for translating relational structures into "proper" relational structures, i.e., those such that there is no pair of worlds w and u such that w is accessible from u for every agent. In particular, our method of translation preserves many classical properties of relational structures, such as transitivity and the Euclidean property. As a result, this method of translation has many applications in the literature on Simplicial Semantics for modal logic, where the creation of proper canonical relational structures is a common step in proofs of completeness.
Analyzing PDFs like Binaries: Adversarially Robust PDF Malware Analysis via Intermediate Representation and Language Model
Venue: arXiv cs.CR 2025
Year: 2025

Malicious PDF files have emerged as a persistent threat and become a popular attack vector in web-based attacks. While machine learning-based PDF malware classifiers have shown promise, these classifiers are often susceptible to adversarial attacks, undermining their reliability. To address this issue, recent studies have aimed to enhance the robustness of PDF classifiers. Despite these efforts, the feature engineering underlying these studies remains outdated. Consequently, even with the application of cutting-edge machine learning techniques, these approaches fail to fundamentally resolve the issue of feature instability. To tackle this, we propose a novel approach for PDF feature extraction and PDF malware detection. We introduce the PDFObj IR (PDF Object Intermediate Representation), an assembly-like language framework for PDF objects, from which we extract semantic features using a pretrained language model. Additionally, we construct an Object Reference Graph to capture structural features, drawing inspiration from program analysis. This dual approach enables us to analyze and detect PDF malware based on both semantic and structural features. Experimental results demonstrate that our proposed classifier achieves strong adversarial robustness while maintaining an exceptionally low false positive rate of only 0.07% on baseline dataset compared to state-of-the-art PDF malware classifiers.
CLEAR-3K: Assessing Causal Explanatory Capabilities in Language Models
Venue: arXiv cs.CL 2025
Year: 2025

We introduce CLEAR-3K, a dataset of 3,000 assertion-reasoning questions designed to evaluate whether language models can determine if one statement causally explains another. Each question present an assertion-reason pair and challenge language models to distinguish between semantic relatedness and genuine causal explanatory relationships. Through comprehensive evaluation of 21 state-of-the-art language models (ranging from 0.5B to 72B parameters), we identify two fundamental findings. First, language models frequently confuse semantic similarity with causality, relying on lexical and semantic overlap instead of inferring actual causal explanatory relationships. Second, as parameter size increases, models tend to shift from being overly skeptical about causal relationships to being excessively permissive in accepting them. Despite this shift, performance measured by the Matthews Correlation Coefficient plateaus at just 0.55, even for the best-performing models.Hence, CLEAR-3K provides a crucial benchmark for developing and evaluating genuine causal reasoning in language models, which is an essential capability for applications that require accurate assessment of causal relationships.
Variational Learning of Disentangled Representations
Venue: arXiv cs.LG 2025
Year: 2025

Disentangled representations enable models to separate factors of variation that are shared across experimental conditions from those that are condition-specific. This separation is essential in domains such as biomedical data analysis, where generalization to new treatments, patients, or species depends on isolating stable biological signals from context-dependent effects. While extensions of the variational autoencoder (VAE) framework have been proposed to address this problem, they frequently suffer from leakage between latent representations, limiting their ability to generalize to unseen conditions. Here, we introduce DISCoVeR, a new variational framework that explicitly separates condition-invariant and condition-specific factors. DISCoVeR integrates three key components: (i) a dual-latent architecture that models shared and specific factors separately; (ii) two parallel reconstructions that ensure both representations remain informative; and (iii) a novel max-min objective that encourages clean separation without relying on handcrafted priors, while making only minimal assumptions. Theoretically, we show that this objective maximizes data likelihood while promoting disentanglement, and that it admits a unique equilibrium. Empirically, we demonstrate that DISCoVeR achieves improved disentanglement on synthetic datasets, natural images, and single-cell RNA-seq data. Together, these results establish DISCoVeR as a principled approach for learning disentangled representations in multi-condition settings.
A Common Pool of Privacy Problems: Legal and Technical Lessons from a Large-Scale Web-Scraped Machine Learning Dataset
Venue: arXiv cs.CR 2025
Year: 2025

We investigate the contents of web-scraped data for training AI systems, at sizes where human dataset curators and compilers no longer manually annotate every sample. Building off of prior privacy concerns in machine learning models, we ask: What are the legal privacy implications of web-scraped machine learning datasets? In an empirical study of a popular training dataset, we find significant presence of personally identifiable information despite sanitization efforts. Our audit provides concrete evidence to support the concern that any large-scale web-scraped dataset may contain personal data. We use these findings of a real-world dataset to inform our legal analysis with respect to existing privacy and data protection laws. We surface various privacy risks of current data curation practices that may propagate personal information to downstream models. From our findings, we argue for reorientation of current frameworks of "publicly available" information to meaningfully limit the development of AI built upon indiscriminate scraping of the internet.
Lower Bounds against the Ideal Proof System in Finite Fields
Venue: arXiv cs.CC 2025
Year: 2025

Lower bounds against strong algebraic proof systems and specifically fragments of the Ideal Proof System (IPS), have been obtained in an ongoing line of work. All of these bounds, however, are proved only over large (or characteristic $0$) fields, yet finite fields are the more natural setting for propositional proof complexity, especially for progress toward lower bounds for Frege systems such as $AC^0[p]$-Frege. This work establishes lower bounds against fragments of IPS over fixed finite fields. Specifically, we show that a variant of the knapsack instance studied by Govindasamy, Hakoniemi, and Tzameret (FOCS'22) has no polynomial-size IPS refutation over finite fields when the refutation is multilinear and written as a constant-depth circuit. The key ingredient of our argument is the recent set-multilinearization result of Forbes (CCC'24), which extends the earlier result of Limaye, Srinivasan, and Tavenas (FOCS'21) to all fields, and an extension of the techniques of Govindasamy, Hakoniemi, and Tzameret to finite fields. We also separate this proof system from the one studied by Govindasamy, Hakoniemi, and Tzameret. In addition, we present new lower bounds for read-once algebraic branching program refutations, roABP-IPS, in finite fields, extending results of Forbes, Shpilka, Tzameret, and Wigderson (Theor. of Comput.'21) and Hakoniemi, Limaye, and Tzameret (STOC'24). Finally, we show that any lower bound against any proof system at least as strong as (non-multilinear) constant-depth IPS over finite fields for any instance, even a purely algebraic instance (i.e., not a translation of a Boolean formula or CNF), implies a hard CNF formula for the respective IPS fragment, and hence an $AC^0[p]$-Frege lower bound by known simulations over finite fields (Grochow and Pitassi (J. ACM'18)).
OAT-Rephrase: Optimization-Aware Training Data Rephrasing for Zeroth-Order LLM Fine-Tuning
Venue: arXiv cs.LG 2025
Year: 2025

Fine-tuning large language models (LLMs) using zeroth-order optimization (ZO) offers a memory-efficient alternative to gradient-based methods but suffers from slower convergence and unstable optimization due to noisy gradient estimates. This paper introduces OAT-Rephrase, an Optimization-Aware Training data rephrasing strategy that leverages an LLM to rephrase training instances based on its understanding of the ZO dynamics, specifically MeZO, derived directly from its paper. The approach incorporates a dual-stage pipeline featuring a rewriter LLM and a semantic judge, ensuring all rephrasings retain task relevance and logical consistency. Evaluations across five classification tasks and three LLM architectures demonstrate that OAT-Rephrase consistently improves MeZO fine-tuning performance, often narrowing or eliminating the gap with first-order methods. Our findings suggest that optimization-aware rephrasing serves as a reusable and low-overhead enhancement for zeroth-order tuning regimes.
STM32-Based IoT Framework for Real-Time Environmental Monitoring and Wireless Node Synchronization
Venue: arXiv cs.OH 2025
Year: 2025

The fast pace of technological growth has created a heightened need for intelligent, autonomous monitoring systems in a variety of fields, especially in environmental applications. This project shows the design process and implementation of a proper dual node (master-slave) IoT-based monitoring system using STM32F103C8T6 microcontrollers. The structure of the wireless monitoring system studies the environmental conditions in real-time and can measure parameters like temperature, humidity, soil moisture, raindrop detection and obstacle distance. The relay of information occurs between the primary master node (designated as the Green House) to the slave node (the Red House) employing the HC-05 Bluetooth module for information transmission. Each node displays the sensor data on OLED screens and a visual or auditory alert is triggered based on predetermined thresholds. A comparative analysis of STM32 (ARM Cortex-M3) and Arduino (AVR) is presented to justify the STM32 used in this work for greater processing power, less energy use, and better peripherals. Practical challenges in this project arise from power distribution and Bluetooth configuration limits. Future work will explore the transition of a Wi-Fi communication protocol and develop a mobile monitoring robot to enhance scalability of the system. Finally, this research shows that ARM based embedded systems can provide real-time environmental monitoring systems that are reliable and consume low power.
PRAISE: Enhancing Product Descriptions with LLM-Driven Structured Insights
Venue: arXiv cs.CL 2025
Year: 2025

Accurate and complete product descriptions are crucial for e-commerce, yet seller-provided information often falls short. Customer reviews offer valuable details but are laborious to sift through manually. We present PRAISE: Product Review Attribute Insight Structuring Engine, a novel system that uses Large Language Models (LLMs) to automatically extract, compare, and structure insights from customer reviews and seller descriptions. PRAISE provides users with an intuitive interface to identify missing, contradictory, or partially matching details between these two sources, presenting the discrepancies in a clear, structured format alongside supporting evidence from reviews. This allows sellers to easily enhance their product listings for clarity and persuasiveness, and buyers to better assess product reliability. Our demonstration showcases PRAISE's workflow, its effectiveness in generating actionable structured insights from unstructured reviews, and its potential to significantly improve the quality and trustworthiness of e-commerce product catalogs.
Using Machine Learning in Analyzing Air Quality Discrepancies of Environmental Impact
Venue: arXiv cs.CY 2025
Year: 2025

In this study, we apply machine learning and software engineering in analyzing air pollution levels in City of Baltimore. The data model was fed with three primary data sources: 1) a biased method of estimating insurance risk used by homeowners loan corporation, 2) demographics of Baltimore residents, and 3) census data estimate of NO2 and PM2.5 concentrations. The dataset covers 650,643 Baltimore residents in 44.7 million residents in 202 major cities in US. The results show that air pollution levels have a clear association with the biased insurance estimating method. Great disparities present in NO2 level between more desirable and low income blocks. Similar disparities exist in air pollution level between residents' ethnicity. As Baltimore population consists of a greater proportion of people of color, the finding reveals how decades old policies has continued to discriminate and affect quality of life of Baltimore citizens today.